Dynamic Associations Between Emotion Regulation, Mental Fatigue, and Stress: An Ecological Momentary Assessment Study

Authors

Affiliation

Rebecca Kirkham

Monash University

Joshua F. Wiley

Monash University

1 Loading Packages

# | label: Loading Packages

library(data.table)
library(readxl)
library(dplyr)

Attaching package: 'dplyr'

The following objects are masked from 'package:data.table':

    between, first, last

The following objects are masked from 'package:stats':

    filter, lag

The following objects are masked from 'package:base':

    intersect, setdiff, setequal, union

library(tidyr)
library(lubridate)

Attaching package: 'lubridate'

The following objects are masked from 'package:data.table':

    hour, isoweek, mday, minute, month, quarter, second, wday, week,
    yday, year

The following objects are masked from 'package:base':

    date, intersect, setdiff, union

library(ggplot2)
library(diagram)

Loading required package: shape

library(mediation)

Warning: package 'mediation' was built under R version 4.4.3

Loading required package: MASS

Warning: package 'MASS' was built under R version 4.4.3

Attaching package: 'MASS'

The following object is masked from 'package:dplyr':

    select

Loading required package: Matrix

Attaching package: 'Matrix'

The following objects are masked from 'package:tidyr':

    expand, pack, unpack

Loading required package: mvtnorm

Loading required package: sandwich

mediation: Causal Mediation Analysis
Version: 4.5.0

library(emmeans)

Welcome to emmeans.
Caution: You lose important information if you filter this package's results.
See '? untidy'

library(lme4)
library(brms)

Loading required package: Rcpp

Loading 'brms' package (version 2.21.0). Useful instructions
can be found by typing help('brms'). A more detailed introduction
to the package is available through vignette('brms_overview').

Attaching package: 'brms'

The following object is masked from 'package:lme4':

    ngrps

The following object is masked from 'package:stats':

    ar

library(cmdstanr)

This is cmdstanr version 0.8.1

- CmdStanR documentation and vignettes: mc-stan.org/cmdstanr

- CmdStan path: C:/Users/becca/.cmdstan/cmdstan-2.35.0

- CmdStan version: 2.35.0

A newer version of CmdStan is available. See ?install_cmdstan() to install it.
To disable this check set option or environment variable cmdstanr_no_ver_check=TRUE.

library(openxlsx)
library(writexl)
library(shinystan)

Loading required package: shiny

This is shinystan version 2.6.0

library(bayestestR)

Warning: package 'bayestestR' was built under R version 4.4.3

library(readr)

2 Summarize and Prepare the Data

d <- read_excel("~/Clin PhD/Research Project/Study 2. Quantitative Study MF/Data/EMA/EMA_data_09.05.25_part.xlsx", na = c("", "na"))
d  <- as.data.table(d)
d <- type_convert(d)

── Column specification ────────────────────────────────────────────────────────
cols(
  IPAddress = col_character(),
  ResponseId = col_character(),
  Context_Place_TEXT = col_character(),
  Context_Activity_TEXT = col_character(),
  Str_Hour_TEXT = col_character(),
  Date = col_character(),
  Time = col_time(format = ""),
  ER_count_cap5 = col_character(),
  Prev_ER_count_cap5 = col_character()
)

##Datacheck - Valid Pairs
# Sort the data by PID, Day_Number, and Session_Number to ensure correct order
d <- d[order(d$PID, d$Day_Number, d$Session_Number), ]

# Ensure that Day_Number and Session_Number are integers
d$Day_Number <- as.integer(d$Day_Number)
d$Session_Number <- as.integer(d$Session_Number)

# Create new columns to indicate the previous session number, day, and PID
d$prev_session_number <- c(NA, head(d$Session_Number, -1))
d$prev_day_number <- c(NA, head(d$Day_Number, -1))
d$prev_PID <- c(NA, head(d$PID, -1))  # To ensure valid pairs are from the same PID

# Identify valid pairs
d$valid_pair <- ifelse(
  !is.na(d$StartDate) & 
    !is.na(c(NA, head(d$StartDate, -1))) & # Check if the previous row's StartDate is also not missing
    d$PID == d$prev_PID &                 # Ensure the previous row has the same PID
    d$Day_Number == d$prev_day_number & 
    d$Session_Number == d$prev_session_number + 1, 
  1, 
  0
)

# Aggregate the number of valid pairs per PID
pairs_per_person <- aggregate(valid_pair ~ PID, data = d, sum)

# Subset the original data to include only PIDs with at least one valid pair
valid_pids <- pairs_per_person$PID[pairs_per_person$valid_pair > 0]
d <- d[d$PID %in% valid_pids, ]

# Print the number of PIDs after filtering and a preview of the data
cat("Number of PIDs with at least one valid pair:", length(unique(d$PID)), "\n")

Number of PIDs with at least one valid pair: 179 

head(d)

     PID Day_Number Session_Number           StartDate             EndDate
   <num>      <int>          <int>              <POSc>              <POSc>
1:  1003          1              1 2024-03-27 08:25:26 2024-03-27 08:27:27
2:  1003          1              2 2024-03-27 11:41:07 2024-03-27 11:42:35
3:  1003          1              3 2024-03-27 15:03:37 2024-03-27 15:06:12
4:  1003          1              4 2024-03-27 17:49:57 2024-03-27 17:51:03
5:  1003          2              1 2024-03-28 09:08:55 2024-03-28 09:10:05
6:  1003          2              2 2024-03-28 11:58:24 2024-03-28 12:05:17
   Status      IPAddress Progress Duration_Sec Finished        RecordedDate
    <num>         <char>    <num>        <num>    <num>              <POSc>
1:      0 14.201.119.251      100          121        1 2024-03-27 08:27:28
2:      0  129.78.56.147      100           87        1 2024-03-27 11:42:36
3:      0  49.179.40.214      100          155        1 2024-03-27 15:06:13
4:      0  129.78.56.147      100           65        1 2024-03-27 17:51:03
5:      0  203.194.63.71      100           70        1 2024-03-28 09:10:05
6:      0  203.194.63.71      100          413        1 2024-03-28 12:05:17
          ResponseId Context_Place Context_Place_TEXT Context_Activity
              <char>         <num>             <char>            <num>
1: R_4TFd7tOWYJan2fw             1               <NA>          1281012
2: R_44lmBPGzglCPzKF             5               <NA>              489
3: R_4DZHzDUbJRS0Jyd             8               <NA>                8
4: R_4M37dVGs4OWIGeU             5               <NA>               48
5: R_4lyTbr45FIeY9g8             1               <NA>                1
6: R_4FFcMEibUt2wMfv             1               <NA>               48
   Context_Activity_TEXT Morn_First Slp_Hours Slp_Quality Aff_Int Aff_Act
                  <char>      <num>     <num>       <num>   <num>   <num>
1:                  <NA>          1         6           8       3       2
2:                  <NA>          2        NA          NA       3       5
3:                  <NA>          2        NA          NA       3       5
4:                  <NA>          2        NA          NA       3       2
5:                  <NA>          1         7           9       3       2
6:                  <NA>          2        NA          NA       4       2
   Aff_Str Aff_Calm Aff_Plac Aff_Cont Aff_Anx Aff_Irr Aff_Gui Aff_Tire Aff_Bor
     <num>    <num>    <num>    <num>   <num>   <num>   <num>    <num>   <num>
1:       2        5        5        5       1       1       1        2       1
2:       3        5        5        5       2       1       1        1       1
3:       3        5        5        5       1       1       1        2       1
4:       3        5        5        5       1       1       1        1       1
5:       2        5        5        5       1       1       1        3       1
6:       2        5        5        5       1       1       1        1       1
   Aff_Sad Str_Overall Str_Hour Str_Hour_TEXT  ER10 CF_1_MTW CF_2_MF CF_3_PF
     <num>       <num>    <num>        <char> <num>    <num>   <num>   <num>
1:       1           0        9          <NA>     0        5       1       1
2:       1           0        9          <NA>     0        5       1       1
3:       1           0        9          <NA>     0        5       1       1
4:       1           0        9          <NA>     0        5       1       1
5:       1           0        9          <NA>     0        5       1       1
6:       1           0        9          <NA>     0        5       2       1
   CF_4_TC CF_5_MPW CF_6_C CF_7_AM CF_8_O Affect_PosHigh Affect_PosLow
     <num>    <num>  <num>   <num>  <num>          <num>         <num>
1:       4        4      5       1      1              7            15
2:       5        5      5       1      2             11            15
3:       5        5      5       1      1             11            15
4:       5        5      5       1      1              8            15
5:       3        3      5       1      1              7            15
6:       5        5      5       2      1              8            15
   Affect_NegHigh Affect_NegLow CF_HighPos CF_LowNeg ER_1_Dis ER_2_Rum ER_3_SBl
            <num>         <num>      <num>     <num>    <num>    <num>    <num>
1:              3             4         18        20        0        0        0
2:              4             3         20        19        0        0        0
3:              3             4         20        20        0        0        0
4:              3             3         20        20        0        0        0
5:              3             5         16        20        0        0        0
6:              3             3         20        18        0        0        0
   ER_4_ExprS ER_5_ExperS ER_6_Acc ER_7_Pla ER_8_Rea ER_9_ESu ER_10_Rel
        <num>       <num>    <num>    <num>    <num>    <num>     <num>
1:          0           0        0        0        0        0         0
2:          0           0        0        0        0        0         0
3:          0           0        0        0        0        0         0
4:          0           0        0        0        0        0         0
5:          0           0        0        0        0        0         0
6:          0           0        0        0        0        0         0
   ER_0_Non    MF T1_DOB T1_Education T1_COB_Australia T1_Ethnicity_1
      <num> <num>  <num>        <num>            <num>          <num>
1:        1     1   2005            2                1             NA
2:        1     1   2005            2                1             NA
3:        1     1   2005            2                1             NA
4:        1     1   2005            2                1             NA
5:        1     1   2005            2                1             NA
6:        1     2   2005            2                1             NA
   T1_Ethnicity_2 T1_Ethnicity_3 T1_Ethnicity_4 T1_Ethnicity_5 T1_Ethnicity_6
           <lgcl>         <lgcl>         <lgcl>          <num>         <lgcl>
1:             NA             NA             NA              1             NA
2:             NA             NA             NA              1             NA
3:             NA             NA             NA              1             NA
4:             NA             NA             NA              1             NA
5:             NA             NA             NA              1             NA
6:             NA             NA             NA              1             NA
   T1_Ethnicity_7 T1_Ethnicity_8 T1_Ethnicity_9 T1_Ethnicity_10
           <lgcl>         <lgcl>         <lgcl>          <lgcl>
1:             NA             NA             NA              NA
2:             NA             NA             NA              NA
3:             NA             NA             NA              NA
4:             NA             NA             NA              NA
5:             NA             NA             NA              NA
6:             NA             NA             NA              NA
   T1_Ethnicity_10_TEXT T1_Gender T1_Sex T1_Income T1_Postcode T1_Handedness
                 <lgcl>     <num>  <num>     <num>       <num>         <num>
1:                   NA         1      1         7        2093             1
2:                   NA         1      1         7        2093             1
3:                   NA         1      1         7        2093             1
4:                   NA         1      1         7        2093             1
5:                   NA         1      1         7        2093             1
6:                   NA         1      1         7        2093             1
   Age_2024 ER_count       Date     Time Prev_ER_1_Dis Prev_ER_2_Rum
      <num>    <num>     <char>    <hms>         <num>         <num>
1:       19        0 27/03/2024 08:27:27            NA            NA
2:       19        0 27/03/2024 11:42:35             0             0
3:       19        0 27/03/2024 15:06:12             0             0
4:       19        0 27/03/2024 17:51:03             0             0
5:       19        0 28/03/2024 09:10:05            NA            NA
6:       19        0 28/03/2024 12:05:17             0             0
   Prev_ER_3_SBl Prev_ER_4_ExprS Prev_ER_5_ExperS Prev_ER_6_Acc Prev_ER_7_Pla
           <num>           <num>            <num>         <num>         <num>
1:            NA              NA               NA            NA            NA
2:             0               0                0             0             0
3:             0               0                0             0             0
4:             0               0                0             0             0
5:            NA              NA               NA            NA            NA
6:             0               0                0             0             0
   Prev_ER_8_Rea Prev_ER_9_ESu Prev_ER_10_Rel Prev_ER_0_Non Prev_ER_count
           <num>         <num>          <num>         <num>         <num>
1:            NA            NA             NA            NA            NA
2:             0             0              0             1             0
3:             0             0              0             1             0
4:             0             0              0             1             0
5:            NA            NA             NA            NA            NA
6:             0             0              0             1             0
   Prev_MF Prev_Str_Overall  MF_mean ER_count_cap5 Prev_ER_count_cap5
     <num>            <num>    <num>        <char>             <char>
1:      NA               NA 1.366337             0               <NA>
2:       1                0 1.366337             0                  0
3:       1                0 1.366337             0                  0
4:       1                0 1.366337             0                  0
5:      NA               NA 1.366337             0               <NA>
6:       1                0 1.366337             0                  0
   Prev_MF_Within Prev_CF_4_TC Str_Overall_mean Prev_Str_Overall_Within
            <num>        <num>            <num>                   <num>
1:             NA           NA       0.04950495                      NA
2:     -0.3663366            4       0.04950495             -0.04950495
3:     -0.3663366            5       0.04950495             -0.04950495
4:     -0.3663366            5       0.04950495             -0.04950495
5:             NA           NA       0.04950495                      NA
6:     -0.3663366            3       0.04950495             -0.04950495
   CF_4_TC_mean Prev_CF_4_TC_Within ER_1_Dis_mean Prev_ER_1_Dis_Within
          <num>               <num>         <num>                <num>
1:     4.722772                  NA             0                   NA
2:     4.722772          -0.7227723             0                    0
3:     4.722772           0.2772277             0                    0
4:     4.722772           0.2772277             0                    0
5:     4.722772                  NA             0                   NA
6:     4.722772          -1.7227723             0                    0
   ER_2_Rum_mean Prev_ER_2_Rum_Within ER_3_SBl_mean Prev_ER_3_SBl_Within
           <num>                <num>         <num>                <num>
1:             0                   NA             0                   NA
2:             0                    0             0                    0
3:             0                    0             0                    0
4:             0                    0             0                    0
5:             0                   NA             0                   NA
6:             0                    0             0                    0
   ER_4_ExprS_mean Prev_ER_4_ExprS_Within ER_5_ExperS_mean
             <num>                  <num>            <num>
1:               0                     NA                0
2:               0                      0                0
3:               0                      0                0
4:               0                      0                0
5:               0                     NA                0
6:               0                      0                0
   Prev_ER_5_ExperS_Within ER_6_Acc_mean Prev_ER_6_Acc_Within ER_7_Pla_mean
                     <num>         <num>                <num>         <num>
1:                      NA    0.03960396                   NA    0.01980198
2:                       0    0.03960396          -0.03960396    0.01980198
3:                       0    0.03960396          -0.03960396    0.01980198
4:                       0    0.03960396          -0.03960396    0.01980198
5:                      NA    0.03960396                   NA    0.01980198
6:                       0    0.03960396          -0.03960396    0.01980198
   Prev_ER_7_Pla_Within ER_8_Rea_mean Prev_ER_8_Rea_Within ER_9_ESu_mean
                  <num>         <num>                <num>         <num>
1:                   NA    0.00990099                   NA             0
2:          -0.01980198    0.00990099          -0.00990099             0
3:          -0.01980198    0.00990099          -0.00990099             0
4:          -0.01980198    0.00990099          -0.00990099             0
5:                   NA    0.00990099                   NA             0
6:          -0.01980198    0.00990099          -0.00990099             0
   Prev_ER_9_ESu_Within ER_10_Rel_mean Prev_ER_10_Rel_Within
                  <num>          <num>                 <num>
1:                   NA              0                    NA
2:                    0              0                     0
3:                    0              0                     0
4:                    0              0                     0
5:                   NA              0                    NA
6:                    0              0                     0
   prev_session_number prev_day_number valid_pair prev_PID
                 <int>           <int>      <num>    <num>
1:                  NA              NA          0       NA
2:                   1               1          1     1003
3:                   2               1          1     1003
4:                   3               1          1     1003
5:                   4               1          0     1003
6:                   1               2          1     1003

# Count valid pairs per PID
pair_counts <- d %>%
  group_by(PID) %>%
  summarise(n_valid_pairs = sum(valid_pair == 1, na.rm = TRUE)) %>%
  filter(n_valid_pairs >= 1)

#### Subset with >10 pairs ####
# Identify PIDs with ≥ 10 valid pairs
valid10_pids <- pairs_per_person %>%
  filter(valid_pair >= 10) %>%
  pull(PID)

# Subset the main data
d_10validpairs <- d %>%
  filter(PID %in% valid10_pids)

# check
cat(
  "Number of PIDs with at least 10 valid pairs:",
  n_distinct(d_10validpairs$PID),
  "\n"
)

Number of PIDs with at least 10 valid pairs: 140 

head(d_10validpairs)

     PID Day_Number Session_Number           StartDate             EndDate
   <num>      <int>          <int>              <POSc>              <POSc>
1:  1003          1              1 2024-03-27 08:25:26 2024-03-27 08:27:27
2:  1003          1              2 2024-03-27 11:41:07 2024-03-27 11:42:35
3:  1003          1              3 2024-03-27 15:03:37 2024-03-27 15:06:12
4:  1003          1              4 2024-03-27 17:49:57 2024-03-27 17:51:03
5:  1003          2              1 2024-03-28 09:08:55 2024-03-28 09:10:05
6:  1003          2              2 2024-03-28 11:58:24 2024-03-28 12:05:17
   Status      IPAddress Progress Duration_Sec Finished        RecordedDate
    <num>         <char>    <num>        <num>    <num>              <POSc>
1:      0 14.201.119.251      100          121        1 2024-03-27 08:27:28
2:      0  129.78.56.147      100           87        1 2024-03-27 11:42:36
3:      0  49.179.40.214      100          155        1 2024-03-27 15:06:13
4:      0  129.78.56.147      100           65        1 2024-03-27 17:51:03
5:      0  203.194.63.71      100           70        1 2024-03-28 09:10:05
6:      0  203.194.63.71      100          413        1 2024-03-28 12:05:17
          ResponseId Context_Place Context_Place_TEXT Context_Activity
              <char>         <num>             <char>            <num>
1: R_4TFd7tOWYJan2fw             1               <NA>          1281012
2: R_44lmBPGzglCPzKF             5               <NA>              489
3: R_4DZHzDUbJRS0Jyd             8               <NA>                8
4: R_4M37dVGs4OWIGeU             5               <NA>               48
5: R_4lyTbr45FIeY9g8             1               <NA>                1
6: R_4FFcMEibUt2wMfv             1               <NA>               48
   Context_Activity_TEXT Morn_First Slp_Hours Slp_Quality Aff_Int Aff_Act
                  <char>      <num>     <num>       <num>   <num>   <num>
1:                  <NA>          1         6           8       3       2
2:                  <NA>          2        NA          NA       3       5
3:                  <NA>          2        NA          NA       3       5
4:                  <NA>          2        NA          NA       3       2
5:                  <NA>          1         7           9       3       2
6:                  <NA>          2        NA          NA       4       2
   Aff_Str Aff_Calm Aff_Plac Aff_Cont Aff_Anx Aff_Irr Aff_Gui Aff_Tire Aff_Bor
     <num>    <num>    <num>    <num>   <num>   <num>   <num>    <num>   <num>
1:       2        5        5        5       1       1       1        2       1
2:       3        5        5        5       2       1       1        1       1
3:       3        5        5        5       1       1       1        2       1
4:       3        5        5        5       1       1       1        1       1
5:       2        5        5        5       1       1       1        3       1
6:       2        5        5        5       1       1       1        1       1
   Aff_Sad Str_Overall Str_Hour Str_Hour_TEXT  ER10 CF_1_MTW CF_2_MF CF_3_PF
     <num>       <num>    <num>        <char> <num>    <num>   <num>   <num>
1:       1           0        9          <NA>     0        5       1       1
2:       1           0        9          <NA>     0        5       1       1
3:       1           0        9          <NA>     0        5       1       1
4:       1           0        9          <NA>     0        5       1       1
5:       1           0        9          <NA>     0        5       1       1
6:       1           0        9          <NA>     0        5       2       1
   CF_4_TC CF_5_MPW CF_6_C CF_7_AM CF_8_O Affect_PosHigh Affect_PosLow
     <num>    <num>  <num>   <num>  <num>          <num>         <num>
1:       4        4      5       1      1              7            15
2:       5        5      5       1      2             11            15
3:       5        5      5       1      1             11            15
4:       5        5      5       1      1              8            15
5:       3        3      5       1      1              7            15
6:       5        5      5       2      1              8            15
   Affect_NegHigh Affect_NegLow CF_HighPos CF_LowNeg ER_1_Dis ER_2_Rum ER_3_SBl
            <num>         <num>      <num>     <num>    <num>    <num>    <num>
1:              3             4         18        20        0        0        0
2:              4             3         20        19        0        0        0
3:              3             4         20        20        0        0        0
4:              3             3         20        20        0        0        0
5:              3             5         16        20        0        0        0
6:              3             3         20        18        0        0        0
   ER_4_ExprS ER_5_ExperS ER_6_Acc ER_7_Pla ER_8_Rea ER_9_ESu ER_10_Rel
        <num>       <num>    <num>    <num>    <num>    <num>     <num>
1:          0           0        0        0        0        0         0
2:          0           0        0        0        0        0         0
3:          0           0        0        0        0        0         0
4:          0           0        0        0        0        0         0
5:          0           0        0        0        0        0         0
6:          0           0        0        0        0        0         0
   ER_0_Non    MF T1_DOB T1_Education T1_COB_Australia T1_Ethnicity_1
      <num> <num>  <num>        <num>            <num>          <num>
1:        1     1   2005            2                1             NA
2:        1     1   2005            2                1             NA
3:        1     1   2005            2                1             NA
4:        1     1   2005            2                1             NA
5:        1     1   2005            2                1             NA
6:        1     2   2005            2                1             NA
   T1_Ethnicity_2 T1_Ethnicity_3 T1_Ethnicity_4 T1_Ethnicity_5 T1_Ethnicity_6
           <lgcl>         <lgcl>         <lgcl>          <num>         <lgcl>
1:             NA             NA             NA              1             NA
2:             NA             NA             NA              1             NA
3:             NA             NA             NA              1             NA
4:             NA             NA             NA              1             NA
5:             NA             NA             NA              1             NA
6:             NA             NA             NA              1             NA
   T1_Ethnicity_7 T1_Ethnicity_8 T1_Ethnicity_9 T1_Ethnicity_10
           <lgcl>         <lgcl>         <lgcl>          <lgcl>
1:             NA             NA             NA              NA
2:             NA             NA             NA              NA
3:             NA             NA             NA              NA
4:             NA             NA             NA              NA
5:             NA             NA             NA              NA
6:             NA             NA             NA              NA
   T1_Ethnicity_10_TEXT T1_Gender T1_Sex T1_Income T1_Postcode T1_Handedness
                 <lgcl>     <num>  <num>     <num>       <num>         <num>
1:                   NA         1      1         7        2093             1
2:                   NA         1      1         7        2093             1
3:                   NA         1      1         7        2093             1
4:                   NA         1      1         7        2093             1
5:                   NA         1      1         7        2093             1
6:                   NA         1      1         7        2093             1
   Age_2024 ER_count       Date     Time Prev_ER_1_Dis Prev_ER_2_Rum
      <num>    <num>     <char>    <hms>         <num>         <num>
1:       19        0 27/03/2024 08:27:27            NA            NA
2:       19        0 27/03/2024 11:42:35             0             0
3:       19        0 27/03/2024 15:06:12             0             0
4:       19        0 27/03/2024 17:51:03             0             0
5:       19        0 28/03/2024 09:10:05            NA            NA
6:       19        0 28/03/2024 12:05:17             0             0
   Prev_ER_3_SBl Prev_ER_4_ExprS Prev_ER_5_ExperS Prev_ER_6_Acc Prev_ER_7_Pla
           <num>           <num>            <num>         <num>         <num>
1:            NA              NA               NA            NA            NA
2:             0               0                0             0             0
3:             0               0                0             0             0
4:             0               0                0             0             0
5:            NA              NA               NA            NA            NA
6:             0               0                0             0             0
   Prev_ER_8_Rea Prev_ER_9_ESu Prev_ER_10_Rel Prev_ER_0_Non Prev_ER_count
           <num>         <num>          <num>         <num>         <num>
1:            NA            NA             NA            NA            NA
2:             0             0              0             1             0
3:             0             0              0             1             0
4:             0             0              0             1             0
5:            NA            NA             NA            NA            NA
6:             0             0              0             1             0
   Prev_MF Prev_Str_Overall  MF_mean ER_count_cap5 Prev_ER_count_cap5
     <num>            <num>    <num>        <char>             <char>
1:      NA               NA 1.366337             0               <NA>
2:       1                0 1.366337             0                  0
3:       1                0 1.366337             0                  0
4:       1                0 1.366337             0                  0
5:      NA               NA 1.366337             0               <NA>
6:       1                0 1.366337             0                  0
   Prev_MF_Within Prev_CF_4_TC Str_Overall_mean Prev_Str_Overall_Within
            <num>        <num>            <num>                   <num>
1:             NA           NA       0.04950495                      NA
2:     -0.3663366            4       0.04950495             -0.04950495
3:     -0.3663366            5       0.04950495             -0.04950495
4:     -0.3663366            5       0.04950495             -0.04950495
5:             NA           NA       0.04950495                      NA
6:     -0.3663366            3       0.04950495             -0.04950495
   CF_4_TC_mean Prev_CF_4_TC_Within ER_1_Dis_mean Prev_ER_1_Dis_Within
          <num>               <num>         <num>                <num>
1:     4.722772                  NA             0                   NA
2:     4.722772          -0.7227723             0                    0
3:     4.722772           0.2772277             0                    0
4:     4.722772           0.2772277             0                    0
5:     4.722772                  NA             0                   NA
6:     4.722772          -1.7227723             0                    0
   ER_2_Rum_mean Prev_ER_2_Rum_Within ER_3_SBl_mean Prev_ER_3_SBl_Within
           <num>                <num>         <num>                <num>
1:             0                   NA             0                   NA
2:             0                    0             0                    0
3:             0                    0             0                    0
4:             0                    0             0                    0
5:             0                   NA             0                   NA
6:             0                    0             0                    0
   ER_4_ExprS_mean Prev_ER_4_ExprS_Within ER_5_ExperS_mean
             <num>                  <num>            <num>
1:               0                     NA                0
2:               0                      0                0
3:               0                      0                0
4:               0                      0                0
5:               0                     NA                0
6:               0                      0                0
   Prev_ER_5_ExperS_Within ER_6_Acc_mean Prev_ER_6_Acc_Within ER_7_Pla_mean
                     <num>         <num>                <num>         <num>
1:                      NA    0.03960396                   NA    0.01980198
2:                       0    0.03960396          -0.03960396    0.01980198
3:                       0    0.03960396          -0.03960396    0.01980198
4:                       0    0.03960396          -0.03960396    0.01980198
5:                      NA    0.03960396                   NA    0.01980198
6:                       0    0.03960396          -0.03960396    0.01980198
   Prev_ER_7_Pla_Within ER_8_Rea_mean Prev_ER_8_Rea_Within ER_9_ESu_mean
                  <num>         <num>                <num>         <num>
1:                   NA    0.00990099                   NA             0
2:          -0.01980198    0.00990099          -0.00990099             0
3:          -0.01980198    0.00990099          -0.00990099             0
4:          -0.01980198    0.00990099          -0.00990099             0
5:                   NA    0.00990099                   NA             0
6:          -0.01980198    0.00990099          -0.00990099             0
   Prev_ER_9_ESu_Within ER_10_Rel_mean Prev_ER_10_Rel_Within
                  <num>          <num>                 <num>
1:                   NA              0                    NA
2:                    0              0                     0
3:                    0              0                     0
4:                    0              0                     0
5:                   NA              0                    NA
6:                    0              0                     0
   prev_session_number prev_day_number valid_pair prev_PID
                 <int>           <int>      <num>    <num>
1:                  NA              NA          0       NA
2:                   1               1          1     1003
3:                   2               1          1     1003
4:                   3               1          1     1003
5:                   4               1          0     1003
6:                   1               2          1     1003

#### Demographics ####
# To find the number of unique PIDs:
unique_pids <- length(unique(d$PID))

# To find the total number of surveys with non-NA 'StartDate':
total_surveys_non_na <- sum(!is.na(d$ER_1_Dis))

# Output the results
cat("Number of unique PIDs:", unique_pids, "\n")

Number of unique PIDs: 179 

cat("Total number of surveys with non-NA StartDate:", total_surveys_non_na, "\n")

Total number of surveys with non-NA StartDate: 10984 

Session1count_non_na <- sum(d$Session_Number == 1 & !is.na(d$StartDate))
cat("Total number of Session 1 surveys with non-NA StartDate:", Session1count_non_na, "\n")

Total number of Session 1 surveys with non-NA StartDate: 2593 

Session2count_non_na <- sum(d$Session_Number == 2 & !is.na(d$StartDate))
cat("Total number of Session 2 surveys with non-NA StartDate:", Session2count_non_na, "\n")

Total number of Session 2 surveys with non-NA StartDate: 2803 

Session3count_non_na <- sum(d$Session_Number == 3 & !is.na(d$StartDate))
cat("Total number of Session 3 surveys with non-NA StartDate:", Session3count_non_na, "\n")

Total number of Session 3 surveys with non-NA StartDate: 2775 

Session4count_non_na <- sum(d$Session_Number == 4 & !is.na(d$StartDate))
cat("Total number of Session 4 surveys with non-NA StartDate:", Session4count_non_na, "\n")

Total number of Session 4 surveys with non-NA StartDate: 2813 

# Calculate the max possible number of surveys
total_possible_surveys <- 4 * 28  

# Count completed surveys per PID
survey_counts <- d %>%
  filter(Finished == 1) %>%
  group_by(PID) %>%
  summarise(surveys_completed = n())

# Calculate percentage completion per participant
Percent_Complete_byPID <- survey_counts %>%
  mutate(Percent_Complete = (surveys_completed / total_possible_surveys) * 100)

# View the result
print(Percent_Complete_byPID)

# A tibble: 179 × 3
     PID surveys_completed Percent_Complete
   <dbl>             <int>            <dbl>
 1  1003               101            90.2 
 2  1004                76            67.9 
 3  1006                27            24.1 
 4  1007                96            85.7 
 5  1017                 8             7.14
 6  1019                59            52.7 
 7  1020               107            95.5 
 8  1022                84            75   
 9  1023                17            15.2 
10  1026                96            85.7 
# ℹ 169 more rows

summary(Percent_Complete_byPID)

      PID       surveys_completed Percent_Complete 
 Min.   :1003   Min.   :  2.00    Min.   :  1.786  
 1st Qu.:1210   1st Qu.: 28.50    1st Qu.: 25.446  
 Median :2001   Median : 69.00    Median : 61.607  
 Mean   :1668   Mean   : 61.36    Mean   : 54.789  
 3rd Qu.:2198   3rd Qu.: 93.50    3rd Qu.: 83.482  
 Max.   :2286   Max.   :112.00    Max.   :100.000  

sd(Percent_Complete_byPID$surveys_completed)

[1] 35.23145

# List of ER variables
er_vars <- c("ER_1_Dis", "ER_2_Rum", "ER_3_SBl", "ER_4_ExprS", "ER_5_ExperS", "ER_6_Acc", "ER_7_Pla", "ER_8_Rea", "ER_9_ESu", "ER_10_Rel")

# Part 1: Count how many surveys had at least one ER strategy endorsed
surveys_with_ER <- d %>%
  mutate(ER_endorsed = rowSums(across(all_of(er_vars)) > 0) > 0) %>%
  filter(ER_endorsed == TRUE)

# Total number of surveys with at least one ER strategy endorsed
total_surveys_with_ER <- nrow(surveys_with_ER)

# Part 2: Calculate mean, median, SD, and range of ER strategies endorsed per survey
ER_usage_stats <- surveys_with_ER %>%
  mutate(ER_endorsed_count = rowSums(across(all_of(er_vars)) > 0)) %>%
  summarise(
    mean_ER = mean(ER_endorsed_count, na.rm = TRUE),
    median_ER = median(ER_endorsed_count, na.rm = TRUE),
    sd_ER = sd(ER_endorsed_count, na.rm = TRUE),
    range_ER = paste(min(ER_endorsed_count), "-", max(ER_endorsed_count))
  )

# Output results
cat("Total surveys with at least one ER strategy endorsed:", total_surveys_with_ER, "\n")

Total surveys with at least one ER strategy endorsed: 3889 

print(ER_usage_stats)

   mean_ER median_ER    sd_ER range_ER
1 1.684495         1 1.007671   1 - 10

# Add 'any ER' (any strategy except 'ER_0_Non')
d <- d %>%
  mutate(Any_ER = rowSums(across(all_of(er_vars)) > 0) > 0)

# Count the number of surveys where each strategy was endorsed
survey_counts <- d %>%
  summarise(across(all_of(c(er_vars, "Any_ER", "ER_0_Non")), 
                   ~ sum(. > 0, na.rm = TRUE), 
                   .names = "Surveys_{.col}"))

# View results
print(survey_counts)

  Surveys_ER_1_Dis Surveys_ER_2_Rum Surveys_ER_3_SBl Surveys_ER_4_ExprS
1             1239              414              328                364
  Surveys_ER_5_ExperS Surveys_ER_6_Acc Surveys_ER_7_Pla Surveys_ER_8_Rea
1                 570             1071              800              351
  Surveys_ER_9_ESu Surveys_ER_10_Rel Surveys_Any_ER Surveys_ER_0_Non
1              405              1009           3889             7095

# Count unique participants who endorsed each strategy
participant_counts <- d %>%
  group_by(PID) %>%
  summarise(across(all_of(c(er_vars, "Any_ER", "ER_0_Non")), 
                   ~ any(. > 0, na.rm = TRUE), 
                   .names = "Used_{.col}")) %>%
  summarise(across(starts_with("Used_"), 
                   ~ sum(.), 
                   .names = "Participants_{.col}")) 

# Calculate the percentage of participants who endorsed each strategy
total_participants <- n_distinct(d$PID)

participant_percentages <- participant_counts %>%
  mutate(across(starts_with("Participants_"), 
                ~ (. / total_participants) * 100, 
                .names = "Percent_{.col}"))

# View results
print(participant_percentages)

# A tibble: 1 × 24
  Participants_Used_ER_1_Dis Participants_Used_ER_2_Rum Participants_Used_ER_3…¹
                       <int>                      <int>                    <int>
1                        137                         99                       81
# ℹ abbreviated name: ¹​Participants_Used_ER_3_SBl
# ℹ 21 more variables: Participants_Used_ER_4_ExprS <int>,
#   Participants_Used_ER_5_ExperS <int>, Participants_Used_ER_6_Acc <int>,
#   Participants_Used_ER_7_Pla <int>, Participants_Used_ER_8_Rea <int>,
#   Participants_Used_ER_9_ESu <int>, Participants_Used_ER_10_Rel <int>,
#   Participants_Used_Any_ER <int>, Participants_Used_ER_0_Non <int>,
#   Percent_Participants_Used_ER_1_Dis <dbl>, …

# Count unique participants who endorsed each strategy by gender
gender_split_counts <- d %>%
  group_by(PID, T1_Gender) %>%
  summarise(across(all_of(c(er_vars, "Any_ER", "ER_0_Non")), 
                   ~ any(. > 0, na.rm = TRUE), 
                   .names = "Used_{.col}")) %>%
  group_by(T1_Gender) %>%
  summarise(across(starts_with("Used_"), 
                   ~ sum(.), 
                   .names = "Participants_{.col}"))

`summarise()` has grouped output by 'PID'. You can override using the `.groups`
argument.

# View the result
gender_split_counts

# A tibble: 2 × 13
  T1_Gender Participants_Used_ER…¹ Participants_Used_ER…² Participants_Used_ER…³
      <dbl>                  <int>                  <int>                  <int>
1         0                     32                     27                     18
2         1                    105                     72                     63
# ℹ abbreviated names: ¹​Participants_Used_ER_1_Dis,
#   ²​Participants_Used_ER_2_Rum, ³​Participants_Used_ER_3_SBl
# ℹ 9 more variables: Participants_Used_ER_4_ExprS <int>,
#   Participants_Used_ER_5_ExperS <int>, Participants_Used_ER_6_Acc <int>,
#   Participants_Used_ER_7_Pla <int>, Participants_Used_ER_8_Rea <int>,
#   Participants_Used_ER_9_ESu <int>, Participants_Used_ER_10_Rel <int>,
#   Participants_Used_Any_ER <int>, Participants_Used_ER_0_Non <int>

# Calculate the mean total number of surveys per participant
mean_total_surveys <- d %>%
  group_by(PID) %>%
  summarise(total_surveys = n()) %>%
  summarise(mean_total_surveys = mean(total_surveys, na.rm = TRUE))

# Calculate the mean number of surveys endorsed per participant for each strategy and calculate the percentage
mean_and_percentage_per_strategy <- d %>%
  group_by(PID) %>%
  summarise(
    across(all_of(er_vars), ~ sum(. > 0, na.rm = TRUE)),  # Count endorsements for each ER variable
    Any_ER = sum(rowSums(across(all_of(er_vars), ~ . > 0), na.rm = TRUE) > 0),  # Count Any_ER
    ER_0_Non = sum(ER_0_Non > 0, na.rm = TRUE)  # Count ER_0_Non
  ) %>%
  summarise(
    across(everything(), mean, na.rm = TRUE)  # Calculate means for each strategy
  ) %>%
  mutate(
    Any_ER_percent = (Any_ER / mean_total_surveys$mean_total_surveys) * 100,  # Calculate percentage for Any_ER
    ER_0_Non_percent = (ER_0_Non / mean_total_surveys$mean_total_surveys) * 100,  # Calculate percentage for ER_0_Non
    across(starts_with("ER_"), ~ (. / mean_total_surveys$mean_total_surveys) * 100, .names = "{.col}_percent")  # Calculate percentage for all ER strategies
  )

Warning: There was 1 warning in `summarise()`.
ℹ In argument: `across(everything(), mean, na.rm = TRUE)`.
Caused by warning:
! The `...` argument of `across()` is deprecated as of dplyr 1.1.0.
Supply arguments directly to `.fns` through an anonymous function instead.

  # Previously
  across(a:b, mean, na.rm = TRUE)

  # Now
  across(a:b, \(x) mean(x, na.rm = TRUE))

# View the result
mean_and_percentage_per_strategy

# A tibble: 1 × 26
    PID ER_1_Dis ER_2_Rum ER_3_SBl ER_4_ExprS ER_5_ExperS ER_6_Acc ER_7_Pla
  <dbl>    <dbl>    <dbl>    <dbl>      <dbl>       <dbl>    <dbl>    <dbl>
1 1668.     6.92     2.31     1.83       2.03        3.18     5.98     4.47
# ℹ 18 more variables: ER_8_Rea <dbl>, ER_9_ESu <dbl>, ER_10_Rel <dbl>,
#   Any_ER <dbl>, ER_0_Non <dbl>, Any_ER_percent <dbl>, ER_0_Non_percent <dbl>,
#   ER_1_Dis_percent <dbl>, ER_2_Rum_percent <dbl>, ER_3_SBl_percent <dbl>,
#   ER_4_ExprS_percent <dbl>, ER_5_ExperS_percent <dbl>,
#   ER_6_Acc_percent <dbl>, ER_7_Pla_percent <dbl>, ER_8_Rea_percent <dbl>,
#   ER_9_ESu_percent <dbl>, ER_10_Rel_percent <dbl>,
#   ER_0_Non_percent_percent <dbl>

#number of males and females 
# Count the number of unique males (0) and females (1) in the sample
sex_counts <- d %>%
  group_by(PID) %>%
  summarise(T1_Sex = first(T1_Sex)) %>%  # Get the sex for each unique participant
  summarise(
    Males = sum(T1_Sex == 0, na.rm = TRUE),
    Females = sum(T1_Sex == 1, na.rm = TRUE)
  )

# View the result
print(sex_counts)

# A tibble: 1 × 2
  Males Females
  <int>   <int>
1    46     133

# Calculate mean, median, SD, and range of Age_2024 per unique participant
age_summary <- d %>%
  group_by(PID) %>%
  summarise(Age_2024 = first(Age_2024)) %>%  # Ensure each participant is counted once
  summarise(
    Mean_Age = mean(Age_2024, na.rm = TRUE),
    Median_Age = median(Age_2024, na.rm = TRUE),
    SD_Age = sd(Age_2024, na.rm = TRUE),
    Min_Age = min(Age_2024, na.rm = TRUE),
    Max_Age = max(Age_2024, na.rm = TRUE),
    Range_Age = Max_Age - Min_Age  # Compute the range
  )

# View the result
print(age_summary)

# A tibble: 1 × 6
  Mean_Age Median_Age SD_Age Min_Age Max_Age Range_Age
     <dbl>      <dbl>  <dbl>   <dbl>   <dbl>     <dbl>
1     27.0         26   5.18      18      36        18

# Count the number of unique men (0) and women (1) in the sample
gender_counts <- d %>%
  group_by(PID) %>%
  summarise(T1_Gender = first(T1_Gender)) %>%  # Get the sex for each unique participant
  summarise(
    Man = sum(T1_Gender == 0, na.rm = TRUE),
    Woman = sum(T1_Gender == 1, na.rm = TRUE),
  )

# View the result
print(gender_counts)

# A tibble: 1 × 2
    Man Woman
  <int> <int>
1    46   133

# Aggregate endorsements per PID, ensuring each PID is counted once per ethnicity
pid_summary <- d %>%
  group_by(PID) %>%
  summarise(across(starts_with("T1_Ethnicity_"), ~any(. == 1, na.rm = TRUE))) %>%
  ungroup()

# Count how many unique PIDs endorsed each ethnicity
ethnicity_counts <- pid_summary %>%
  summarise(across(starts_with("T1_Ethnicity_"), ~sum(. == TRUE)))

print(ethnicity_counts)

# A tibble: 1 × 11
  T1_Ethnicity_1 T1_Ethnicity_2 T1_Ethnicity_3 T1_Ethnicity_4 T1_Ethnicity_5
           <int>          <int>          <int>          <int>          <int>
1             79              3              1              0             80
# ℹ 6 more variables: T1_Ethnicity_6 <int>, T1_Ethnicity_7 <int>,
#   T1_Ethnicity_8 <int>, T1_Ethnicity_9 <int>, T1_Ethnicity_10 <int>,
#   T1_Ethnicity_10_TEXT <int>

# Define income bracket labels
income_labels <- c(
  "Less than $10,000",
  "$10,000 to $19,999",
  "$20,000 to $29,999",
  "$30,000 to $39,999",
  "$40,000 to $49,999",
  "$50,000 to $59,999",
  "$60,000 to $69,999",
  "$70,000 to $79,999",
  "$80,000 to $89,999",
  "$90,000 to $99,999",
  "$100,000 to $149,999",
  "$150,000 or more"
)

# Ensure unique endorsement per PID by taking the first non-NA income level
income_summary <- d %>%
  group_by(PID) %>%
  summarise(T1_Income = min(T1_Income, na.rm = TRUE)) %>%  # Get the lowest valid income level per PID
  ungroup()

# Remove cases where all values were NA
income_summary <- income_summary %>%
  filter(!is.infinite(T1_Income)) 

# Count occurrences of each income level
income_counts <- income_summary %>%
  count(T1_Income, name = "Count") %>%
  arrange(T1_Income)

# Assign labels
income_counts <- income_counts %>%
  mutate(Income_Bracket = income_labels[T1_Income])

# Print result
print(income_counts)

# A tibble: 12 × 3
   T1_Income Count Income_Bracket      
       <dbl> <int> <chr>               
 1         1    16 Less than $10,000   
 2         2     4 $10,000 to $19,999  
 3         3     8 $20,000 to $29,999  
 4         4    11 $30,000 to $39,999  
 5         5     7 $40,000 to $49,999  
 6         6    11 $50,000 to $59,999  
 7         7    10 $60,000 to $69,999  
 8         8     9 $70,000 to $79,999  
 9         9    14 $80,000 to $89,999  
10        10    14 $90,000 to $99,999  
11        11    41 $100,000 to $149,999
12        12    34 $150,000 or more    

#how long did surveys take?
mean(d$Duration_Sec, na.rm = TRUE)

[1] 136.825

sd(d$Duration_Sec, na.rm = TRUE)

[1] 241.3281

median(d$Duration_Sec, na.rm = TRUE)

[1] 81

#how long between surveys (betwene PID, within day)

# Calculate time differences between consecutive sessions (1-2, 2-3, 3-4) for each PID and Day_Number
average_time_between_consecutive_sessions <- d %>%
  arrange(PID, Day_Number, Session_Number) %>% # Ensure sorting by PID, Day, and Session
  group_by(PID, Day_Number) %>%
  mutate(time_diff = as.numeric(difftime(StartDate, lag(StartDate), units = "mins"))) %>%
  filter((Session_Number == 2 & lag(Session_Number) == 1) |
           (Session_Number == 3 & lag(Session_Number) == 2) |
           (Session_Number == 4 & lag(Session_Number) == 3)) %>%
  summarise(mean_time_diff_per_day = mean(time_diff, na.rm = TRUE)) %>%
  ungroup()

`summarise()` has grouped output by 'PID'. You can override using the `.groups`
argument.

# Calculate the overall average time between sessions across all PIDs and days
overall_average_time <- mean(average_time_between_consecutive_sessions$mean_time_diff_per_day, na.rm = TRUE)

# Print the overall average time
print(overall_average_time)

[1] 178.8311

#### How long between surveys####
# Calculate time differences between consecutive sessions (1-2, 2-3, 3-4)
time_diffs <- d %>%
  arrange(PID, Day_Number, Session_Number) %>%
  group_by(PID, Day_Number) %>%
  mutate(time_diff = as.numeric(difftime(StartDate, lag(StartDate), units = "mins"))) %>%

# Keep only true consecutive pairs
  filter((Session_Number == 2 & lag(Session_Number) == 1) |
           (Session_Number == 3 & lag(Session_Number) == 2) |
           (Session_Number == 4 & lag(Session_Number) == 3)) %>%
  ungroup()

# Summary statistics across ALL consecutive pairs
mean_time <- mean(time_diffs$time_diff, na.rm = TRUE)
sd_time   <- sd(time_diffs$time_diff, na.rm = TRUE)
range_time <- range(time_diffs$time_diff, na.rm = TRUE)

print(mean_time)

[1] 178.9201

print(sd_time)

[1] 41.50713

print(range_time)

[1]   6.016667 417.183333

# Count surveys relative to ±1 SD of the mean
n_below  <- sum(time_diffs$time_diff < (mean_time - sd_time), na.rm = TRUE)
n_within <- sum(time_diffs$time_diff >= (mean_time - sd_time) & time_diffs$time_diff <= (mean_time + sd_time), na.rm = TRUE)
n_above  <- sum(time_diffs$time_diff > (mean_time + sd_time), na.rm = TRUE)

n_below

[1] 1081

n_within

[1] 4340

n_above

[1] 1081

# Histogram 
ggplot(time_diffs, aes(x = time_diff)) +
  geom_histogram(binwidth = 10, colour = "black", fill = "lightblue") +
  labs(
    title = "Histogram of Time Between Consecutive Surveys",
    x = "Time Between Surveys (minutes)",
    y = "Frequency"
  ) +
  theme_minimal()

Warning: Removed 8534 rows containing non-finite outside the scale range
(`stat_bin()`).

# look at time intervals in a table

all_intervals <- time_diffs %>%
  mutate(previous_session = Session_Number - 1)

all_intervals <- all_intervals[ , c("PID", "Day_Number", "previous_session", 
                                    "Session_Number", "time_diff")]

all_intervals <- all_intervals[order(all_intervals$time_diff), ]

all_intervals

# A tibble: 15,036 × 5
     PID Day_Number previous_session Session_Number time_diff
   <dbl>      <int>            <dbl>          <int>     <dbl>
 1  2030         22                3              4      6.02
 2  1255         21                2              3     44.0 
 3  2196         13                2              3     45.7 
 4  1268          3                3              4     47.5 
 5  1003         14                1              2     49.2 
 6  2238          3                1              2     56.8 
 7  1222         23                1              2     58.0 
 8  2273         16                3              4     60.1 
 9  1251         11                2              3     60.1 
10  1036         11                3              4     60.3 
# ℹ 15,026 more rows

#calc -1 and +1 sd and create a subset of the data

lower_bound <- mean_time - sd_time
upper_bound <- mean_time + sd_time

lower_bound

[1] 137.4129

upper_bound

[1] 220.4272

time_diffs_filtered <- time_diffs %>%
  filter(time_diff >= lower_bound,
         time_diff <= upper_bound)

nrow(time_diffs_filtered)

[1] 4340

d_timediff_subset <- d %>%
  semi_join(time_diffs_filtered,
            by = c("PID", "Day_Number", "Session_Number"))

#### Number of Strategies####
df <- read_excel("~/Clin PhD/Research Project/Study 2. Quantitative Study MF/Data/EMA/EMA_data_09.05.25_part.xlsx")

# Count the number of strategies used (from 0 to 10) in each survey
df <- df %>%
  mutate(
    num_strategies = rowSums(cbind(ER_1_Dis, ER_2_Rum, ER_3_SBl, ER_4_ExprS,
                                   ER_5_ExperS, ER_6_Acc, ER_7_Pla, ER_8_Rea,
                                   ER_9_ESu, ER_10_Rel) == 1, na.rm = TRUE)
  )

# Count how many surveys had 0, 1, 2, ..., 10 strategies used
strategy_count <- df %>%
  group_by(num_strategies) %>%
  summarise(
    total_surveys = n(),
    percentage = total_surveys / nrow(df) * 100,
    .groups = "drop"
  )

# Count how many rows had ER_0_Non endorsed (ER_0_Non == 1)
non_endorsed_count <- df %>%
  filter(ER_0_Non == 1) %>%
  summarise(
    total_non_endorsed = n(),
    percentage_non_endorsed = total_non_endorsed / nrow(df) * 100
  )

# Output the results
print(strategy_count)

# A tibble: 11 × 3
   num_strategies total_surveys percentage
            <dbl>         <int>      <dbl>
 1              0         16159   80.6    
 2              1          2213   11.0    
 3              2          1036    5.17   
 4              3           422    2.10   
 5              4           145    0.723  
 6              5            41    0.205  
 7              6            18    0.0898 
 8              7            10    0.0499 
 9              8             1    0.00499
10              9             1    0.00499
11             10             2    0.00998

print(non_endorsed_count)

# A tibble: 1 × 2
  total_non_endorsed percentage_non_endorsed
               <int>                   <dbl>
1               7095                    35.4

# Count unique participants endorsing each count of strategies
unique_participants <- df %>%
  group_by(num_strategies) %>%
  summarise(
    unique_participants = n_distinct(PID),
    .groups = "drop"
  )

# View the result
print(unique_participants)

# A tibble: 11 × 2
   num_strategies unique_participants
            <dbl>               <int>
 1              0                 179
 2              1                 162
 3              2                 121
 4              3                  95
 5              4                  48
 6              5                  23
 7              6                   9
 8              7                  10
 9              8                   1
10              9                   1
11             10                   1

# Count unique participants by gender (1=man, 0=woman)
Sex_breakdown <- df %>%
  group_by(num_strategies, T1_Sex) %>%
  summarise(
    unique_participants = n_distinct(PID),
    .groups = "drop"
  ) %>%
  spread(key = T1_Sex, value = unique_participants, fill = 0) %>%
  mutate(
    total_participants = `1` + `0`,
    woman_percentage = (`1` / total_participants) * 100,
    man_percentage = (`0` / total_participants) * 100
  )

# View the result
print(Sex_breakdown)

# A tibble: 11 × 6
   num_strategies   `0`   `1` total_participants woman_percentage man_percentage
            <dbl> <dbl> <dbl>              <dbl>            <dbl>          <dbl>
 1              0    46   133                179             74.3          25.7 
 2              1    36   126                162             77.8          22.2 
 3              2    22    99                121             81.8          18.2 
 4              3    20    75                 95             78.9          21.1 
 5              4     8    40                 48             83.3          16.7 
 6              5     2    21                 23             91.3           8.70
 7              6     1     8                  9             88.9          11.1 
 8              7     1     9                 10             90            10   
 9              8     0     1                  1            100             0   
10              9     0     1                  1            100             0   
11             10     0     1                  1            100             0   

#mean number of surveys per participant that included each strategy and the mean number of surveys endorsing any strategy,
# Step 1: Count the number of surveys where each strategy was endorsed per participant
df_counts <- df %>%
  group_by(PID) %>%
  mutate(
    total_surveys_per_participant = n(),  # Count total surveys per participant
    across(c(ER_1_Dis, ER_2_Rum, ER_3_SBl, ER_4_ExprS, 
             ER_5_ExperS, ER_6_Acc, ER_7_Pla, ER_8_Rea, 
             ER_9_ESu, ER_10_Rel), 
           ~ sum(. == 1, na.rm = TRUE), .names = "count_{.col}"),
    
    # Count surveys where at least one strategy was used (excluding ER_0_Non)
    total_surveys_with_any_strategy = sum(rowSums(across(c(ER_1_Dis:ER_10_Rel), ~ . == 1), na.rm = TRUE) > 0 & ER_0_Non != 1, na.rm = TRUE)
  ) %>%
  ungroup()

# Step 2: Compute the mean number of surveys endorsed per strategy across all participants
mean_counts <- df_counts %>%
  summarise(
    across(starts_with("count_"), ~ mean(.), .names = "mean_surveys_{.col}"),
    mean_surveys_with_any_strategy = mean(total_surveys_with_any_strategy)
  )

# View result
print(mean_counts)

# A tibble: 1 × 11
  mean_surveys_count_ER_1_Dis mean_surveys_count_ER_2_Rum mean_surveys_count_E…¹
                        <dbl>                       <dbl>                  <dbl>
1                        6.92                        2.31                   1.83
# ℹ abbreviated name: ¹​mean_surveys_count_ER_3_SBl
# ℹ 8 more variables: mean_surveys_count_ER_4_ExprS <dbl>,
#   mean_surveys_count_ER_5_ExperS <dbl>, mean_surveys_count_ER_6_Acc <dbl>,
#   mean_surveys_count_ER_7_Pla <dbl>, mean_surveys_count_ER_8_Rea <dbl>,
#   mean_surveys_count_ER_9_ESu <dbl>, mean_surveys_count_ER_10_Rel <dbl>,
#   mean_surveys_with_any_strategy <dbl>

# Count how many times ER_0_Non was endorsed per participant
df_ER0_counts <- df %>%
  group_by(PID) %>%
  summarise(total_ER0_Non = sum(ER_0_Non == 1, na.rm = TRUE)) %>%
  ungroup()

# Calculate the mean number of times ER_0_Non was endorsed across participants
mean_ER0_Non <- mean(df_ER0_counts$total_ER0_Non, na.rm = TRUE)

# View result
print(mean_ER0_Non)

[1] 39.63687

# Count the number of surveys where each strategy was endorsed per participant
df_counts <- df %>%
  group_by(PID) %>%
  mutate(
    total_surveys_per_participant = n(),  # Count total surveys per participant
    across(c(ER_1_Dis, ER_2_Rum, ER_3_SBl, ER_4_ExprS, 
             ER_5_ExperS, ER_6_Acc, ER_7_Pla, ER_8_Rea, 
             ER_9_ESu, ER_10_Rel), 
           ~ sum(. == 1, na.rm = TRUE), .names = "count_{.col}"),
    
    # Count surveys where at least one strategy was used (excluding ER_0_Non)
    total_surveys_with_any_strategy = sum(rowSums(across(c(ER_1_Dis:ER_10_Rel), ~ . == 1), na.rm = TRUE) > 0 & ER_0_Non != 1, na.rm = TRUE)
  ) %>%
  ungroup()

# Compute the mean, standard deviation, and range for each strategy across all participants
stats_counts <- df_counts %>%
  summarise(
    across(starts_with("count_"), 
           list(mean = ~ mean(., na.rm = TRUE),
                sd = ~ sd(., na.rm = TRUE),
                min = ~ min(., na.rm = TRUE),
                max = ~ max(., na.rm = TRUE)), 
           .names = "{.fn}_surveys_{.col}"),
    
    mean_surveys_with_any_strategy = mean(total_surveys_with_any_strategy, na.rm = TRUE),
    sd_surveys_with_any_strategy = sd(total_surveys_with_any_strategy, na.rm = TRUE),
    min_surveys_with_any_strategy = min(total_surveys_with_any_strategy, na.rm = TRUE),
    max_surveys_with_any_strategy = max(total_surveys_with_any_strategy, na.rm = TRUE)
  )

# View results for strategies
print(stats_counts)

# A tibble: 1 × 44
  mean_surveys_count_ER_1_Dis sd_surveys_count_ER_1_Dis min_surveys_count_ER_1…¹
                        <dbl>                     <dbl>                    <int>
1                        6.92                      11.7                        0
# ℹ abbreviated name: ¹​min_surveys_count_ER_1_Dis
# ℹ 41 more variables: max_surveys_count_ER_1_Dis <int>,
#   mean_surveys_count_ER_2_Rum <dbl>, sd_surveys_count_ER_2_Rum <dbl>,
#   min_surveys_count_ER_2_Rum <int>, max_surveys_count_ER_2_Rum <int>,
#   mean_surveys_count_ER_3_SBl <dbl>, sd_surveys_count_ER_3_SBl <dbl>,
#   min_surveys_count_ER_3_SBl <int>, max_surveys_count_ER_3_SBl <int>,
#   mean_surveys_count_ER_4_ExprS <dbl>, sd_surveys_count_ER_4_ExprS <dbl>, …

# Count how many times ER_0_Non was endorsed per participant
df_ER0_counts <- df %>%
  group_by(PID) %>%
  summarise(total_ER0_Non = sum(ER_0_Non == 1, na.rm = TRUE)) %>%
  ungroup()

# Compute the mean, SD, and range for ER_0_Non endorsements
stats_ER0_Non <- df_ER0_counts %>%
  summarise(
    mean_ER0_Non = mean(total_ER0_Non, na.rm = TRUE),
    sd_ER0_Non = sd(total_ER0_Non, na.rm = TRUE),
    min_ER0_Non = min(total_ER0_Non, na.rm = TRUE),
    max_ER0_Non = max(total_ER0_Non, na.rm = TRUE)
  )

# View result for ER_0_Non
print(stats_ER0_Non)

# A tibble: 1 × 4
  mean_ER0_Non sd_ER0_Non min_ER0_Non max_ER0_Non
         <dbl>      <dbl>       <int>       <int>
1         39.6       29.4           0         101

#Number of surveys completed at each Session Number
table(d$Session_Number[!is.na(d$StartDate)])

   1    2    3    4 
2593 2803 2775 2813 

#Number of pairs completed across the day
d$session_pair <- NA

d$session_pair[d$valid_pair == 1 & 
                 d$prev_session_number == 1 & d$Session_Number == 2] <- "1–2"

d$session_pair[d$valid_pair == 1 & 
                 d$prev_session_number == 2 & d$Session_Number == 3] <- "2–3"

d$session_pair[d$valid_pair == 1 & 
                 d$prev_session_number == 3 & d$Session_Number == 4] <- "3–4"

table(d$session_pair, useNA = "no")

 1–2  2–3  3–4 
2049 2220 2233 

#### Checking Fatigue Scores####
df <- read_excel("~/Clin PhD/Research Project/Study 2. Quantitative Study MF/Data/EMA/EMA_data_09.05.25_part.xlsx")

#### within day correlations for MF####
# Filter relevant columns
df_filtered <- subset(d, select = c(PID, Day_Number, Session_Number, MF))

# Calculate correlation across all days for Session 1 and Session 2
MFcor_result_1_2 <- df_filtered %>%
  filter(Session_Number %in% c(1, 2)) %>%  # Keep only sessions 1 and 2
  pivot_wider(names_from = Session_Number, values_from = MF, names_prefix = "Session_") %>%  # Pivot data for paired structure
  summarise(
    correlation_1_2 = cor(Session_1, Session_2, use = "pairwise.complete.obs")
  )

# Print the result
print(MFcor_result_1_2)

# A tibble: 1 × 1
  correlation_1_2
            <dbl>
1           0.617

MFcor_result_2_3 <- df_filtered %>%
  filter(Session_Number %in% c(2, 3)) %>%  # Keep only sessions 2 and 3
  pivot_wider(names_from = Session_Number, values_from = MF, names_prefix = "Session_") %>%  # Pivot data for paired structure
  summarise(
    correlation_2_3 = cor(Session_2, Session_3, use = "pairwise.complete.obs")
  )

# Print the result
print(MFcor_result_2_3)

# A tibble: 1 × 1
  correlation_2_3
            <dbl>
1           0.632

MFcor_result_3_4 <- df_filtered %>%
  filter(Session_Number %in% c(3, 4)) %>%  # Keep only sessions 2 and 3
  pivot_wider(names_from = Session_Number, values_from = MF, names_prefix = "Session_") %>%  # Pivot data for paired structure
  summarise(
    correlation_3_4 = cor(Session_3, Session_4, use = "pairwise.complete.obs")
  )

# Print the result
print(MFcor_result_3_4)

# A tibble: 1 × 1
  correlation_3_4
            <dbl>
1           0.609

(sum(MFcor_result_1_2 + MFcor_result_2_3 + MFcor_result_3_4))/3

[1] 0.619491

#### within day correlations for stress####
# Filter relevant columns
Strdf_filtered <- subset(d, select = c(PID, Day_Number, Session_Number, Str_Overall))

# Calculate correlation across all days for Session 1 and Session 2
Strcor_result_1_2 <- Strdf_filtered %>%
  filter(Session_Number %in% c(1, 2)) %>%  # Keep only sessions 1 and 2
  pivot_wider(names_from = Session_Number, values_from = Str_Overall, names_prefix = "Session_") %>%  # Pivot data for paired structure
  summarise(
    correlation_1_2 = cor(Session_1, Session_2, use = "pairwise.complete.obs")
  )

# Print the result
print(Strcor_result_1_2)

# A tibble: 1 × 1
  correlation_1_2
            <dbl>
1           0.670

Strcor_result_2_3 <- Strdf_filtered %>%
  filter(Session_Number %in% c(2, 3)) %>%  # Keep only sessions 2 and 3
  pivot_wider(names_from = Session_Number, values_from = Str_Overall, names_prefix = "Session_") %>%  # Pivot data for paired structure
  summarise(
    correlation_2_3 = cor(Session_2, Session_3, use = "pairwise.complete.obs")
  )

# Print the result
print(Strcor_result_2_3)

# A tibble: 1 × 1
  correlation_2_3
            <dbl>
1           0.715

Strcor_result_3_4 <- Strdf_filtered %>%
  filter(Session_Number %in% c(3, 4)) %>%  # Keep only sessions 2 and 3
  pivot_wider(names_from = Session_Number, values_from = Str_Overall, names_prefix = "Session_") %>%  # Pivot data for paired structure
  summarise(
    correlation_3_4 = cor(Session_3, Session_4, use = "pairwise.complete.obs")
  )

# Print the result
print(Strcor_result_3_4)

# A tibble: 1 × 1
  correlation_3_4
            <dbl>
1           0.737

(sum(Strcor_result_1_2 + Strcor_result_2_3 + Strcor_result_3_4))/3

[1] 0.7072349

#### Datachecks####

#proportion completed
#Count total surveys completed per PID
survey_counts <- d %>%
  group_by(PID) %>%
  summarize(
    total_surveys_completed = sum(!is.na(MF))/112,
    Age_2024 = first(Age_2024),          # Ensure consistent grouping
    T1_Gender = first(T1_Gender), # Keep relevant demographic info
    T1_Income = first(T1_Income),
    T1_Education = first(T1_Education),
    Str_Overall_mean = first(Str_Overall_mean),
    MF = first(MF)
  )

t.test(total_surveys_completed~T1_Gender, data = survey_counts)

    Welch Two Sample t-test

data:  total_surveys_completed by T1_Gender
t = -1.3164, df = 81.882, p-value = 0.1917
alternative hypothesis: true difference in means between group 0 and group 1 is not equal to 0
95 percent confidence interval:
 -0.17327621  0.03527593
sample estimates:
mean in group 0 mean in group 1 
      0.4961180       0.5651182 

summary(aov(total_surveys_completed~T1_Income, data = survey_counts)) #same as t test but for multiple categories

             Df Sum Sq Mean Sq F value Pr(>F)
T1_Income     1   0.00 0.00002       0   0.99
Residuals   177  17.61 0.09951               

cor.test(~T1_Income + total_surveys_completed, data = survey_counts)

    Pearson's product-moment correlation

data:  T1_Income and total_surveys_completed
t = 0.012363, df = 177, p-value = 0.9901
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 -0.1457629  0.1475814
sample estimates:
         cor 
0.0009292684 

summary(aov(total_surveys_completed~Str_Overall_mean, data = survey_counts)) 

                  Df Sum Sq Mean Sq F value  Pr(>F)   
Str_Overall_mean   1  0.798  0.7981   8.401 0.00422 **
Residuals        177 16.815  0.0950                   
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

cor.test(~Str_Overall_mean + total_surveys_completed, data = survey_counts)

    Pearson's product-moment correlation

data:  Str_Overall_mean and total_surveys_completed
t = -2.8985, df = 177, p-value = 0.004224
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 -0.34865927 -0.06833432
sample estimates:
      cor 
-0.212873 

summary(aov(total_surveys_completed~T1_Education, data = survey_counts)) 

              Df Sum Sq Mean Sq F value Pr(>F)  
T1_Education   1  0.317  0.3173   3.247 0.0733 .
Residuals    177 17.296  0.0977                 
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

cor.test(~T1_Education + total_surveys_completed, data = survey_counts)

    Pearson's product-moment correlation

data:  T1_Education and total_surveys_completed
t = 1.802, df = 177, p-value = 0.07325
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 -0.01270482  0.27546700
sample estimates:
      cor 
0.1342176 

summary(aov(total_surveys_completed~Age_2024, data = survey_counts)) 

             Df Sum Sq Mean Sq F value  Pr(>F)   
Age_2024      1  0.835  0.8347   8.806 0.00342 **
Residuals   177 16.778  0.0948                   
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

cor.test(~Age_2024 + total_surveys_completed, data = survey_counts)

    Pearson's product-moment correlation

data:  Age_2024 and total_surveys_completed
t = 2.9675, df = 177, p-value = 0.003418
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 0.07337166 0.35309871
sample estimates:
      cor 
0.2177011 

summary(aov(total_surveys_completed~MF, data = survey_counts)) 

             Df Sum Sq Mean Sq F value Pr(>F)
MF            1  0.001 0.00089   0.009  0.924
Residuals   138 13.564 0.09829               
39 observations deleted due to missingness

cor.test(~MF + total_surveys_completed, data = survey_counts)

    Pearson's product-moment correlation

data:  MF and total_surveys_completed
t = 0.095028, df = 138, p-value = 0.9244
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 -0.1580263  0.1737591
sample estimates:
        cor 
0.008089038 

#test difference between endorsement across strategies.
# Remove the 'Surveys_Any_ER' and 'Surveys_ER_0_Non' rows
participant_counts_filtered <- participant_counts[, !grepl("Participants_Used_Any_ER|Participants_Used_ER_0_Non", colnames(participant_counts))]

# Convert the tibble to a numeric vector
counts_vector <- as.numeric(participant_counts_filtered[1, ])

# Perform the chi-squared test
chisq.test(counts_vector)

    Chi-squared test for given probabilities

data:  counts_vector
X-squared = 41.357, df = 9, p-value = 4.306e-06

## weekdays and weekends 
weekdays2  <- d %>%
  group_by(weekdays(StartDate)) %>%
  summarize(
    total_surveys_completed = sum(!is.na(StartDate))
  )

# Survey counts per day
survey_counts <- c(Friday = 1563, Monday = 1578, Saturday = 1508, 
                   Sunday = 1523, Thursday = 1604, Tuesday = 1595, Wednesday = 1613)

# Expected frequency assuming equal distribution
expected_counts <- rep(sum(survey_counts) / length(survey_counts), length(survey_counts))

# Perform Chi-square goodness-of-fit test
chi_test <- chisq.test(survey_counts, p = rep(1/7, 7))

# Print results
print(chi_test)

    Chi-squared test for given probabilities

data:  survey_counts
X-squared = 6.2396, df = 6, p-value = 0.3969

#using the code from demogrpahics section -
strategy_counts <- survey_counts

# Chi-square goodness-of-fit test (equal expected frequencies)
expected_counts <- rep(sum(strategy_counts) / length(strategy_counts), length(strategy_counts))

chi_test <- chisq.test(strategy_counts, p = rep(1/length(strategy_counts), length(strategy_counts)))

# Print results
print(chi_test)

    Chi-squared test for given probabilities

data:  strategy_counts
X-squared = 6.2396, df = 6, p-value = 0.3969

#check how many pairs we have per PID
# Ensure Day_Number and Session_Number are integers
d <- d %>%
  mutate(
    Day_Number = as.integer(Day_Number),
    Session_Number = as.integer(Session_Number)
  ) %>%
  # Sort by PID, Day_Number, and Session_Number
  arrange(PID, Day_Number, Session_Number)

# Identify valid pairs
d <- d %>%
  group_by(PID) %>%
  mutate(
    prev_day_number = lag(Day_Number),
    prev_session_number = lag(Session_Number),
    prev_start_date = lag(StartDate),
    valid_pair = ifelse(
      !is.na(StartDate) & !is.na(prev_start_date) & 
        Day_Number == prev_day_number & 
        Session_Number == prev_session_number + 1, 
      1, 0
    )
  ) %>%
  ungroup()

#Aggregate and Summarize 
# Count valid pairs per participant
pairs_per_person <- d %>%
  group_by(PID) %>%
  summarise(
    num_valid_pairs = sum(valid_pair, na.rm = TRUE)
  )

# Calculate statistics
min_pairs <- min(pairs_per_person$num_valid_pairs, na.rm = TRUE)
max_pairs <- max(pairs_per_person$num_valid_pairs, na.rm = TRUE)
median_valid_pairs <- median(pairs_per_person$num_valid_pairs, na.rm = TRUE)

cat("Minimum number of pairs per participant:", min_pairs, "\n")

Minimum number of pairs per participant: 1 

cat("Maximum number of pairs per participant:", max_pairs, "\n")

Maximum number of pairs per participant: 84 

cat("Median number of valid pairs per participant:", median_valid_pairs, "\n")

Median number of valid pairs per participant: 35 

# Create a frequency table with the full range of 0 to 84
pair_freq_df <- pairs_per_person %>%
  count(num_valid_pairs) %>%
  complete(num_valid_pairs = 0:84, fill = list(n = 0)) %>%
  rename(Number_of_Pairs = num_valid_pairs, Frequency = n)

# View result
print(pair_freq_df)

# A tibble: 85 × 2
   Number_of_Pairs Frequency
             <dbl>     <int>
 1               0         0
 2               1        10
 3               2         6
 4               3         5
 5               4         3
 6               5         5
 7               6         3
 8               7         2
 9               8         1
10               9         4
# ℹ 75 more rows

3 Test Bidirectional impact of Mental Fatigue and not using ER strategies

#### Test MF and not using strategies####

# m_MF_ERNon <- brm(MF ~ Prev_ER_0_Non + T1_Gender + Age_2024 + Prev_Str_Overall
#                   + Prev_MF_Within + (1 | PID),
#                   family = cumulative(link = "logit", threshold = "flexible"), data = d,
#                   backend = "cmdstanr", chains = 4, iter = 4000, warmup = 1000, cores = 4)
# saveRDS(m_MF_ERNon, file = "m_MF_ERNon.RDS")

# Load the model from the saved RDS file
m_MF_ERNon <- readRDS("../m_MF_ERNon.RDS")

# Display the summary of the model
summary(m_MF_ERNon)

 Family: cumulative 
  Links: mu = logit; disc = identity 
Formula: MF ~ Prev_ER_0_Non + T1_Gender + Age_2024 + Prev_Str_Overall + Prev_MF_Within + (1 | PID) 
   Data: d (Number of observations: 6492) 
  Draws: 4 chains, each with iter = 4000; warmup = 1000; thin = 1;
         total post-warmup draws = 12000

Multilevel Hyperparameters:
~PID (Number of levels: 179) 
              Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
sd(Intercept)     1.82      0.11     1.61     2.05 1.00     1807     3363

Regression Coefficients:
                 Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
Intercept[1]        -2.20      0.78    -3.77    -0.70 1.00     1004     3131
Intercept[2]         0.59      0.78    -0.99     2.09 1.00     1006     3085
Intercept[3]         2.83      0.78     1.25     4.32 1.00     1009     3100
Intercept[4]         4.90      0.78     3.32     6.40 1.00     1024     3133
Prev_ER_0_Non        0.01      0.07    -0.12     0.14 1.00    15923     9617
T1_Gender           -0.34      0.33    -1.00     0.30 1.01     1220     2158
Age_2024            -0.01      0.03    -0.07     0.04 1.00     1086     2634
Prev_Str_Overall     0.11      0.02     0.08     0.15 1.00    11482     9390
Prev_MF_Within       0.94      0.04     0.87     1.01 1.00    18083     9186

Further Distributional Parameters:
     Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
disc     1.00      0.00     1.00     1.00   NA       NA       NA

Draws were sampled using sample(hmc). For each parameter, Bulk_ESS
and Tail_ESS are effective sample size measures, and Rhat is the potential
scale reduction factor on split chains (at convergence, Rhat = 1).

exp(fixef(m_MF_ERNon))

                    Estimate Est.Error        Q2.5      Q97.5
Intercept[1]       0.1111316  2.178606  0.02308007   0.494835
Intercept[2]       1.8035970  2.176698  0.37197980   8.081843
Intercept[3]      16.8798005  2.180079  3.49800743  74.885891
Intercept[4]     133.8795376  2.190611 27.60930904 602.631712
Prev_ER_0_Non      1.0124328  1.068405  0.88921811   1.152963
T1_Gender          0.7125944  1.393153  0.36796806   1.349248
Age_2024           0.9882098  1.027226  0.93635275   1.040582
Prev_Str_Overall   1.1180724  1.017744  1.08058019   1.156918
Prev_MF_Within     2.5662207  1.037372  2.38678799   2.758901

pd_m_MF_ERNon <- pd(m_MF_ERNon)
pd_m_MF_ERNon

Probability of Direction

Parameter        |     pd
-------------------------
Intercept[1]     | 99.82%
Intercept[2]     | 77.78%
Intercept[3]     |   100%
Intercept[4]     |   100%
Prev_ER_0_Non    | 57.27%
T1_Gender        | 84.85%
Age_2024         | 66.24%
Prev_Str_Overall |   100%
Prev_MF_Within   |   100%

# For Prev_MF             
b <- -0.02 
SE <- 0.04
z_value <- b / SE
p_value <- 2 * (1 - pnorm(abs(z_value)))  # Two-tailed test
p_value

[1] 0.6170751

table(d$ER_0_Non)

   0    1 
3889 7095 

4 AIM 1 - To explore whether emotion regulation strategies predict subsequent mental fatigue and stress

#To explore whether emotion regulation strategies predict subsequent mental fatigue
#### m_MF_ERall_final ####
# rerun <- TRUE
# 
# if (rerun) {
#   start_time <- Sys.time()
#   print(start_time)
#   m_MF_ERall_final <- brm(MF ~ Prev_ER_1_Dis + Prev_ER_2_Rum + Prev_ER_3_SBl + Prev_ER_4_ExprS 
#                          + Prev_ER_5_ExperS + Prev_ER_6_Acc + Prev_ER_7_Pla + Prev_ER_8_Rea
#                          + Prev_ER_9_ESu + Prev_ER_10_Rel + T1_Gender + Age_2024 + Prev_Str_Overall 
#                          + Prev_MF_Within + (1 | PID), 
#                          family = cumulative(link = "logit", threshold = "flexible"), data = d,
#                          backend = "cmdstanr", chains = 4, iter = 4000, warmup = 1000, cores = 4)
#   saveRDS(m_MF_ERall_final, file = "m_MF_ERall_final.RDS")
#   end_time <- Sys.time()
#   print(end_time)
# }

m_MF_ERall_final <- readRDS("m_MF_ERall_final.RDS")
summary(m_MF_ERall_final)

 Family: cumulative 
  Links: mu = logit; disc = identity 
Formula: MF ~ Prev_ER_1_Dis + Prev_ER_2_Rum + Prev_ER_3_SBl + Prev_ER_4_ExprS + Prev_ER_5_ExperS + Prev_ER_6_Acc + Prev_ER_7_Pla + Prev_ER_8_Rea + Prev_ER_9_ESu + Prev_ER_10_Rel + T1_Gender + Age_2024 + Prev_Str_Overall + Prev_MF_Within + (1 | PID) 
   Data: d (Number of observations: 6492) 
  Draws: 4 chains, each with iter = 4000; warmup = 1000; thin = 1;
         total post-warmup draws = 12000

Multilevel Hyperparameters:
~PID (Number of levels: 179) 
              Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
sd(Intercept)     1.81      0.11     1.61     2.05 1.00     1348     3495

Regression Coefficients:
                 Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
Intercept[1]        -2.23      0.80    -3.74    -0.62 1.00      984     1949
Intercept[2]         0.56      0.80    -0.95     2.17 1.00      982     2029
Intercept[3]         2.80      0.80     1.28     4.40 1.00      986     1992
Intercept[4]         4.87      0.81     3.34     6.50 1.00      994     1983
Prev_ER_1_Dis        0.22      0.09     0.04     0.40 1.00    18502     9350
Prev_ER_2_Rum        0.00      0.14    -0.28     0.28 1.00    20430     8789
Prev_ER_3_SBl       -0.14      0.17    -0.46     0.19 1.00    23824     8556
Prev_ER_4_ExprS      0.02      0.14    -0.25     0.28 1.00    21983     9010
Prev_ER_5_ExperS    -0.03      0.12    -0.27     0.21 1.00    19792     9609
Prev_ER_6_Acc       -0.03      0.09    -0.22     0.15 1.00    21328     8994
Prev_ER_7_Pla       -0.06      0.11    -0.27     0.15 1.00    19890     9325
Prev_ER_8_Rea        0.03      0.15    -0.27     0.32 1.00    22800     9017
Prev_ER_9_ESu       -0.09      0.14    -0.36     0.17 1.00    21600     9563
Prev_ER_10_Rel      -0.09      0.10    -0.28     0.11 1.00    20807     8573
T1_Gender           -0.34      0.33    -0.96     0.31 1.00     1045     2151
Age_2024            -0.01      0.03    -0.06     0.04 1.00      978     1888
Prev_Str_Overall     0.11      0.02     0.08     0.15 1.00    10538     9546
Prev_MF_Within       0.94      0.04     0.87     1.02 1.00    18554     9162

Further Distributional Parameters:
     Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
disc     1.00      0.00     1.00     1.00   NA       NA       NA

Draws were sampled using sample(hmc). For each parameter, Bulk_ESS
and Tail_ESS are effective sample size measures, and Rhat is the potential
scale reduction factor on split chains (at convergence, Rhat = 1).

exp(fixef(m_MF_ERall_final))

                    Estimate Est.Error        Q2.5       Q97.5
Intercept[1]       0.1077715  2.234582  0.02364088   0.5401388
Intercept[2]       1.7561832  2.233495  0.38598455   8.7348848
Intercept[3]      16.4657789  2.236316  3.59473671  81.5750944
Intercept[4]     130.8846175  2.246065 28.25305943 664.2177284
Prev_ER_1_Dis      1.2464986  1.094230  1.04313873   1.4855482
Prev_ER_2_Rum      1.0001527  1.155319  0.75437111   1.3277029
Prev_ER_3_SBl      0.8703192  1.181915  0.62906334   1.2126560
Prev_ER_4_ExprS    1.0167212  1.146084  0.77816290   1.3261894
Prev_ER_5_ExperS   0.9731368  1.132780  0.76325162   1.2382399
Prev_ER_6_Acc      0.9679639  1.096678  0.80643015   1.1610194
Prev_ER_7_Pla      0.9422250  1.114462  0.76038610   1.1630442
Prev_ER_8_Rea      1.0254967  1.161234  0.76545184   1.3748057
Prev_ER_9_ESu      0.9121158  1.147927  0.69617383   1.1859206
Prev_ER_10_Rel     0.9181839  1.105161  0.75426233   1.1184754
T1_Gender          0.7152226  1.386806  0.38468103   1.3686339
Age_2024           0.9872387  1.028187  0.93718647   1.0439572
Prev_Str_Overall   1.1190080  1.018026  1.08025537   1.1593230
Prev_MF_Within     2.5704377  1.037262  2.39244098   2.7623251

pd_m_MF_ERall_final <- pd(m_MF_ERall_final)
pd_m_MF_ERall_final

Probability of Direction

Parameter        |     pd
-------------------------
Intercept[1]     | 99.58%
Intercept[2]     | 75.17%
Intercept[3]     | 99.96%
Intercept[4]     |   100%
Prev_ER_1_Dis    | 99.22%
Prev_ER_2_Rum    | 50.28%
Prev_ER_3_SBl    | 80.04%
Prev_ER_4_ExprS  | 54.80%
Prev_ER_5_ExperS | 58.74%
Prev_ER_6_Acc    | 64.03%
Prev_ER_7_Pla    | 70.86%
Prev_ER_8_Rea    | 57.05%
Prev_ER_9_ESu    | 74.25%
Prev_ER_10_Rel   | 80.44%
T1_Gender        | 84.27%
Age_2024         | 68.18%
Prev_Str_Overall |   100%
Prev_MF_Within   |   100%

p_values <- describe_posterior(m_MF_ERall_final, test = "p_direction")
p_values

Summary of Posterior Distribution

Parameter        |    Median |         95% CI |     pd |  Rhat |      ESS
-------------------------------------------------------------------------
Intercept[1]     |     -2.24 | [-3.74, -0.62] | 99.58% | 1.001 |   978.00
Intercept[2]     |      0.55 | [-0.95,  2.17] | 75.17% | 1.001 |   976.00
Intercept[3]     |      2.79 | [ 1.28,  4.40] | 99.96% | 1.001 |   979.00
Intercept[4]     |      4.85 | [ 3.34,  6.50] |   100% | 1.002 |   989.00
Prev_ER_1_Dis    |      0.22 | [ 0.04,  0.40] | 99.22% | 1.000 | 18387.00
Prev_ER_2_Rum    | -7.05e-04 | [-0.28,  0.28] | 50.28% | 1.000 | 20498.00
Prev_ER_3_SBl    |     -0.14 | [-0.46,  0.19] | 80.04% | 1.000 | 23628.00
Prev_ER_4_ExprS  |      0.02 | [-0.25,  0.28] | 54.80% | 1.000 | 21857.00
Prev_ER_5_ExperS |     -0.03 | [-0.27,  0.21] | 58.74% | 1.000 | 19949.00
Prev_ER_6_Acc    |     -0.03 | [-0.22,  0.15] | 64.03% | 1.000 | 21239.00
Prev_ER_7_Pla    |     -0.06 | [-0.27,  0.15] | 70.86% | 1.000 | 19727.00
Prev_ER_8_Rea    |      0.03 | [-0.27,  0.32] | 57.05% | 1.000 | 22617.00
Prev_ER_9_ESu    |     -0.09 | [-0.36,  0.17] | 74.25% | 1.000 | 21477.00
Prev_ER_10_Rel   |     -0.09 | [-0.28,  0.11] | 80.44% | 1.000 | 20628.00
T1_Gender        |     -0.34 | [-0.96,  0.31] | 84.27% | 1.005 |  1022.00
Age_2024         |     -0.01 | [-0.06,  0.04] | 68.18% | 1.002 |   975.00
Prev_Str_Overall |      0.11 | [ 0.08,  0.15] |   100% | 1.000 | 10557.00
Prev_MF_Within   |      0.94 | [ 0.87,  1.02] |   100% | 1.000 | 18441.00

#To explore whether emotion regulation strategies predict subsequent stress
# if (rerun) {
#   start_time <- Sys.time()
#   print(start_time)
#   m_Stress_ERall_Cov4 <- brm(Str_Overall ~ Prev_ER_1_Dis + Prev_ER_2_Rum + Prev_ER_3_SBl + Prev_ER_4_ExprS
#                              + Prev_ER_5_ExperS + Prev_ER_6_Acc + Prev_ER_7_Pla + Prev_ER_8_Rea
#                              + Prev_ER_9_ESu + Prev_ER_10_Rel + T1_Gender + Age_2024 + Prev_Str_Overall_Within
#                              + Prev_MF + (1 | PID),
#                              family = cumulative(link = "logit", threshold = "flexible"), data = d,
#                              backend = "cmdstanr", chains = 4, iter = 4000, warmup = 1000, cores = 4)
#   saveRDS(m_Stress_ERall_Cov4, file = "m_Stress_ERall_Cov4.RDS")
#   end_time <- Sys.time()
#   print(end_time)
# ]

m_Stress_ERall_Cov4 <- readRDS("m_Stress_ERall_Cov4.RDS")
summary(m_Stress_ERall_Cov4)

 Family: cumulative 
  Links: mu = logit; disc = identity 
Formula: Str_Overall ~ Prev_ER_1_Dis + Prev_ER_2_Rum + Prev_ER_3_SBl + Prev_ER_4_ExprS + Prev_ER_5_ExperS + Prev_ER_6_Acc + Prev_ER_7_Pla + Prev_ER_8_Rea + Prev_ER_9_ESu + Prev_ER_10_Rel + T1_Gender + Age_2024 + Prev_Str_Overall_Within + Prev_MF + (1 | PID) 
   Data: d (Number of observations: 6497) 
  Draws: 4 chains, each with iter = 4000; warmup = 1000; thin = 1;
         total post-warmup draws = 12000

Multilevel Hyperparameters:
~PID (Number of levels: 179) 
              Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
sd(Intercept)     2.37      0.15     2.10     2.68 1.00      815     2105

Regression Coefficients:
                        Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS
Intercept[1]               -1.66      1.00    -3.65     0.31 1.01      385
Intercept[2]               -0.03      1.00    -2.02     1.94 1.01      384
Intercept[3]                1.14      1.00    -0.86     3.10 1.01      385
Intercept[4]                2.07      1.00     0.08     4.05 1.01      384
Intercept[5]                2.86      1.00     0.87     4.84 1.01      384
Intercept[6]                3.72      1.00     1.73     5.69 1.01      384
Intercept[7]                4.77      1.00     2.77     6.74 1.01      386
Intercept[8]                5.87      1.00     3.87     7.85 1.01      386
Intercept[9]                7.20      1.01     5.18     9.21 1.01      390
Intercept[10]               8.40      1.03     6.35    10.44 1.01      401
Prev_ER_1_Dis               0.32      0.09     0.15     0.49 1.00    13064
Prev_ER_2_Rum               0.00      0.14    -0.26     0.27 1.00    12522
Prev_ER_3_SBl               0.12      0.16    -0.19     0.43 1.00    12031
Prev_ER_4_ExprS            -0.08      0.13    -0.34     0.17 1.00    13359
Prev_ER_5_ExperS            0.05      0.12    -0.18     0.28 1.00    11997
Prev_ER_6_Acc               0.16      0.09    -0.01     0.34 1.00    11667
Prev_ER_7_Pla              -0.00      0.10    -0.20     0.19 1.00    11151
Prev_ER_8_Rea               0.12      0.14    -0.15     0.40 1.00    11467
Prev_ER_9_ESu              -0.22      0.13    -0.48     0.04 1.00    12480
Prev_ER_10_Rel             -0.03      0.10    -0.22     0.17 1.00    10607
T1_Gender                  -0.02      0.43    -0.84     0.86 1.00      508
Age_2024                   -0.01      0.03    -0.08     0.06 1.01      381
Prev_Str_Overall_Within     0.46      0.02     0.42     0.50 1.00     9775
Prev_MF                     0.15      0.03     0.08     0.21 1.00     9670
                        Tail_ESS
Intercept[1]                 846
Intercept[2]                 863
Intercept[3]                 870
Intercept[4]                 869
Intercept[5]                 863
Intercept[6]                 852
Intercept[7]                 853
Intercept[8]                 857
Intercept[9]                 901
Intercept[10]                915
Prev_ER_1_Dis               8987
Prev_ER_2_Rum               9330
Prev_ER_3_SBl               9193
Prev_ER_4_ExprS             9146
Prev_ER_5_ExperS            9297
Prev_ER_6_Acc               9807
Prev_ER_7_Pla               9485
Prev_ER_8_Rea               9391
Prev_ER_9_ESu               8858
Prev_ER_10_Rel              8994
T1_Gender                    905
Age_2024                     989
Prev_Str_Overall_Within     9292
Prev_MF                     9272

Further Distributional Parameters:
     Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
disc     1.00      0.00     1.00     1.00   NA       NA       NA

Draws were sampled using sample(hmc). For each parameter, Bulk_ESS
and Tail_ESS are effective sample size measures, and Rhat is the potential
scale reduction factor on split chains (at convergence, Rhat = 1).

exp(fixef(m_Stress_ERall_Cov4))

                            Estimate Est.Error         Q2.5        Q97.5
Intercept[1]               0.1906392  2.713800   0.02589783     1.357926
Intercept[2]               0.9750364  2.712367   0.13304076     6.958960
Intercept[3]               3.1139546  2.712335   0.42483224    22.299795
Intercept[4]               7.9371925  2.713295   1.07959015    57.263504
Intercept[5]              17.4859989  2.715265   2.39885071   126.889135
Intercept[6]              41.1411496  2.718678   5.61880215   296.743091
Intercept[7]             117.7164859  2.723224  15.90590223   847.333831
Intercept[8]             352.7551693  2.731498  48.11371396  2573.482966
Intercept[9]            1337.0944696  2.750682 177.52243584  9949.665883
Intercept[10]           4463.2599083  2.795430 572.50430211 34136.244931
Prev_ER_1_Dis              1.3731822  1.090688   1.15785441     1.631813
Prev_ER_2_Rum              1.0045341  1.145622   0.77270172     1.310132
Prev_ER_3_SBl              1.1316029  1.171697   0.82884329     1.537252
Prev_ER_4_ExprS            0.9189860  1.136380   0.71407957     1.179986
Prev_ER_5_ExperS           1.0472207  1.123800   0.83457224     1.317882
Prev_ER_6_Acc              1.1786678  1.090402   0.99318334     1.398441
Prev_ER_7_Pla              0.9978954  1.106135   0.81886014     1.213137
Prev_ER_8_Rea              1.1310848  1.152470   0.85716645     1.487176
Prev_ER_9_ESu              0.8033403  1.141034   0.62051298     1.043051
Prev_ER_10_Rel             0.9746181  1.102742   0.80537021     1.181752
T1_Gender                  0.9833126  1.541037   0.43021717     2.355603
Age_2024                   0.9902688  1.034757   0.92372686     1.057313
Prev_Str_Overall_Within    1.5818433  1.019082   1.52504604     1.641223
Prev_MF                    1.1609270  1.033124   1.08773882     1.236365

pd_m_Stress_ERall_Cov4 <- pd(m_Stress_ERall_Cov4)
pd_m_Stress_ERall_Cov4

Probability of Direction

Parameter               |     pd
--------------------------------
Intercept[1]            | 95.22%
Intercept[2]            | 51.66%
Intercept[3]            | 87.98%
Intercept[4]            | 97.92%
Intercept[5]            | 99.78%
Intercept[6]            | 99.99%
Intercept[7]            |   100%
Intercept[8]            |   100%
Intercept[9]            |   100%
Intercept[10]           |   100%
Prev_ER_1_Dis           | 99.98%
Prev_ER_2_Rum           | 51.31%
Prev_ER_3_SBl           | 78.03%
Prev_ER_4_ExprS         | 74.92%
Prev_ER_5_ExperS        | 65.58%
Prev_ER_6_Acc           | 97.03%
Prev_ER_7_Pla           | 51.02%
Prev_ER_8_Rea           | 80.73%
Prev_ER_9_ESu           | 95.07%
Prev_ER_10_Rel          | 60.39%
T1_Gender               | 52.33%
Age_2024                | 61.30%
Prev_Str_Overall_Within |   100%
Prev_MF                 |   100%

p_values_m_Stress_ERall_Cov4 <- describe_posterior(m_Stress_ERall_Cov4, test = "p_direction")
p_values_m_Stress_ERall_Cov4

Summary of Posterior Distribution

Parameter               |    Median |         95% CI |     pd |  Rhat |      ESS
--------------------------------------------------------------------------------
Intercept[1]            |     -1.67 | [-3.65,  0.31] | 95.22% | 1.012 |   387.00
Intercept[2]            |     -0.04 | [-2.02,  1.94] | 51.66% | 1.012 |   386.00
Intercept[3]            |      1.12 | [-0.86,  3.10] | 87.98% | 1.012 |   387.00
Intercept[4]            |      2.06 | [ 0.08,  4.05] | 97.92% | 1.012 |   387.00
Intercept[5]            |      2.85 | [ 0.87,  4.84] | 99.78% | 1.011 |   387.00
Intercept[6]            |      3.70 | [ 1.73,  5.69] | 99.99% | 1.012 |   387.00
Intercept[7]            |      4.75 | [ 2.77,  6.74] |   100% | 1.011 |   389.00
Intercept[8]            |      5.85 | [ 3.87,  7.85] |   100% | 1.012 |   389.00
Intercept[9]            |      7.19 | [ 5.18,  9.21] |   100% | 1.011 |   393.00
Intercept[10]           |      8.39 | [ 6.35, 10.44] |   100% | 1.011 |   405.00
Prev_ER_1_Dis           |      0.32 | [ 0.15,  0.49] | 99.98% | 1.000 | 13056.00
Prev_ER_2_Rum           |  4.61e-03 | [-0.26,  0.27] | 51.31% | 1.000 | 12497.00
Prev_ER_3_SBl           |      0.13 | [-0.19,  0.43] | 78.03% | 1.000 | 11983.00
Prev_ER_4_ExprS         |     -0.08 | [-0.34,  0.17] | 74.92% | 1.000 | 13351.00
Prev_ER_5_ExperS        |      0.05 | [-0.18,  0.28] | 65.58% | 1.000 | 11965.00
Prev_ER_6_Acc           |      0.17 | [-0.01,  0.34] | 97.03% | 1.000 | 11666.00
Prev_ER_7_Pla           | -2.62e-03 | [-0.20,  0.19] | 51.02% | 1.000 | 11102.00
Prev_ER_8_Rea           |      0.12 | [-0.15,  0.40] | 80.73% | 1.000 | 11401.00
Prev_ER_9_ESu           |     -0.22 | [-0.48,  0.04] | 95.07% | 1.000 | 12485.00
Prev_ER_10_Rel          |     -0.03 | [-0.22,  0.17] | 60.39% | 1.000 | 10512.00
T1_Gender               |     -0.03 | [-0.84,  0.86] | 52.33% | 1.004 |   504.00
Age_2024                | -9.38e-03 | [-0.08,  0.06] | 61.30% | 1.009 |   383.00
Prev_Str_Overall_Within |      0.46 | [ 0.42,  0.50] |   100% | 1.000 |  9783.00
Prev_MF                 |      0.15 | [ 0.08,  0.21] |   100% | 1.000 |  9645.00

2 * (1 - .9998)

[1] 4e-04

2 * (1 - .5131)

[1] 0.9738

2 * (1 - .7803)

[1] 0.4394

2 * (1 - .7492)

[1] 0.5016

2 * (1 - .6558)

[1] 0.6884

2 * (1 - .9703)

[1] 0.0594

2 * (1 - .5102)

[1] 0.9796

2 * (1 - .8073)

[1] 0.3854

2 * (1 - .9507)

[1] 0.0986

2 * (1 - .6039)

[1] 0.7922

2 * (1 - 1)

[1] 0

2 * (1 - .6130)

[1] 0.774

2 * (1 - .5233)

[1] 0.9534

5 AIM 2 - To explore whether mental fatigue and stress predicts subsequent emotion regulation strategies.

### m_ERall_MF_final ####
# rerun <- TRUE
# 
# if (rerun) {
#   Sys.time()
#   m_ERall_MF_final <- brm(mvbind(ER_1_Dis, ER_2_Rum, ER_3_SBl, ER_4_ExprS, 
#                                 ER_5_ExperS, ER_6_Acc, ER_7_Pla, ER_8_Rea,
#                                 ER_9_ESu, ER_10_Rel) ~ Prev_MF + T1_Gender + Age_2024 + 
#                            Prev_Str_Overall + (1 | p | PID),
#                          family = bernoulli(link = "logit"), data = d,
#                          backend = "cmdstanr", chains = 4, iter = 4000, warmup = 1000, cores = 4)
#   saveRDS(m_ERall_MF_final, file = "m_ERall_MF_final.RDS")
#   Sys.time()
# }

m_ERall_MF_final <- readRDS("m_ERall_MF_final.RDS")
summary(m_ERall_MF_final)

 Family: MV(bernoulli, bernoulli, bernoulli, bernoulli, bernoulli, bernoulli, bernoulli, bernoulli, bernoulli, bernoulli) 
  Links: mu = logit
         mu = logit
         mu = logit
         mu = logit
         mu = logit
         mu = logit
         mu = logit
         mu = logit
         mu = logit
         mu = logit 
Formula: ER_1_Dis ~ Prev_MF + T1_Gender + Age_2024 + Prev_Str_Overall + (1 | p | PID) 
         ER_2_Rum ~ Prev_MF + T1_Gender + Age_2024 + Prev_Str_Overall + (1 | p | PID) 
         ER_3_SBl ~ Prev_MF + T1_Gender + Age_2024 + Prev_Str_Overall + (1 | p | PID) 
         ER_4_ExprS ~ Prev_MF + T1_Gender + Age_2024 + Prev_Str_Overall + (1 | p | PID) 
         ER_5_ExperS ~ Prev_MF + T1_Gender + Age_2024 + Prev_Str_Overall + (1 | p | PID) 
         ER_6_Acc ~ Prev_MF + T1_Gender + Age_2024 + Prev_Str_Overall + (1 | p | PID) 
         ER_7_Pla ~ Prev_MF + T1_Gender + Age_2024 + Prev_Str_Overall + (1 | p | PID) 
         ER_8_Rea ~ Prev_MF + T1_Gender + Age_2024 + Prev_Str_Overall + (1 | p | PID) 
         ER_9_ESu ~ Prev_MF + T1_Gender + Age_2024 + Prev_Str_Overall + (1 | p | PID) 
         ER_10_Rel ~ Prev_MF + T1_Gender + Age_2024 + Prev_Str_Overall + (1 | p | PID) 
   Data: d (Number of observations: 6497) 
  Draws: 4 chains, each with iter = 4000; warmup = 1000; thin = 1;
         total post-warmup draws = 12000

Multilevel Hyperparameters:
~PID (Number of levels: 179) 
                                            Estimate Est.Error l-95% CI
sd(ER1Dis_Intercept)                            1.78      0.15     1.51
sd(ER2Rum_Intercept)                            1.49      0.18     1.18
sd(ER3SBl_Intercept)                            1.67      0.21     1.29
sd(ER4ExprS_Intercept)                          1.29      0.15     1.03
sd(ER5ExperS_Intercept)                         1.79      0.18     1.47
sd(ER6Acc_Intercept)                            1.35      0.12     1.15
sd(ER7Pla_Intercept)                            1.33      0.12     1.11
sd(ER8Rea_Intercept)                            1.56      0.19     1.22
sd(ER9ESu_Intercept)                            1.46      0.18     1.14
sd(ER10Rel_Intercept)                           1.68      0.15     1.42
cor(ER1Dis_Intercept,ER2Rum_Intercept)          0.29      0.11     0.06
cor(ER1Dis_Intercept,ER3SBl_Intercept)          0.25      0.11     0.01
cor(ER2Rum_Intercept,ER3SBl_Intercept)          0.74      0.09     0.55
cor(ER1Dis_Intercept,ER4ExprS_Intercept)        0.26      0.11     0.03
cor(ER2Rum_Intercept,ER4ExprS_Intercept)        0.33      0.12     0.09
cor(ER3SBl_Intercept,ER4ExprS_Intercept)        0.40      0.12     0.15
cor(ER1Dis_Intercept,ER5ExperS_Intercept)       0.38      0.10     0.17
cor(ER2Rum_Intercept,ER5ExperS_Intercept)       0.37      0.11     0.15
cor(ER3SBl_Intercept,ER5ExperS_Intercept)       0.43      0.11     0.21
cor(ER4ExprS_Intercept,ER5ExperS_Intercept)     0.85      0.06     0.72
cor(ER1Dis_Intercept,ER6Acc_Intercept)          0.25      0.10     0.05
cor(ER2Rum_Intercept,ER6Acc_Intercept)          0.40      0.10     0.19
cor(ER3SBl_Intercept,ER6Acc_Intercept)          0.35      0.11     0.13
cor(ER4ExprS_Intercept,ER6Acc_Intercept)        0.65      0.08     0.47
cor(ER5ExperS_Intercept,ER6Acc_Intercept)       0.52      0.09     0.34
cor(ER1Dis_Intercept,ER7Pla_Intercept)          0.38      0.09     0.18
cor(ER2Rum_Intercept,ER7Pla_Intercept)          0.50      0.10     0.29
cor(ER3SBl_Intercept,ER7Pla_Intercept)          0.39      0.11     0.17
cor(ER4ExprS_Intercept,ER7Pla_Intercept)        0.35      0.11     0.13
cor(ER5ExperS_Intercept,ER7Pla_Intercept)       0.31      0.10     0.10
cor(ER6Acc_Intercept,ER7Pla_Intercept)          0.74      0.06     0.60
cor(ER1Dis_Intercept,ER8Rea_Intercept)          0.07      0.12    -0.16
cor(ER2Rum_Intercept,ER8Rea_Intercept)          0.50      0.11     0.27
cor(ER3SBl_Intercept,ER8Rea_Intercept)          0.37      0.12     0.13
cor(ER4ExprS_Intercept,ER8Rea_Intercept)        0.31      0.12     0.06
cor(ER5ExperS_Intercept,ER8Rea_Intercept)       0.13      0.12    -0.11
cor(ER6Acc_Intercept,ER8Rea_Intercept)          0.57      0.09     0.37
cor(ER7Pla_Intercept,ER8Rea_Intercept)          0.64      0.09     0.45
cor(ER1Dis_Intercept,ER9ESu_Intercept)          0.24      0.11     0.01
cor(ER2Rum_Intercept,ER9ESu_Intercept)          0.54      0.11     0.31
cor(ER3SBl_Intercept,ER9ESu_Intercept)          0.49      0.12     0.24
cor(ER4ExprS_Intercept,ER9ESu_Intercept)        0.40      0.11     0.17
cor(ER5ExperS_Intercept,ER9ESu_Intercept)       0.29      0.11     0.05
cor(ER6Acc_Intercept,ER9ESu_Intercept)          0.64      0.09     0.45
cor(ER7Pla_Intercept,ER9ESu_Intercept)          0.55      0.10     0.34
cor(ER8Rea_Intercept,ER9ESu_Intercept)          0.62      0.10     0.41
cor(ER1Dis_Intercept,ER10Rel_Intercept)         0.51      0.09     0.33
cor(ER2Rum_Intercept,ER10Rel_Intercept)         0.26      0.11     0.04
cor(ER3SBl_Intercept,ER10Rel_Intercept)         0.22      0.11    -0.01
cor(ER4ExprS_Intercept,ER10Rel_Intercept)       0.12      0.11    -0.10
cor(ER5ExperS_Intercept,ER10Rel_Intercept)      0.24      0.10     0.04
cor(ER6Acc_Intercept,ER10Rel_Intercept)         0.32      0.09     0.13
cor(ER7Pla_Intercept,ER10Rel_Intercept)         0.42      0.09     0.24
cor(ER8Rea_Intercept,ER10Rel_Intercept)         0.23      0.11     0.01
cor(ER9ESu_Intercept,ER10Rel_Intercept)         0.40      0.10     0.19
                                            u-95% CI Rhat Bulk_ESS Tail_ESS
sd(ER1Dis_Intercept)                            2.10 1.00     2452     4848
sd(ER2Rum_Intercept)                            1.89 1.00     3301     5841
sd(ER3SBl_Intercept)                            2.13 1.00     4533     6840
sd(ER4ExprS_Intercept)                          1.60 1.00     4933     7212
sd(ER5ExperS_Intercept)                         2.19 1.00     5053     7451
sd(ER6Acc_Intercept)                            1.59 1.00     5724     7714
sd(ER7Pla_Intercept)                            1.58 1.00     5315     7921
sd(ER8Rea_Intercept)                            1.97 1.00     4809     6834
sd(ER9ESu_Intercept)                            1.84 1.00     5529     7066
sd(ER10Rel_Intercept)                           1.99 1.00     5390     7671
cor(ER1Dis_Intercept,ER2Rum_Intercept)          0.50 1.00     3972     7065
cor(ER1Dis_Intercept,ER3SBl_Intercept)          0.46 1.00     4715     7683
cor(ER2Rum_Intercept,ER3SBl_Intercept)          0.88 1.00     3061     6007
cor(ER1Dis_Intercept,ER4ExprS_Intercept)        0.47 1.00     3859     7182
cor(ER2Rum_Intercept,ER4ExprS_Intercept)        0.55 1.00     2173     5293
cor(ER3SBl_Intercept,ER4ExprS_Intercept)        0.62 1.00     2227     3919
cor(ER1Dis_Intercept,ER5ExperS_Intercept)       0.56 1.00     4071     6832
cor(ER2Rum_Intercept,ER5ExperS_Intercept)       0.57 1.00     2304     4772
cor(ER3SBl_Intercept,ER5ExperS_Intercept)       0.62 1.00     2329     4767
cor(ER4ExprS_Intercept,ER5ExperS_Intercept)     0.94 1.00     3129     6331
cor(ER1Dis_Intercept,ER6Acc_Intercept)          0.43 1.00     3489     6009
cor(ER2Rum_Intercept,ER6Acc_Intercept)          0.59 1.00     1950     4829
cor(ER3SBl_Intercept,ER6Acc_Intercept)          0.55 1.00     2423     4856
cor(ER4ExprS_Intercept,ER6Acc_Intercept)        0.79 1.00     2143     4205
cor(ER5ExperS_Intercept,ER6Acc_Intercept)       0.68 1.00     3063     6195
cor(ER1Dis_Intercept,ER7Pla_Intercept)          0.55 1.00     4834     6664
cor(ER2Rum_Intercept,ER7Pla_Intercept)          0.68 1.00     3073     5647
cor(ER3SBl_Intercept,ER7Pla_Intercept)          0.59 1.00     3611     6891
cor(ER4ExprS_Intercept,ER7Pla_Intercept)        0.56 1.00     2032     5240
cor(ER5ExperS_Intercept,ER7Pla_Intercept)       0.51 1.00     3421     6123
cor(ER6Acc_Intercept,ER7Pla_Intercept)          0.85 1.00     5745     8004
cor(ER1Dis_Intercept,ER8Rea_Intercept)          0.30 1.00     4643     6923
cor(ER2Rum_Intercept,ER8Rea_Intercept)          0.69 1.00     3466     5494
cor(ER3SBl_Intercept,ER8Rea_Intercept)          0.59 1.00     4039     7162
cor(ER4ExprS_Intercept,ER8Rea_Intercept)        0.53 1.00     3120     6208
cor(ER5ExperS_Intercept,ER8Rea_Intercept)       0.36 1.00     5046     7902
cor(ER6Acc_Intercept,ER8Rea_Intercept)          0.73 1.00     7035     8908
cor(ER7Pla_Intercept,ER8Rea_Intercept)          0.80 1.00     6355     8847
cor(ER1Dis_Intercept,ER9ESu_Intercept)          0.45 1.00     4920     7770
cor(ER2Rum_Intercept,ER9ESu_Intercept)          0.74 1.00     2516     5695
cor(ER3SBl_Intercept,ER9ESu_Intercept)          0.71 1.00     2966     6159
cor(ER4ExprS_Intercept,ER9ESu_Intercept)        0.61 1.00     2422     5178
cor(ER5ExperS_Intercept,ER9ESu_Intercept)       0.50 1.00     3254     7011
cor(ER6Acc_Intercept,ER9ESu_Intercept)          0.79 1.00     6055     8560
cor(ER7Pla_Intercept,ER9ESu_Intercept)          0.73 1.00     6817     9061
cor(ER8Rea_Intercept,ER9ESu_Intercept)          0.80 1.00     5231     8965
cor(ER1Dis_Intercept,ER10Rel_Intercept)         0.66 1.00     5127     7671
cor(ER2Rum_Intercept,ER10Rel_Intercept)         0.46 1.00     3359     6271
cor(ER3SBl_Intercept,ER10Rel_Intercept)         0.44 1.00     3335     5308
cor(ER4ExprS_Intercept,ER10Rel_Intercept)       0.33 1.00     2461     5524
cor(ER5ExperS_Intercept,ER10Rel_Intercept)      0.43 1.00     4031     7473
cor(ER6Acc_Intercept,ER10Rel_Intercept)         0.50 1.00     5000     7808
cor(ER7Pla_Intercept,ER10Rel_Intercept)         0.59 1.00     5275     8309
cor(ER8Rea_Intercept,ER10Rel_Intercept)         0.45 1.00     5101     8049
cor(ER9ESu_Intercept,ER10Rel_Intercept)         0.59 1.00     4970     8848

Regression Coefficients:
                           Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS
ER1Dis_Intercept              -5.61      0.91    -7.44    -3.88 1.00     1836
ER2Rum_Intercept              -5.61      0.94    -7.51    -3.83 1.00     3657
ER3SBl_Intercept              -5.34      1.09    -7.53    -3.24 1.00     4434
ER4ExprS_Intercept            -5.70      0.85    -7.40    -4.04 1.00     4163
ER5ExperS_Intercept           -5.33      1.05    -7.45    -3.30 1.00     3830
ER6Acc_Intercept              -5.05      0.73    -6.53    -3.65 1.00     3937
ER7Pla_Intercept              -3.99      0.74    -5.45    -2.55 1.00     4015
ER8Rea_Intercept              -7.10      1.05    -9.22    -5.08 1.00     6328
ER9ESu_Intercept              -9.30      1.07   -11.49    -7.32 1.00     5224
ER10Rel_Intercept             -4.01      0.87    -5.73    -2.34 1.00     4248
ER1Dis_Prev_MF                 0.12      0.06     0.01     0.24 1.00    13114
ER1Dis_T1_Gender               0.55      0.38    -0.19     1.31 1.00     2054
ER1Dis_Age_2024                0.07      0.03     0.01     0.13 1.00     1672
ER1Dis_Prev_Str_Overall        0.12      0.03     0.07     0.18 1.00    11107
ER2Rum_Prev_MF                 0.04      0.08    -0.12     0.20 1.00    11610
ER2Rum_T1_Gender              -0.01      0.38    -0.75     0.73 1.00     2951
ER2Rum_Age_2024                0.03      0.03    -0.03     0.09 1.00     3298
ER2Rum_Prev_Str_Overall        0.16      0.04     0.09     0.23 1.00    10232
ER3SBl_Prev_MF                 0.11      0.09    -0.07     0.29 1.00    14517
ER3SBl_T1_Gender               0.02      0.44    -0.83     0.90 1.00     4174
ER3SBl_Age_2024               -0.00      0.04    -0.08     0.07 1.00     4094
ER3SBl_Prev_Str_Overall        0.15      0.04     0.06     0.23 1.00    13836
ER4ExprS_Prev_MF              -0.01      0.09    -0.17     0.16 1.00    11201
ER4ExprS_T1_Gender             0.18      0.33    -0.45     0.84 1.00     4121
ER4ExprS_Age_2024              0.04      0.03    -0.01     0.10 1.00     3424
ER4ExprS_Prev_Str_Overall      0.13      0.04     0.06     0.20 1.00    11921
ER5ExperS_Prev_MF             -0.04      0.08    -0.20     0.12 1.00    14698
ER5ExperS_T1_Gender            0.21      0.42    -0.60     1.05 1.00     3774
ER5ExperS_Age_2024             0.03      0.03    -0.04     0.10 1.00     3320
ER5ExperS_Prev_Str_Overall     0.13      0.04     0.06     0.19 1.00    13298
ER6Acc_Prev_MF                 0.06      0.06    -0.05     0.17 1.00    13013
ER6Acc_T1_Gender               0.55      0.31    -0.04     1.18 1.00     3723
ER6Acc_Age_2024                0.05      0.02     0.00     0.10 1.00     3212
ER6Acc_Prev_Str_Overall        0.09      0.03     0.04     0.14 1.00    14364
ER7Pla_Prev_MF                -0.06      0.06    -0.19     0.06 1.00    14248
ER7Pla_T1_Gender               0.95      0.32     0.33     1.59 1.00     4192
ER7Pla_Age_2024               -0.00      0.02    -0.05     0.04 1.00     3507
ER7Pla_Prev_Str_Overall        0.10      0.03     0.05     0.16 1.00    13841
ER8Rea_Prev_MF                 0.14      0.09    -0.03     0.31 1.00    13808
ER8Rea_T1_Gender               0.74      0.44    -0.10     1.62 1.00     5234
ER8Rea_Age_2024                0.05      0.03    -0.02     0.11 1.00     5618
ER8Rea_Prev_Str_Overall        0.14      0.04     0.07     0.21 1.00    14683
ER9ESu_Prev_MF                 0.10      0.08    -0.06     0.25 1.00    14653
ER9ESu_T1_Gender               2.73      0.56     1.70     3.92 1.00     6130
ER9ESu_Age_2024                0.08      0.03     0.02     0.14 1.00     4479
ER9ESu_Prev_Str_Overall        0.14      0.03     0.07     0.21 1.00    12940
ER10Rel_Prev_MF                0.04      0.06    -0.08     0.15 1.00    15243
ER10Rel_T1_Gender              0.90      0.38     0.16     1.65 1.00     5218
ER10Rel_Age_2024               0.00      0.03    -0.06     0.06 1.00     3655
ER10Rel_Prev_Str_Overall       0.00      0.03    -0.05     0.06 1.00    14691
                           Tail_ESS
ER1Dis_Intercept               3975
ER2Rum_Intercept               6371
ER3SBl_Intercept               7438
ER4ExprS_Intercept             6573
ER5ExperS_Intercept            6472
ER6Acc_Intercept               6337
ER7Pla_Intercept               6816
ER8Rea_Intercept               7806
ER9ESu_Intercept               7226
ER10Rel_Intercept              6858
ER1Dis_Prev_MF                 9049
ER1Dis_T1_Gender               3640
ER1Dis_Age_2024                3316
ER1Dis_Prev_Str_Overall        9791
ER2Rum_Prev_MF                 9043
ER2Rum_T1_Gender               5512
ER2Rum_Age_2024                6130
ER2Rum_Prev_Str_Overall        8811
ER3SBl_Prev_MF                 9507
ER3SBl_T1_Gender               6230
ER3SBl_Age_2024                7154
ER3SBl_Prev_Str_Overall        9883
ER4ExprS_Prev_MF               9024
ER4ExprS_T1_Gender             6369
ER4ExprS_Age_2024              5760
ER4ExprS_Prev_Str_Overall      9657
ER5ExperS_Prev_MF              9594
ER5ExperS_T1_Gender            6419
ER5ExperS_Age_2024             6675
ER5ExperS_Prev_Str_Overall     9352
ER6Acc_Prev_MF                 9433
ER6Acc_T1_Gender               6137
ER6Acc_Age_2024                5707
ER6Acc_Prev_Str_Overall        9686
ER7Pla_Prev_MF                 9007
ER7Pla_T1_Gender               6869
ER7Pla_Age_2024                5993
ER7Pla_Prev_Str_Overall        9064
ER8Rea_Prev_MF                 9268
ER8Rea_T1_Gender               7481
ER8Rea_Age_2024                8026
ER8Rea_Prev_Str_Overall        9022
ER9ESu_Prev_MF                 9970
ER9ESu_T1_Gender               8087
ER9ESu_Age_2024                7123
ER9ESu_Prev_Str_Overall        9439
ER10Rel_Prev_MF                9011
ER10Rel_T1_Gender              8400
ER10Rel_Age_2024               5467
ER10Rel_Prev_Str_Overall       9094

Draws were sampled using sample(hmc). For each parameter, Bulk_ESS
and Tail_ESS are effective sample size measures, and Rhat is the potential
scale reduction factor on split chains (at convergence, Rhat = 1).

exp(fixef(m_ERall_MF_final))

                               Estimate Est.Error         Q2.5        Q97.5
ER1Dis_Intercept           3.671720e-03  2.494540 5.861404e-04  0.020632645
ER2Rum_Intercept           3.643317e-03  2.547230 5.484249e-04  0.021730163
ER3SBl_Intercept           4.804764e-03  2.960249 5.361283e-04  0.039088470
ER4ExprS_Intercept         3.360571e-03  2.337829 6.108029e-04  0.017555697
ER5ExperS_Intercept        4.835116e-03  2.864004 5.812409e-04  0.036849923
ER6Acc_Intercept           6.415892e-03  2.084019 1.453590e-03  0.025891314
ER7Pla_Intercept           1.845073e-02  2.097105 4.284232e-03  0.078308157
ER8Rea_Intercept           8.274161e-04  2.858473 9.859530e-05  0.006210709
ER9ESu_Intercept           9.186679e-05  2.909548 1.027743e-05  0.000664380
ER10Rel_Intercept          1.809933e-02  2.377032 3.261757e-03  0.096412685
ER1Dis_Prev_MF             1.129918e+00  1.059497 1.007933e+00  1.266399614
ER1Dis_T1_Gender           1.733249e+00  1.463526 8.230252e-01  3.690944916
ER1Dis_Age_2024            1.067436e+00  1.031461 1.005817e+00  1.135732282
ER1Dis_Prev_Str_Overall    1.129127e+00  1.027759 1.071343e+00  1.193193916
ER2Rum_Prev_MF             1.044098e+00  1.085025 8.889293e-01  1.223360922
ER2Rum_T1_Gender           9.886757e-01  1.458399 4.704537e-01  2.072548066
ER2Rum_Age_2024            1.029240e+00  1.031669 9.682303e-01  1.094386552
ER2Rum_Prev_Str_Overall    1.172874e+00  1.038021 1.091530e+00  1.263608100
ER3SBl_Prev_MF             1.118621e+00  1.096265 9.354676e-01  1.338184942
ER3SBl_T1_Gender           1.015516e+00  1.550876 4.360145e-01  2.470856063
ER3SBl_Age_2024            9.950697e-01  1.036882 9.268623e-01  1.067915350
ER3SBl_Prev_Str_Overall    1.157217e+00  1.042268 1.066097e+00  1.254479192
ER4ExprS_Prev_MF           9.914345e-01  1.088931 8.397460e-01  1.169089599
ER4ExprS_T1_Gender         1.200208e+00  1.391070 6.345372e-01  2.312329193
ER4ExprS_Age_2024          1.042812e+00  1.028034 9.884845e-01  1.102046626
ER4ExprS_Prev_Str_Overall  1.138843e+00  1.037375 1.059132e+00  1.222907078
ER5ExperS_Prev_MF          9.637183e-01  1.082887 8.208991e-01  1.122015223
ER5ExperS_T1_Gender        1.230206e+00  1.524018 5.488920e-01  2.855188170
ER5ExperS_Age_2024         1.029679e+00  1.035068 9.613934e-01  1.100986480
ER5ExperS_Prev_Str_Overall 1.133216e+00  1.035664 1.058317e+00  1.214615602
ER6Acc_Prev_MF             1.065568e+00  1.057009 9.557907e-01  1.186737743
ER6Acc_T1_Gender           1.737821e+00  1.360482 9.567846e-01  3.251518929
ER6Acc_Age_2024            1.051879e+00  1.024735 1.003249e+00  1.104549056
ER6Acc_Prev_Str_Overall    1.095516e+00  1.025564 1.043726e+00  1.151119072
ER7Pla_Prev_MF             9.377798e-01  1.065495 8.269581e-01  1.062626746
ER7Pla_T1_Gender           2.576640e+00  1.379587 1.393222e+00  4.896779529
ER7Pla_Age_2024            9.960906e-01  1.025227 9.483235e-01  1.045886154
ER7Pla_Prev_Str_Overall    1.107646e+00  1.028229 1.048023e+00  1.168684229
ER8Rea_Prev_MF             1.148201e+00  1.091317 9.659313e-01  1.362188980
ER8Rea_T1_Gender           2.089193e+00  1.554042 9.006625e-01  5.041160180
ER8Rea_Age_2024            1.048697e+00  1.034192 9.829576e-01  1.121001100
ER8Rea_Prev_Str_Overall    1.148666e+00  1.037839 1.067768e+00  1.234535147
ER9ESu_Prev_MF             1.101335e+00  1.082101 9.415149e-01  1.282514142
ER9ESu_T1_Gender           1.540540e+01  1.752128 5.487610e+00 50.270189634
ER9ESu_Age_2024            1.081260e+00  1.031472 1.019010e+00  1.150583178
ER9ESu_Prev_Str_Overall    1.150053e+00  1.035546 1.074069e+00  1.232057486
ER10Rel_Prev_MF            1.035893e+00  1.060878 9.227662e-01  1.163505173
ER10Rel_T1_Gender          2.450247e+00  1.460682 1.173114e+00  5.191281065
ER10Rel_Age_2024           1.001208e+00  1.030417 9.442338e-01  1.060828660
ER10Rel_Prev_Str_Overall   1.003757e+00  1.029630 9.468763e-01  1.062032173

pd_m_ERall_MF_final <- pd(m_ERall_MF_final)
pd_m_ERall_MF_final

Probability of Direction () (ER1Dis)

Parameter        | Response |     pd
------------------------------------
(Intercept)      |   ER1Dis |   100%
Prev_MF          |   ER1Dis | 98.23%
T1_Gender        |   ER1Dis | 92.55%
Age_2024         |   ER1Dis | 98.32%
Prev_Str_Overall |   ER1Dis |   100%

# Fixed effects () (ER2Rum)

Parameter        | Response |     pd
------------------------------------
(Intercept)      |   ER2Rum |   100%
Prev_MF          |   ER2Rum | 70.22%
T1_Gender        |   ER2Rum | 51.17%
Age_2024         |   ER2Rum | 82.38%
Prev_Str_Overall |   ER2Rum |   100%

# Fixed effects () (ER3SBl)

Parameter        | Response |     pd
------------------------------------
(Intercept)      |   ER3SBl |   100%
Prev_MF          |   ER3SBl | 88.59%
T1_Gender        |   ER3SBl | 51.07%
Age_2024         |   ER3SBl | 55.18%
Prev_Str_Overall |   ER3SBl | 99.99%

# Fixed effects () (ER4ExprS)

Parameter        | Response |     pd
------------------------------------
(Intercept)      | ER4ExprS |   100%
Prev_MF          | ER4ExprS | 53.67%
T1_Gender        | ER4ExprS | 70.95%
Age_2024         | ER4ExprS | 93.47%
Prev_Str_Overall | ER4ExprS | 99.97%

# Fixed effects () (ER5ExperS)

Parameter        |  Response |     pd
-------------------------------------
(Intercept)      | ER5ExperS |   100%
Prev_MF          | ER5ExperS | 67.86%
T1_Gender        | ER5ExperS | 68.77%
Age_2024         | ER5ExperS | 80.67%
Prev_Str_Overall | ER5ExperS | 99.99%

# Fixed effects () (ER6Acc)

Parameter        | Response |     pd
------------------------------------
(Intercept)      |   ER6Acc |   100%
Prev_MF          |   ER6Acc | 87.33%
T1_Gender        |   ER6Acc | 96.46%
Age_2024         |   ER6Acc | 98.17%
Prev_Str_Overall |   ER6Acc | 99.99%

# Fixed effects () (ER7Pla)

Parameter        | Response |     pd
------------------------------------
(Intercept)      |   ER7Pla |   100%
Prev_MF          |   ER7Pla | 84.74%
T1_Gender        |   ER7Pla | 99.87%
Age_2024         |   ER7Pla | 56.49%
Prev_Str_Overall |   ER7Pla | 99.97%

# Fixed effects () (ER8Rea)

Parameter        | Response |     pd
------------------------------------
(Intercept)      |   ER8Rea |   100%
Prev_MF          |   ER8Rea | 94.06%
T1_Gender        |   ER8Rea | 95.66%
Age_2024         |   ER8Rea | 92.23%
Prev_Str_Overall |   ER8Rea |   100%

# Fixed effects () (ER9ESu)

Parameter        | Response |     pd
------------------------------------
(Intercept)      |   ER9ESu |   100%
Prev_MF          |   ER9ESu | 88.97%
T1_Gender        |   ER9ESu |   100%
Age_2024         |   ER9ESu | 99.51%
Prev_Str_Overall |   ER9ESu | 99.99%

# Fixed effects () (ER10Rel)

Parameter        | Response |     pd
------------------------------------
(Intercept)      |  ER10Rel |   100%
Prev_MF          |  ER10Rel | 72.85%
T1_Gender        |  ER10Rel | 99.17%
Age_2024         |  ER10Rel | 51.68%
Prev_Str_Overall |  ER10Rel | 54.99%

p_values <- describe_posterior(m_MF_ERall_final, test = "p_direction")
p_values

Summary of Posterior Distribution

Parameter        |    Median |         95% CI |     pd |  Rhat |      ESS
-------------------------------------------------------------------------
Intercept[1]     |     -2.24 | [-3.74, -0.62] | 99.58% | 1.001 |   978.00
Intercept[2]     |      0.55 | [-0.95,  2.17] | 75.17% | 1.001 |   976.00
Intercept[3]     |      2.79 | [ 1.28,  4.40] | 99.96% | 1.001 |   979.00
Intercept[4]     |      4.85 | [ 3.34,  6.50] |   100% | 1.002 |   989.00
Prev_ER_1_Dis    |      0.22 | [ 0.04,  0.40] | 99.22% | 1.000 | 18387.00
Prev_ER_2_Rum    | -7.05e-04 | [-0.28,  0.28] | 50.28% | 1.000 | 20498.00
Prev_ER_3_SBl    |     -0.14 | [-0.46,  0.19] | 80.04% | 1.000 | 23628.00
Prev_ER_4_ExprS  |      0.02 | [-0.25,  0.28] | 54.80% | 1.000 | 21857.00
Prev_ER_5_ExperS |     -0.03 | [-0.27,  0.21] | 58.74% | 1.000 | 19949.00
Prev_ER_6_Acc    |     -0.03 | [-0.22,  0.15] | 64.03% | 1.000 | 21239.00
Prev_ER_7_Pla    |     -0.06 | [-0.27,  0.15] | 70.86% | 1.000 | 19727.00
Prev_ER_8_Rea    |      0.03 | [-0.27,  0.32] | 57.05% | 1.000 | 22617.00
Prev_ER_9_ESu    |     -0.09 | [-0.36,  0.17] | 74.25% | 1.000 | 21477.00
Prev_ER_10_Rel   |     -0.09 | [-0.28,  0.11] | 80.44% | 1.000 | 20628.00
T1_Gender        |     -0.34 | [-0.96,  0.31] | 84.27% | 1.005 |  1022.00
Age_2024         |     -0.01 | [-0.06,  0.04] | 68.18% | 1.002 |   975.00
Prev_Str_Overall |      0.11 | [ 0.08,  0.15] |   100% | 1.000 | 10557.00
Prev_MF_Within   |      0.94 | [ 0.87,  1.02] |   100% | 1.000 | 18441.00

2 * (1 - 1)

[1] 0

2 * (1 - .9922)

[1] 0.0156

#### m_ERall_MF_Adj_final ####
#Model above adjusted for prior ER strategy levels 


# if (rerun) {
#   Sys.time()
#   m_ERall_MF_Adj_final <- brm(
#   bf(ER_1_Dis ~ Prev_ER_1_Dis_Within + Prev_Str_Overall + Prev_MF + T1_Gender + 
#        Age_2024 + (1|p|PID)) +
#   bf(ER_2_Rum ~ Prev_ER_2_Rum_Within + Prev_Str_Overall + Prev_MF + T1_Gender + 
#        Age_2024 + (1|p|PID)) +
#   bf(ER_3_SBl ~ Prev_ER_3_SBl_Within + Prev_Str_Overall + Prev_MF + T1_Gender + 
#         Age_2024 + (1|p|PID)) +
#   bf(ER_4_ExprS ~ Prev_ER_4_ExprS_Within + Prev_Str_Overall + Prev_MF + T1_Gender + 
#         Age_2024 + (1|p|PID)) +
#   bf(ER_5_ExperS ~ Prev_ER_5_ExperS_Within + Prev_Str_Overall + Prev_MF + T1_Gender + 
#         Age_2024 + (1|p|PID)) +
#   bf(ER_6_Acc ~ Prev_ER_6_Acc_Within + Prev_Str_Overall + Prev_MF + T1_Gender + 
#         Age_2024 + (1|p|PID)) +
#   bf(ER_7_Pla ~ Prev_ER_7_Pla_Within + Prev_Str_Overall + Prev_MF + T1_Gender + 
#         Age_2024 + (1|p|PID)) +
#   bf(ER_8_Rea ~ Prev_ER_8_Rea_Within + Prev_Str_Overall + Prev_MF + T1_Gender + 
#         Age_2024 + (1|p|PID)) +
#   bf(ER_9_ESu ~ Prev_ER_9_ESu_Within + Prev_Str_Overall + Prev_MF + T1_Gender + 
#         Age_2024 + (1|p|PID)) +
#   bf(ER_10_Rel ~ Prev_ER_10_Rel_Within + Prev_Str_Overall + Prev_MF + T1_Gender + 
#         Age_2024 + (1|p|PID)) +
#   set_rescor(TRUE), 
#   data = d,
#   backend = "cmdstanr", chains = 4, iter = 4000, warmup = 1000, cores = 4)
# saveRDS(m_ERall_MF_Adj_final, file = "m_ERall_MF_Adj_final.RDS")
# Sys.time()
# }

m_ERall_MF_Adj_final <- readRDS("m_ERall_MF_Adj_final.RDS")
summary(m_ERall_MF_Adj_final)

 Family: MV(gaussian, gaussian, gaussian, gaussian, gaussian, gaussian, gaussian, gaussian, gaussian, gaussian) 
  Links: mu = identity; sigma = identity
         mu = identity; sigma = identity
         mu = identity; sigma = identity
         mu = identity; sigma = identity
         mu = identity; sigma = identity
         mu = identity; sigma = identity
         mu = identity; sigma = identity
         mu = identity; sigma = identity
         mu = identity; sigma = identity
         mu = identity; sigma = identity 
Formula: ER_1_Dis ~ Prev_ER_1_Dis_Within + Prev_Str_Overall + Prev_MF + T1_Gender + Age_2024 + (1 | p | PID) 
         ER_2_Rum ~ Prev_ER_2_Rum_Within + Prev_Str_Overall + Prev_MF + T1_Gender + Age_2024 + (1 | p | PID) 
         ER_3_SBl ~ Prev_ER_3_SBl_Within + Prev_Str_Overall + Prev_MF + T1_Gender + Age_2024 + (1 | p | PID) 
         ER_4_ExprS ~ Prev_ER_4_ExprS_Within + Prev_Str_Overall + Prev_MF + T1_Gender + Age_2024 + (1 | p | PID) 
         ER_5_ExperS ~ Prev_ER_5_ExperS_Within + Prev_Str_Overall + Prev_MF + T1_Gender + Age_2024 + (1 | p | PID) 
         ER_6_Acc ~ Prev_ER_6_Acc_Within + Prev_Str_Overall + Prev_MF + T1_Gender + Age_2024 + (1 | p | PID) 
         ER_7_Pla ~ Prev_ER_7_Pla_Within + Prev_Str_Overall + Prev_MF + T1_Gender + Age_2024 + (1 | p | PID) 
         ER_8_Rea ~ Prev_ER_8_Rea_Within + Prev_Str_Overall + Prev_MF + T1_Gender + Age_2024 + (1 | p | PID) 
         ER_9_ESu ~ Prev_ER_9_ESu_Within + Prev_Str_Overall + Prev_MF + T1_Gender + Age_2024 + (1 | p | PID) 
         ER_10_Rel ~ Prev_ER_10_Rel_Within + Prev_Str_Overall + Prev_MF + T1_Gender + Age_2024 + (1 | p | PID) 
   Data: d (Number of observations: 6497) 
  Draws: 4 chains, each with iter = 4000; warmup = 1000; thin = 1;
         total post-warmup draws = 12000

Multilevel Hyperparameters:
~PID (Number of levels: 179) 
                                            Estimate Est.Error l-95% CI
sd(ER1Dis_Intercept)                            0.16      0.01     0.14
sd(ER2Rum_Intercept)                            0.05      0.00     0.04
sd(ER3SBl_Intercept)                            0.04      0.00     0.04
sd(ER4ExprS_Intercept)                          0.05      0.00     0.04
sd(ER5ExperS_Intercept)                         0.09      0.01     0.08
sd(ER6Acc_Intercept)                            0.11      0.01     0.09
sd(ER7Pla_Intercept)                            0.09      0.01     0.08
sd(ER8Rea_Intercept)                            0.06      0.00     0.05
sd(ER9ESu_Intercept)                            0.05      0.00     0.05
sd(ER10Rel_Intercept)                           0.13      0.01     0.12
cor(ER1Dis_Intercept,ER2Rum_Intercept)          0.25      0.09     0.07
cor(ER1Dis_Intercept,ER3SBl_Intercept)          0.17      0.10    -0.02
cor(ER2Rum_Intercept,ER3SBl_Intercept)          0.59      0.07     0.44
cor(ER1Dis_Intercept,ER4ExprS_Intercept)        0.26      0.09     0.08
cor(ER2Rum_Intercept,ER4ExprS_Intercept)        0.08      0.10    -0.12
cor(ER3SBl_Intercept,ER4ExprS_Intercept)        0.14      0.10    -0.06
cor(ER1Dis_Intercept,ER5ExperS_Intercept)       0.34      0.08     0.18
cor(ER2Rum_Intercept,ER5ExperS_Intercept)       0.05      0.09    -0.13
cor(ER3SBl_Intercept,ER5ExperS_Intercept)       0.11      0.09    -0.07
cor(ER4ExprS_Intercept,ER5ExperS_Intercept)     0.88      0.04     0.80
cor(ER1Dis_Intercept,ER6Acc_Intercept)          0.28      0.08     0.11
cor(ER2Rum_Intercept,ER6Acc_Intercept)          0.26      0.09     0.08
cor(ER3SBl_Intercept,ER6Acc_Intercept)          0.19      0.09    -0.00
cor(ER4ExprS_Intercept,ER6Acc_Intercept)        0.60      0.07     0.45
cor(ER5ExperS_Intercept,ER6Acc_Intercept)       0.46      0.07     0.31
cor(ER1Dis_Intercept,ER7Pla_Intercept)          0.29      0.08     0.12
cor(ER2Rum_Intercept,ER7Pla_Intercept)          0.36      0.09     0.18
cor(ER3SBl_Intercept,ER7Pla_Intercept)          0.29      0.09     0.11
cor(ER4ExprS_Intercept,ER7Pla_Intercept)        0.07      0.09    -0.12
cor(ER5ExperS_Intercept,ER7Pla_Intercept)       0.09      0.09    -0.07
cor(ER6Acc_Intercept,ER7Pla_Intercept)          0.52      0.07     0.37
cor(ER1Dis_Intercept,ER8Rea_Intercept)         -0.02      0.09    -0.20
cor(ER2Rum_Intercept,ER8Rea_Intercept)          0.17      0.09    -0.02
cor(ER3SBl_Intercept,ER8Rea_Intercept)          0.14      0.09    -0.04
cor(ER4ExprS_Intercept,ER8Rea_Intercept)        0.04      0.09    -0.14
cor(ER5ExperS_Intercept,ER8Rea_Intercept)      -0.06      0.09    -0.23
cor(ER6Acc_Intercept,ER8Rea_Intercept)          0.33      0.08     0.17
cor(ER7Pla_Intercept,ER8Rea_Intercept)          0.56      0.07     0.41
cor(ER1Dis_Intercept,ER9ESu_Intercept)          0.16      0.09    -0.03
cor(ER2Rum_Intercept,ER9ESu_Intercept)          0.43      0.09     0.25
cor(ER3SBl_Intercept,ER9ESu_Intercept)          0.21      0.10     0.02
cor(ER4ExprS_Intercept,ER9ESu_Intercept)        0.04      0.10    -0.15
cor(ER5ExperS_Intercept,ER9ESu_Intercept)      -0.04      0.09    -0.22
cor(ER6Acc_Intercept,ER9ESu_Intercept)          0.49      0.08     0.33
cor(ER7Pla_Intercept,ER9ESu_Intercept)          0.45      0.08     0.28
cor(ER8Rea_Intercept,ER9ESu_Intercept)          0.53      0.07     0.38
cor(ER1Dis_Intercept,ER10Rel_Intercept)         0.52      0.07     0.37
cor(ER2Rum_Intercept,ER10Rel_Intercept)         0.25      0.09     0.07
cor(ER3SBl_Intercept,ER10Rel_Intercept)         0.11      0.09    -0.07
cor(ER4ExprS_Intercept,ER10Rel_Intercept)       0.05      0.09    -0.13
cor(ER5ExperS_Intercept,ER10Rel_Intercept)      0.12      0.08    -0.04
cor(ER6Acc_Intercept,ER10Rel_Intercept)         0.29      0.08     0.13
cor(ER7Pla_Intercept,ER10Rel_Intercept)         0.29      0.08     0.12
cor(ER8Rea_Intercept,ER10Rel_Intercept)         0.18      0.08     0.02
cor(ER9ESu_Intercept,ER10Rel_Intercept)         0.36      0.08     0.19
                                            u-95% CI Rhat Bulk_ESS Tail_ESS
sd(ER1Dis_Intercept)                            0.19 1.00     3045     5256
sd(ER2Rum_Intercept)                            0.06 1.00     6009     8955
sd(ER3SBl_Intercept)                            0.05 1.00     6526     8959
sd(ER4ExprS_Intercept)                          0.05 1.00     5826     8264
sd(ER5ExperS_Intercept)                         0.10 1.00     5913     8274
sd(ER6Acc_Intercept)                            0.12 1.00     5391     8012
sd(ER7Pla_Intercept)                            0.10 1.00     6410     8767
sd(ER8Rea_Intercept)                            0.07 1.00     7443     8277
sd(ER9ESu_Intercept)                            0.06 1.00     8121     8836
sd(ER10Rel_Intercept)                           0.15 1.00     6883     8727
cor(ER1Dis_Intercept,ER2Rum_Intercept)          0.42 1.00     5217     8447
cor(ER1Dis_Intercept,ER3SBl_Intercept)          0.35 1.00     5947     7870
cor(ER2Rum_Intercept,ER3SBl_Intercept)          0.72 1.00     5252     7971
cor(ER1Dis_Intercept,ER4ExprS_Intercept)        0.43 1.00     4659     7827
cor(ER2Rum_Intercept,ER4ExprS_Intercept)        0.27 1.00     3790     6773
cor(ER3SBl_Intercept,ER4ExprS_Intercept)        0.33 1.00     3543     6869
cor(ER1Dis_Intercept,ER5ExperS_Intercept)       0.49 1.00     5588     7859
cor(ER2Rum_Intercept,ER5ExperS_Intercept)       0.23 1.00     4336     7131
cor(ER3SBl_Intercept,ER5ExperS_Intercept)       0.28 1.00     3580     6676
cor(ER4ExprS_Intercept,ER5ExperS_Intercept)     0.94 1.00     3286     5051
cor(ER1Dis_Intercept,ER6Acc_Intercept)          0.44 1.00     5579     7880
cor(ER2Rum_Intercept,ER6Acc_Intercept)          0.43 1.00     4398     7241
cor(ER3SBl_Intercept,ER6Acc_Intercept)          0.36 1.00     3734     6647
cor(ER4ExprS_Intercept,ER6Acc_Intercept)        0.72 1.00     3687     6264
cor(ER5ExperS_Intercept,ER6Acc_Intercept)       0.59 1.00     6362     8785
cor(ER1Dis_Intercept,ER7Pla_Intercept)          0.44 1.00     6230     8723
cor(ER2Rum_Intercept,ER7Pla_Intercept)          0.52 1.00     4609     7264
cor(ER3SBl_Intercept,ER7Pla_Intercept)          0.47 1.00     5319     7837
cor(ER4ExprS_Intercept,ER7Pla_Intercept)        0.25 1.00     4219     6701
cor(ER5ExperS_Intercept,ER7Pla_Intercept)       0.26 1.00     7592     9616
cor(ER6Acc_Intercept,ER7Pla_Intercept)          0.65 1.00     6742     8787
cor(ER1Dis_Intercept,ER8Rea_Intercept)          0.15 1.00     6428     8036
cor(ER2Rum_Intercept,ER8Rea_Intercept)          0.34 1.00     4936     6680
cor(ER3SBl_Intercept,ER8Rea_Intercept)          0.32 1.00     4835     6808
cor(ER4ExprS_Intercept,ER8Rea_Intercept)        0.22 1.00     4508     7524
cor(ER5ExperS_Intercept,ER8Rea_Intercept)       0.11 1.00     7656     8899
cor(ER6Acc_Intercept,ER8Rea_Intercept)          0.49 1.00     6057     8831
cor(ER7Pla_Intercept,ER8Rea_Intercept)          0.69 1.00     5548     8753
cor(ER1Dis_Intercept,ER9ESu_Intercept)          0.34 1.00     7013     7936
cor(ER2Rum_Intercept,ER9ESu_Intercept)          0.59 1.00     6007     8615
cor(ER3SBl_Intercept,ER9ESu_Intercept)          0.40 1.00     6220     8727
cor(ER4ExprS_Intercept,ER9ESu_Intercept)        0.23 1.00     4177     7099
cor(ER5ExperS_Intercept,ER9ESu_Intercept)       0.13 1.00     8232     9646
cor(ER6Acc_Intercept,ER9ESu_Intercept)          0.63 1.00     8147     9524
cor(ER7Pla_Intercept,ER9ESu_Intercept)          0.61 1.00     7441     8801
cor(ER8Rea_Intercept,ER9ESu_Intercept)          0.67 1.00     7566     8768
cor(ER1Dis_Intercept,ER10Rel_Intercept)         0.64 1.00     6464     8478
cor(ER2Rum_Intercept,ER10Rel_Intercept)         0.41 1.00     5517     7473
cor(ER3SBl_Intercept,ER10Rel_Intercept)         0.29 1.00     4725     7599
cor(ER4ExprS_Intercept,ER10Rel_Intercept)       0.22 1.00     3415     6052
cor(ER5ExperS_Intercept,ER10Rel_Intercept)      0.27 1.00     6119     8386
cor(ER6Acc_Intercept,ER10Rel_Intercept)         0.44 1.00     5472     8602
cor(ER7Pla_Intercept,ER10Rel_Intercept)         0.44 1.00     5347     8603
cor(ER8Rea_Intercept,ER10Rel_Intercept)         0.35 1.00     6370     8669
cor(ER9ESu_Intercept,ER10Rel_Intercept)         0.51 1.00     5009     7876

Regression Coefficients:
                                  Estimate Est.Error l-95% CI u-95% CI Rhat
ER1Dis_Intercept                     -0.11      0.07    -0.25     0.04 1.00
ER2Rum_Intercept                     -0.01      0.03    -0.06     0.05 1.00
ER3SBl_Intercept                      0.01      0.02    -0.03     0.06 1.00
ER4ExprS_Intercept                   -0.01      0.03    -0.06     0.04 1.00
ER5ExperS_Intercept                   0.01      0.04    -0.08     0.09 1.00
ER6Acc_Intercept                     -0.05      0.05    -0.16     0.05 1.00
ER7Pla_Intercept                      0.00      0.04    -0.08     0.09 1.00
ER8Rea_Intercept                     -0.06      0.03    -0.12    -0.00 1.00
ER9ESu_Intercept                     -0.10      0.03    -0.15    -0.04 1.00
ER10Rel_Intercept                    -0.01      0.06    -0.13     0.11 1.00
ER1Dis_Prev_ER_1_Dis_Within           0.20      0.01     0.18     0.23 1.00
ER1Dis_Prev_Str_Overall               0.01      0.00     0.00     0.01 1.00
ER1Dis_Prev_MF                        0.01      0.00     0.00     0.02 1.00
ER1Dis_T1_Gender                      0.03      0.03    -0.03     0.09 1.00
ER1Dis_Age_2024                       0.01      0.00     0.00     0.01 1.00
ER2Rum_Prev_ER_2_Rum_Within           0.12      0.01     0.10     0.14 1.00
ER2Rum_Prev_Str_Overall               0.00      0.00     0.00     0.01 1.00
ER2Rum_Prev_MF                        0.00      0.00    -0.00     0.01 1.00
ER2Rum_T1_Gender                     -0.00      0.01    -0.02     0.02 1.00
ER2Rum_Age_2024                       0.00      0.00    -0.00     0.00 1.00
ER3SBl_Prev_ER_3_SBl_Within           0.09      0.01     0.06     0.11 1.00
ER3SBl_Prev_Str_Overall               0.00      0.00     0.00     0.01 1.00
ER3SBl_Prev_MF                        0.00      0.00    -0.00     0.01 1.00
ER3SBl_T1_Gender                     -0.00      0.01    -0.02     0.01 1.00
ER3SBl_Age_2024                       0.00      0.00    -0.00     0.00 1.00
ER4ExprS_Prev_ER_4_ExprS_Within       0.11      0.01     0.09     0.13 1.00
ER4ExprS_Prev_Str_Overall             0.00      0.00     0.00     0.01 1.00
ER4ExprS_Prev_MF                     -0.00      0.00    -0.01     0.00 1.00
ER4ExprS_T1_Gender                    0.00      0.01    -0.02     0.02 1.00
ER4ExprS_Age_2024                     0.00      0.00    -0.00     0.00 1.00
ER5ExperS_Prev_ER_5_ExperS_Within     0.10      0.01     0.07     0.12 1.00
ER5ExperS_Prev_Str_Overall            0.00      0.00     0.00     0.01 1.00
ER5ExperS_Prev_MF                    -0.00      0.00    -0.01     0.00 1.00
ER5ExperS_T1_Gender                   0.00      0.02    -0.03     0.04 1.00
ER5ExperS_Age_2024                    0.00      0.00    -0.00     0.00 1.00
ER6Acc_Prev_ER_6_Acc_Within           0.07      0.01     0.04     0.09 1.00
ER6Acc_Prev_Str_Overall               0.01      0.00     0.00     0.01 1.00
ER6Acc_Prev_MF                        0.01      0.00    -0.00     0.01 1.00
ER6Acc_T1_Gender                      0.02      0.02    -0.02     0.07 1.00
ER6Acc_Age_2024                       0.00      0.00     0.00     0.01 1.00
ER7Pla_Prev_ER_7_Pla_Within           0.09      0.01     0.07     0.12 1.00
ER7Pla_Prev_Str_Overall               0.00      0.00     0.00     0.01 1.00
ER7Pla_Prev_MF                       -0.00      0.00    -0.01     0.00 1.00
ER7Pla_T1_Gender                      0.04      0.02     0.00     0.07 1.00
ER7Pla_Age_2024                       0.00      0.00    -0.00     0.00 1.00
ER8Rea_Prev_ER_8_Rea_Within           0.07      0.01     0.04     0.09 1.00
ER8Rea_Prev_Str_Overall               0.00      0.00     0.00     0.01 1.00
ER8Rea_Prev_MF                        0.00      0.00    -0.00     0.01 1.00
ER8Rea_T1_Gender                      0.02      0.01    -0.01     0.04 1.00
ER8Rea_Age_2024                       0.00      0.00     0.00     0.00 1.00
ER9ESu_Prev_ER_9_ESu_Within           0.05      0.01     0.02     0.07 1.00
ER9ESu_Prev_Str_Overall               0.01      0.00     0.00     0.01 1.00
ER9ESu_Prev_MF                        0.00      0.00    -0.00     0.01 1.00
ER9ESu_T1_Gender                      0.04      0.01     0.02     0.06 1.00
ER9ESu_Age_2024                       0.00      0.00     0.00     0.00 1.00
ER10Rel_Prev_ER_10_Rel_Within         0.13      0.01     0.10     0.15 1.00
ER10Rel_Prev_Str_Overall              0.00      0.00    -0.00     0.00 1.00
ER10Rel_Prev_MF                       0.00      0.00    -0.01     0.01 1.00
ER10Rel_T1_Gender                     0.05      0.03    -0.01     0.10 1.00
ER10Rel_Age_2024                      0.00      0.00    -0.00     0.01 1.00
                                  Bulk_ESS Tail_ESS
ER1Dis_Intercept                      2319     4388
ER2Rum_Intercept                      6051     8844
ER3SBl_Intercept                      9365    10108
ER4ExprS_Intercept                    6181     8353
ER5ExperS_Intercept                   6213     7955
ER6Acc_Intercept                      7365     8687
ER7Pla_Intercept                      8420     9250
ER8Rea_Intercept                      9873     9596
ER9ESu_Intercept                      9723    10495
ER10Rel_Intercept                     5398     8229
ER1Dis_Prev_ER_1_Dis_Within          29383     8882
ER1Dis_Prev_Str_Overall              14062    10538
ER1Dis_Prev_MF                       17849     9645
ER1Dis_T1_Gender                      2296     4217
ER1Dis_Age_2024                       2191     3872
ER2Rum_Prev_ER_2_Rum_Within          28595     8516
ER2Rum_Prev_Str_Overall              13734    10940
ER2Rum_Prev_MF                       16147    11036
ER2Rum_T1_Gender                      5234     7603
ER2Rum_Age_2024                       5963     7906
ER3SBl_Prev_ER_3_SBl_Within          29704     9349
ER3SBl_Prev_Str_Overall              15256    10704
ER3SBl_Prev_MF                       16675     9869
ER3SBl_T1_Gender                      7079     8602
ER3SBl_Age_2024                       8839     9238
ER4ExprS_Prev_ER_4_ExprS_Within      30068     8968
ER4ExprS_Prev_Str_Overall            13248    10417
ER4ExprS_Prev_MF                     15555    10486
ER4ExprS_T1_Gender                    4335     6890
ER4ExprS_Age_2024                     5900     8327
ER5ExperS_Prev_ER_5_ExperS_Within    27008     8636
ER5ExperS_Prev_Str_Overall           16944    10720
ER5ExperS_Prev_MF                    20773    10317
ER5ExperS_T1_Gender                   4275     6812
ER5ExperS_Age_2024                    5890     7310
ER6Acc_Prev_ER_6_Acc_Within          35956     8961
ER6Acc_Prev_Str_Overall              18543     9861
ER6Acc_Prev_MF                       22456    10748
ER6Acc_T1_Gender                      5114     7640
ER6Acc_Age_2024                       7087     8277
ER7Pla_Prev_ER_7_Pla_Within          30611     8948
ER7Pla_Prev_Str_Overall              16458    10532
ER7Pla_Prev_MF                       20410    10116
ER7Pla_T1_Gender                      5501     7126
ER7Pla_Age_2024                       8073     9414
ER8Rea_Prev_ER_8_Rea_Within          33045     8934
ER8Rea_Prev_Str_Overall              16583    11596
ER8Rea_Prev_MF                       18804    10785
ER8Rea_T1_Gender                      6729     8357
ER8Rea_Age_2024                       9757     9388
ER9ESu_Prev_ER_9_ESu_Within          29531     8737
ER9ESu_Prev_Str_Overall              16397    11274
ER9ESu_Prev_MF                       17413    10260
ER9ESu_T1_Gender                      6654     9018
ER9ESu_Age_2024                       9400     9291
ER10Rel_Prev_ER_10_Rel_Within        27240     8901
ER10Rel_Prev_Str_Overall             18680    10294
ER10Rel_Prev_MF                      23631     9061
ER10Rel_T1_Gender                     4394     7034
ER10Rel_Age_2024                      5330     8079

Further Distributional Parameters:
                Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
sigma_ER1Dis        0.28      0.00     0.27     0.28 1.00    28439     8558
sigma_ER2Rum        0.17      0.00     0.17     0.18 1.00    26703     9122
sigma_ER3SBl        0.15      0.00     0.15     0.16 1.00    25798     8409
sigma_ER4ExprS      0.17      0.00     0.17     0.18 1.00    24254     8805
sigma_ER5ExperS     0.20      0.00     0.19     0.20 1.00    29051     7970
sigma_ER6Acc        0.28      0.00     0.27     0.28 1.00    27990     8790
sigma_ER7Pla        0.24      0.00     0.24     0.25 1.00    27973     8512
sigma_ER8Rea        0.16      0.00     0.16     0.17 1.00    27806     9072
sigma_ER9ESu        0.19      0.00     0.18     0.19 1.00    29432     8140
sigma_ER10Rel       0.26      0.00     0.25     0.26 1.00    24787     8405

Residual Correlations: 
                           Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS
rescor(ER1Dis,ER2Rum)          0.08      0.01     0.05     0.10 1.00    25852
rescor(ER1Dis,ER3SBl)          0.02      0.01    -0.01     0.04 1.00    26914
rescor(ER2Rum,ER3SBl)          0.27      0.01     0.24     0.29 1.00    27712
rescor(ER1Dis,ER4ExprS)        0.03      0.01     0.00     0.05 1.00    27139
rescor(ER2Rum,ER4ExprS)        0.06      0.01     0.04     0.09 1.00    26645
rescor(ER3SBl,ER4ExprS)        0.06      0.01     0.04     0.09 1.00    28581
rescor(ER1Dis,ER5ExperS)       0.04      0.01     0.02     0.07 1.00    28126
rescor(ER2Rum,ER5ExperS)       0.07      0.01     0.04     0.09 1.00    26400
rescor(ER3SBl,ER5ExperS)       0.06      0.01     0.03     0.08 1.00    27076
rescor(ER4ExprS,ER5ExperS)     0.19      0.01     0.17     0.22 1.00    27906
rescor(ER1Dis,ER6Acc)          0.01      0.01    -0.01     0.04 1.00    25116
rescor(ER2Rum,ER6Acc)          0.06      0.01     0.03     0.08 1.00    24057
rescor(ER3SBl,ER6Acc)          0.03      0.01     0.01     0.06 1.00    24069
rescor(ER4ExprS,ER6Acc)        0.06      0.01     0.04     0.08 1.00    25286
rescor(ER5ExperS,ER6Acc)       0.03      0.01     0.01     0.06 1.00    26738
rescor(ER1Dis,ER7Pla)          0.01      0.01    -0.01     0.04 1.00    24191
rescor(ER2Rum,ER7Pla)          0.08      0.01     0.06     0.11 1.00    23235
rescor(ER3SBl,ER7Pla)          0.02      0.01    -0.01     0.04 1.00    24764
rescor(ER4ExprS,ER7Pla)       -0.00      0.01    -0.03     0.02 1.00    22974
rescor(ER5ExperS,ER7Pla)      -0.03      0.01    -0.05    -0.00 1.00    23764
rescor(ER6Acc,ER7Pla)          0.26      0.01     0.24     0.28 1.00    24672
rescor(ER1Dis,ER8Rea)          0.02      0.01    -0.00     0.04 1.00    28422
rescor(ER2Rum,ER8Rea)          0.09      0.01     0.07     0.12 1.00    25746
rescor(ER3SBl,ER8Rea)          0.01      0.01    -0.01     0.04 1.00    26595
rescor(ER4ExprS,ER8Rea)        0.03      0.01     0.00     0.05 1.00    25130
rescor(ER5ExperS,ER8Rea)      -0.00      0.01    -0.03     0.02 1.00    28498
rescor(ER6Acc,ER8Rea)          0.14      0.01     0.11     0.16 1.00    25092
rescor(ER7Pla,ER8Rea)          0.21      0.01     0.19     0.24 1.00    27071
rescor(ER1Dis,ER9ESu)          0.02      0.01    -0.00     0.04 1.00    26345
rescor(ER2Rum,ER9ESu)          0.05      0.01     0.03     0.07 1.00    27273
rescor(ER3SBl,ER9ESu)          0.06      0.01     0.04     0.08 1.00    25584
rescor(ER4ExprS,ER9ESu)       -0.02      0.01    -0.04     0.01 1.00    25257
rescor(ER5ExperS,ER9ESu)       0.01      0.01    -0.02     0.03 1.00    29065
rescor(ER6Acc,ER9ESu)          0.08      0.01     0.05     0.10 1.00    25560
rescor(ER7Pla,ER9ESu)          0.07      0.01     0.04     0.09 1.00    29360
rescor(ER8Rea,ER9ESu)          0.07      0.01     0.04     0.09 1.00    26755
rescor(ER1Dis,ER10Rel)         0.05      0.01     0.03     0.08 1.00    30200
rescor(ER2Rum,ER10Rel)        -0.01      0.01    -0.03     0.01 1.00    29237
rescor(ER3SBl,ER10Rel)        -0.03      0.01    -0.06    -0.01 1.00    29779
rescor(ER4ExprS,ER10Rel)      -0.02      0.01    -0.04     0.00 1.00    28512
rescor(ER5ExperS,ER10Rel)     -0.02      0.01    -0.04     0.00 1.00    27867
rescor(ER6Acc,ER10Rel)        -0.05      0.01    -0.08    -0.03 1.00    29918
rescor(ER7Pla,ER10Rel)        -0.04      0.01    -0.06    -0.01 1.00    29746
rescor(ER8Rea,ER10Rel)        -0.00      0.01    -0.03     0.02 1.00    28313
rescor(ER9ESu,ER10Rel)         0.06      0.01     0.03     0.08 1.00    29691
                           Tail_ESS
rescor(ER1Dis,ER2Rum)         10254
rescor(ER1Dis,ER3SBl)          9867
rescor(ER2Rum,ER3SBl)          9358
rescor(ER1Dis,ER4ExprS)        9216
rescor(ER2Rum,ER4ExprS)        8795
rescor(ER3SBl,ER4ExprS)        8761
rescor(ER1Dis,ER5ExperS)       8328
rescor(ER2Rum,ER5ExperS)       8826
rescor(ER3SBl,ER5ExperS)       8792
rescor(ER4ExprS,ER5ExperS)     9037
rescor(ER1Dis,ER6Acc)          9012
rescor(ER2Rum,ER6Acc)         10042
rescor(ER3SBl,ER6Acc)          8205
rescor(ER4ExprS,ER6Acc)        9283
rescor(ER5ExperS,ER6Acc)       9122
rescor(ER1Dis,ER7Pla)          8404
rescor(ER2Rum,ER7Pla)          9841
rescor(ER3SBl,ER7Pla)          9337
rescor(ER4ExprS,ER7Pla)        9299
rescor(ER5ExperS,ER7Pla)       8473
rescor(ER6Acc,ER7Pla)          9358
rescor(ER1Dis,ER8Rea)          8896
rescor(ER2Rum,ER8Rea)          9621
rescor(ER3SBl,ER8Rea)          8939
rescor(ER4ExprS,ER8Rea)        9010
rescor(ER5ExperS,ER8Rea)       9083
rescor(ER6Acc,ER8Rea)          9157
rescor(ER7Pla,ER8Rea)          8841
rescor(ER1Dis,ER9ESu)          8697
rescor(ER2Rum,ER9ESu)          9054
rescor(ER3SBl,ER9ESu)          8307
rescor(ER4ExprS,ER9ESu)        8864
rescor(ER5ExperS,ER9ESu)       8549
rescor(ER6Acc,ER9ESu)          8594
rescor(ER7Pla,ER9ESu)          9016
rescor(ER8Rea,ER9ESu)          9193
rescor(ER1Dis,ER10Rel)         9164
rescor(ER2Rum,ER10Rel)         8569
rescor(ER3SBl,ER10Rel)         7862
rescor(ER4ExprS,ER10Rel)       8674
rescor(ER5ExperS,ER10Rel)      8437
rescor(ER6Acc,ER10Rel)         8455
rescor(ER7Pla,ER10Rel)         7434
rescor(ER8Rea,ER10Rel)         8487
rescor(ER9ESu,ER10Rel)         8382

Draws were sampled using sample(hmc). For each parameter, Bulk_ESS
and Tail_ESS are effective sample size measures, and Rhat is the potential
scale reduction factor on split chains (at convergence, Rhat = 1).

exp(fixef(m_ERall_MF_Adj_final))

                                   Estimate Est.Error      Q2.5     Q97.5
ER1Dis_Intercept                  0.8992420  1.077585 0.7756240 1.0394690
ER2Rum_Intercept                  0.9941354  1.027384 0.9433051 1.0480465
ER3SBl_Intercept                  1.0122707  1.022865 0.9683419 1.0570585
ER4ExprS_Intercept                0.9873694  1.026115 0.9383705 1.0382901
ER5ExperS_Intercept               1.0065688  1.045287 0.9230657 1.0984627
ER6Acc_Intercept                  0.9473726  1.053570 0.8558346 1.0495192
ER7Pla_Intercept                  1.0049096  1.044389 0.9241188 1.0957123
ER8Rea_Intercept                  0.9392863  1.030342 0.8852659 0.9957395
ER9ESu_Intercept                  0.9074772  1.029403 0.8574511 0.9608170
ER10Rel_Intercept                 0.9883096  1.065320 0.8752663 1.1165302
ER1Dis_Prev_ER_1_Dis_Within       1.2251924  1.012591 1.1952577 1.2558519
ER1Dis_Prev_Str_Overall           1.0064945  1.002286 1.0020431 1.0110309
ER1Dis_Prev_MF                    1.0099665  1.004536 1.0010786 1.0189396
ER1Dis_T1_Gender                  1.0290394  1.030853 0.9694894 1.0911753
ER1Dis_Age_2024                   1.0064367  1.002573 1.0014367 1.0115401
ER2Rum_Prev_ER_2_Rum_Within       1.1257318  1.011448 1.1009864 1.1509016
ER2Rum_Prev_Str_Overall           1.0050084  1.001337 1.0024036 1.0076557
ER2Rum_Prev_MF                    1.0010328  1.002727 0.9956119 1.0062084
ER2Rum_T1_Gender                  0.9986861  1.011023 0.9769681 1.0201787
ER2Rum_Age_2024                   1.0009525  1.000913 0.9991503 1.0027259
ER3SBl_Prev_ER_3_SBl_Within       1.0923173  1.012045 1.0670983 1.1183710
ER3SBl_Prev_Str_Overall           1.0029481  1.001178 1.0006128 1.0052990
ER3SBl_Prev_MF                    1.0029703  1.002403 0.9982357 1.0076819
ER3SBl_T1_Gender                  0.9959011  1.009310 0.9775958 1.0141037
ER3SBl_Age_2024                   1.0001213  1.000759 0.9986234 1.0016113
ER4ExprS_Prev_ER_4_ExprS_Within   1.1168687  1.011841 1.0914842 1.1426975
ER4ExprS_Prev_Str_Overall         1.0040375  1.001240 1.0016021 1.0064729
ER4ExprS_Prev_MF                  0.9991403  1.002608 0.9940783 1.0043644
ER4ExprS_T1_Gender                1.0022221  1.010380 0.9821165 1.0226920
ER4ExprS_Age_2024                 1.0014054  1.000864 0.9997156 1.0031003
ER5ExperS_Prev_ER_5_ExperS_Within 1.1010730  1.012185 1.0753128 1.1273890
ER5ExperS_Prev_Str_Overall        1.0043934  1.001569 1.0013183 1.0075198
ER5ExperS_Prev_MF                 0.9985289  1.003199 0.9924072 1.0047985
ER5ExperS_T1_Gender               1.0012719  1.018153 0.9668501 1.0374268
ER5ExperS_Age_2024                1.0013892  1.001522 0.9983920 1.0043581
ER6Acc_Prev_ER_6_Acc_Within       1.0695569  1.012267 1.0443586 1.0951534
ER6Acc_Prev_Str_Overall           1.0067533  1.002191 1.0024411 1.0110505
ER6Acc_Prev_MF                    1.0060301  1.004410 0.9974248 1.0147154
ER6Acc_T1_Gender                  1.0237757  1.021768 0.9813610 1.0679846
ER6Acc_Age_2024                   1.0039411  1.001786 1.0004220 1.0073993
ER7Pla_Prev_ER_7_Pla_Within       1.0984532  1.012065 1.0731967 1.1247970
ER7Pla_Prev_Str_Overall           1.0044129  1.001923 1.0006455 1.0081764
ER7Pla_Prev_MF                    0.9960952  1.003930 0.9885714 1.0038835
ER7Pla_T1_Gender                  1.0402070  1.018089 1.0042845 1.0769967
ER7Pla_Age_2024                   1.0012737  1.001485 0.9982832 1.0041805
ER8Rea_Prev_ER_8_Rea_Within       1.0707940  1.011902 1.0459966 1.0960886
ER8Rea_Prev_Str_Overall           1.0041611  1.001293 1.0016495 1.0067544
ER8Rea_Prev_MF                    1.0044357  1.002621 0.9992771 1.0096758
ER8Rea_T1_Gender                  1.0152620  1.012401 0.9911461 1.0399034
ER8Rea_Age_2024                   1.0022473  1.001020 1.0002413 1.0042738
ER9ESu_Prev_ER_9_ESu_Within       1.0495240  1.013116 1.0229909 1.0768118
ER9ESu_Prev_Str_Overall           1.0054827  1.001412 1.0027221 1.0082990
ER9ESu_Prev_MF                    1.0042468  1.002906 0.9984948 1.0099818
ER9ESu_T1_Gender                  1.0417322  1.011831 1.0179278 1.0658702
ER9ESu_Age_2024                   1.0030267  1.000980 1.0011037 1.0049725
ER10Rel_Prev_ER_10_Rel_Within     1.1385307  1.013041 1.1096002 1.1670749
ER10Rel_Prev_Str_Overall          1.0005075  1.002067 0.9964883 1.0045280
ER10Rel_Prev_MF                   1.0026011  1.004182 0.9942939 1.0107339
ER10Rel_T1_Gender                 1.0463302  1.026713 0.9935189 1.1015170
ER10Rel_Age_2024                  1.0023813  1.002173 0.9981818 1.0066967

pd_m_ERall_MF_Adj_final <- pd(m_ERall_MF_Adj_final)
pd_m_ERall_MF_Adj_final

Probability of Direction () (ER1Dis)

Parameter            | Response |     pd
----------------------------------------
(Intercept)          |   ER1Dis | 92.54%
Prev_ER_1_Dis_Within |   ER1Dis |   100%
Prev_Str_Overall     |   ER1Dis | 99.78%
Prev_MF              |   ER1Dis | 98.53%
T1_Gender            |   ER1Dis | 82.48%
Age_2024             |   ER1Dis | 99.41%

# Fixed effects () (ER2Rum)

Parameter            | Response |     pd
----------------------------------------
(Intercept)          |   ER2Rum | 58.65%
Prev_ER_2_Rum_Within |   ER2Rum |   100%
Prev_Str_Overall     |   ER2Rum | 99.99%
Prev_MF              |   ER2Rum | 65.12%
T1_Gender            |   ER2Rum | 54.21%
Age_2024             |   ER2Rum | 85.13%

# Fixed effects () (ER3SBl)

Parameter            | Response |     pd
----------------------------------------
(Intercept)          |   ER3SBl | 70.33%
Prev_ER_3_SBl_Within |   ER3SBl |   100%
Prev_Str_Overall     |   ER3SBl | 99.41%
Prev_MF              |   ER3SBl | 89.15%
T1_Gender            |   ER3SBl | 66.97%
Age_2024             |   ER3SBl | 56.51%

# Fixed effects () (ER4ExprS)

Parameter              | Response |     pd
------------------------------------------
(Intercept)            | ER4ExprS | 69.24%
Prev_ER_4_ExprS_Within | ER4ExprS |   100%
Prev_Str_Overall       | ER4ExprS | 99.96%
Prev_MF                | ER4ExprS | 62.95%
T1_Gender              | ER4ExprS | 58.14%
Age_2024               | ER4ExprS | 94.77%

# Fixed effects () (ER5ExperS)

Parameter               |  Response |     pd
--------------------------------------------
(Intercept)             | ER5ExperS | 55.44%
Prev_ER_5_ExperS_Within | ER5ExperS |   100%
Prev_Str_Overall        | ER5ExperS | 99.76%
Prev_MF                 | ER5ExperS | 67.83%
T1_Gender               | ER5ExperS | 52.50%
Age_2024                | ER5ExperS | 81.98%

# Fixed effects () (ER6Acc)

Parameter            | Response |     pd
----------------------------------------
(Intercept)          |   ER6Acc | 84.99%
Prev_ER_6_Acc_Within |   ER6Acc |   100%
Prev_Str_Overall     |   ER6Acc | 99.95%
Prev_MF              |   ER6Acc | 91.25%
T1_Gender            |   ER6Acc | 86.28%
Age_2024             |   ER6Acc | 98.57%

# Fixed effects () (ER7Pla)

Parameter            | Response |     pd
----------------------------------------
(Intercept)          |   ER7Pla | 54.23%
Prev_ER_7_Pla_Within |   ER7Pla |   100%
Prev_Str_Overall     |   ER7Pla | 98.79%
Prev_MF              |   ER7Pla | 83.90%
T1_Gender            |   ER7Pla | 98.62%
Age_2024             |   ER7Pla | 80.60%

# Fixed effects () (ER8Rea)

Parameter            | Response |     pd
----------------------------------------
(Intercept)          |   ER8Rea | 98.12%
Prev_ER_8_Rea_Within |   ER8Rea |   100%
Prev_Str_Overall     |   ER8Rea | 99.91%
Prev_MF              |   ER8Rea | 95.47%
T1_Gender            |   ER8Rea | 89.29%
Age_2024             |   ER8Rea | 98.66%

# Fixed effects () (ER9ESu)

Parameter            | Response |     pd
----------------------------------------
(Intercept)          |   ER9ESu | 99.96%
Prev_ER_9_ESu_Within |   ER9ESu |   100%
Prev_Str_Overall     |   ER9ESu |   100%
Prev_MF              |   ER9ESu | 92.84%
T1_Gender            |   ER9ESu | 99.96%
Age_2024             |   ER9ESu | 99.91%

# Fixed effects () (ER10Rel)

Parameter             | Response |     pd
-----------------------------------------
(Intercept)           |  ER10Rel | 56.95%
Prev_ER_10_Rel_Within |  ER10Rel |   100%
Prev_Str_Overall      |  ER10Rel | 60.02%
Prev_MF               |  ER10Rel | 73.31%
T1_Gender             |  ER10Rel | 95.67%
Age_2024              |  ER10Rel | 86.48%

p_values_m_ERall_MF_Adj_final <- describe_posterior(m_ERall_MF_Adj_final, test = "p_direction")

Warning: Multivariate response models are not yet supported for tests `rope` and
  `p_rope`.

p_values_m_ERall_MF_Adj_final

Summary of Posterior Distribution () (ER1Dis)

Parameter            | Response |   Median |         95% CI |     pd |  Rhat |      ESS
---------------------------------------------------------------------------------------
(Intercept)          |   ER1Dis |    -0.11 | [-0.25,  0.04] | 92.54% | 1.001 |  2305.00
Prev_ER_1_Dis_Within |   ER1Dis |     0.20 | [ 0.18,  0.23] |   100% | 1.000 | 29149.00
Prev_Str_Overall     |   ER1Dis | 6.44e-03 | [ 0.00,  0.01] | 99.78% | 1.000 | 14027.00
Prev_MF              |   ER1Dis | 9.91e-03 | [ 0.00,  0.02] | 98.53% | 1.000 | 17784.00
T1_Gender            |   ER1Dis |     0.03 | [-0.03,  0.09] | 82.48% | 1.000 |  2304.00
Age_2024             |   ER1Dis | 6.42e-03 | [ 0.00,  0.01] | 99.41% | 1.001 |  2176.00

# Fixed effects () (ER2Rum)

Parameter            | Response |    Median |         95% CI |     pd |  Rhat |      ESS
----------------------------------------------------------------------------------------
(Intercept)          |   ER2Rum | -6.04e-03 | [-0.06,  0.05] | 58.65% | 1.001 |  6051.00
Prev_ER_2_Rum_Within |   ER2Rum |      0.12 | [ 0.10,  0.14] |   100% | 1.000 | 28381.00
Prev_Str_Overall     |   ER2Rum |  5.00e-03 | [ 0.00,  0.01] | 99.99% | 1.000 | 13702.00
Prev_MF              |   ER2Rum |  1.05e-03 | [ 0.00,  0.01] | 65.12% | 1.000 | 16102.00
T1_Gender            |   ER2Rum | -1.19e-03 | [-0.02,  0.02] | 54.21% | 1.000 |  5216.00
Age_2024             |   ER2Rum |  9.60e-04 | [ 0.00,  0.00] | 85.13% | 1.001 |  5895.00

# Fixed effects () (ER3SBl)

Parameter            | Response |    Median |         95% CI |     pd |  Rhat |      ESS
----------------------------------------------------------------------------------------
(Intercept)          |   ER3SBl |      0.01 | [-0.03,  0.06] | 70.33% | 1.000 |  9395.00
Prev_ER_3_SBl_Within |   ER3SBl |      0.09 | [ 0.06,  0.11] |   100% | 1.000 | 29409.00
Prev_Str_Overall     |   ER3SBl |  2.94e-03 | [ 0.00,  0.01] | 99.41% | 1.000 | 15240.00
Prev_MF              |   ER3SBl |  2.98e-03 | [ 0.00,  0.01] | 89.15% | 1.000 | 16708.00
T1_Gender            |   ER3SBl | -3.99e-03 | [-0.02,  0.01] | 66.97% | 1.000 |  7053.00
Age_2024             |   ER3SBl |  1.26e-04 | [ 0.00,  0.00] | 56.51% | 1.000 |  8846.00

# Fixed effects () (ER4ExprS)

Parameter              | Response |    Median |         95% CI |     pd |  Rhat |      ESS
------------------------------------------------------------------------------------------
(Intercept)            | ER4ExprS |     -0.01 | [-0.06,  0.04] | 69.24% | 1.001 |  6162.00
Prev_ER_4_ExprS_Within | ER4ExprS |      0.11 | [ 0.09,  0.13] |   100% | 1.000 | 31198.00
Prev_Str_Overall       | ER4ExprS |  4.02e-03 | [ 0.00,  0.01] | 99.96% | 1.000 | 13209.00
Prev_MF                | ER4ExprS | -8.72e-04 | [-0.01,  0.00] | 62.95% | 1.000 | 15574.00
T1_Gender              | ER4ExprS |  2.20e-03 | [-0.02,  0.02] | 58.14% | 1.000 |  4301.00
Age_2024               | ER4ExprS |  1.40e-03 | [ 0.00,  0.00] | 94.77% | 1.000 |  5876.00

# Fixed effects () (ER5ExperS)

Parameter               |  Response |    Median |         95% CI |     pd
-------------------------------------------------------------------------
(Intercept)             | ER5ExperS |  5.73e-03 | [-0.08,  0.09] | 55.44%
Prev_ER_5_ExperS_Within | ER5ExperS |      0.10 | [ 0.07,  0.12] |   100%
Prev_Str_Overall        | ER5ExperS |  4.37e-03 | [ 0.00,  0.01] | 99.76%
Prev_MF                 | ER5ExperS | -1.44e-03 | [-0.01,  0.00] | 67.83%
T1_Gender               | ER5ExperS |  1.05e-03 | [-0.03,  0.04] | 52.50%
Age_2024                | ER5ExperS |  1.41e-03 | [ 0.00,  0.00] | 81.98%

Parameter               |  Rhat |      ESS
------------------------------------------
(Intercept)             | 1.000 |  6211.00
Prev_ER_5_ExperS_Within | 1.000 | 26736.00
Prev_Str_Overall        | 1.000 | 16898.00
Prev_MF                 | 1.000 | 20392.00
T1_Gender               | 1.000 |  4253.00
Age_2024                | 1.000 |  5867.00

# Fixed effects () (ER6Acc)

Parameter            | Response |   Median |         95% CI |     pd |  Rhat |      ESS
---------------------------------------------------------------------------------------
(Intercept)          |   ER6Acc |    -0.06 | [-0.16,  0.05] | 84.99% | 1.000 |  7343.00
Prev_ER_6_Acc_Within |   ER6Acc |     0.07 | [ 0.04,  0.09] |   100% | 1.000 | 35562.00
Prev_Str_Overall     |   ER6Acc | 6.72e-03 | [ 0.00,  0.01] | 99.95% | 1.000 | 18531.00
Prev_MF              |   ER6Acc | 5.96e-03 | [ 0.00,  0.01] | 91.25% | 1.000 | 22383.00
T1_Gender            |   ER6Acc |     0.02 | [-0.02,  0.07] | 86.28% | 1.000 |  5098.00
Age_2024             |   ER6Acc | 3.96e-03 | [ 0.00,  0.01] | 98.57% | 1.000 |  7065.00

# Fixed effects () (ER7Pla)

Parameter            | Response |    Median |         95% CI |     pd |  Rhat |      ESS
----------------------------------------------------------------------------------------
(Intercept)          |   ER7Pla |  4.52e-03 | [-0.08,  0.09] | 54.23% | 1.000 |  8403.00
Prev_ER_7_Pla_Within |   ER7Pla |      0.09 | [ 0.07,  0.12] |   100% | 1.000 | 30114.00
Prev_Str_Overall     |   ER7Pla |  4.40e-03 | [ 0.00,  0.01] | 98.79% | 1.000 | 16347.00
Prev_MF              |   ER7Pla | -3.96e-03 | [-0.01,  0.00] | 83.90% | 1.000 | 20273.00
T1_Gender            |   ER7Pla |      0.04 | [ 0.00,  0.07] | 98.62% | 1.000 |  5492.00
Age_2024             |   ER7Pla |  1.27e-03 | [ 0.00,  0.00] | 80.60% | 1.000 |  8050.00

# Fixed effects () (ER8Rea)

Parameter            | Response |   Median |         95% CI |     pd |  Rhat |      ESS
---------------------------------------------------------------------------------------
(Intercept)          |   ER8Rea |    -0.06 | [-0.12,  0.00] | 98.12% | 1.000 |  9781.00
Prev_ER_8_Rea_Within |   ER8Rea |     0.07 | [ 0.04,  0.09] |   100% | 1.000 | 33946.00
Prev_Str_Overall     |   ER8Rea | 4.15e-03 | [ 0.00,  0.01] | 99.91% | 1.000 | 16460.00
Prev_MF              |   ER8Rea | 4.42e-03 | [ 0.00,  0.01] | 95.47% | 1.000 | 18665.00
T1_Gender            |   ER8Rea |     0.02 | [-0.01,  0.04] | 89.29% | 1.000 |  6724.00
Age_2024             |   ER8Rea | 2.24e-03 | [ 0.00,  0.00] | 98.66% | 1.000 |  9705.00

# Fixed effects () (ER9ESu)

Parameter            | Response |   Median |         95% CI |     pd |  Rhat |      ESS
---------------------------------------------------------------------------------------
(Intercept)          |   ER9ESu |    -0.10 | [-0.15, -0.04] | 99.96% | 1.000 |  9621.00
Prev_ER_9_ESu_Within |   ER9ESu |     0.05 | [ 0.02,  0.07] |   100% | 1.000 | 30680.00
Prev_Str_Overall     |   ER9ESu | 5.46e-03 | [ 0.00,  0.01] |   100% | 1.000 | 16629.00
Prev_MF              |   ER9ESu | 4.23e-03 | [ 0.00,  0.01] | 92.84% | 1.000 | 17846.00
T1_Gender            |   ER9ESu |     0.04 | [ 0.02,  0.06] | 99.96% | 1.000 |  6654.00
Age_2024             |   ER9ESu | 3.03e-03 | [ 0.00,  0.00] | 99.91% | 1.000 |  9310.00

# Fixed effects () (ER10Rel)

Parameter             | Response |   Median |         95% CI |     pd |  Rhat |      ESS
----------------------------------------------------------------------------------------
(Intercept)           |  ER10Rel |    -0.01 | [-0.13,  0.11] | 56.95% | 1.000 |  5359.00
Prev_ER_10_Rel_Within |  ER10Rel |     0.13 | [ 0.10,  0.15] |   100% | 1.000 | 26751.00
Prev_Str_Overall      |  ER10Rel | 5.29e-04 | [ 0.00,  0.00] | 60.02% | 1.000 | 18555.00
Prev_MF               |  ER10Rel | 2.65e-03 | [-0.01,  0.01] | 73.31% | 1.000 | 23351.00
T1_Gender             |  ER10Rel |     0.05 | [-0.01,  0.10] | 95.67% | 1.001 |  4379.00
Age_2024              |  ER10Rel | 2.35e-03 | [ 0.00,  0.01] | 86.48% | 1.000 |  5300.00

2* (1 -.9843)

[1] 0.0314

2* (1 -.6526)

[1] 0.6948

2* (1 - .8873)

[1] 0.2254

2* (1 - .6315)

[1] 0.737

2* (1 - .6727)

[1] 0.6546

2* (1 - .9103)

[1] 0.1794

2* (1 - .8356)

[1] 0.3288

2* (1 - .9539)

[1] 0.0922

2* (1 - .9212)

[1] 0.1576

2* (1 - .7335)

[1] 0.533

2* (1 -.9973)

[1] 0.0054

2* (1 -.9998)

[1] 4e-04

2* (1 - .9928)

[1] 0.0144

2* (1 - .9990)

[1] 0.002

2* (1 - .9972)

[1] 0.0056

2* (1 - .9989)

[1] 0.0022

2* (1 - .9882)

[1] 0.0236

2* (1 - .9998)

[1] 4e-04

2* (1 - .9999)

[1] 2e-04

2* (1 - .6031)

[1] 0.7938

2* (1 -.9853)

[1] 0.0294

2* (1 -.6512)

[1] 0.6976

2* (1 - .8915)

[1] 0.217

2* (1 - .6295)

[1] 0.741

2* (1 - .6783)

[1] 0.6434

2* (1 - .9125)

[1] 0.175

2* (1 - .8390)

[1] 0.322

2* (1 - .9547)

[1] 0.0906

2* (1 - .9284)

[1] 0.1432

2* (1 - .7313)

[1] 0.5374

2* (1 -.9978)

[1] 0.0044

2* (1 -.9999)

[1] 2e-04

2* (1 - .9941)

[1] 0.0118

2* (1 - .9996)

[1] 8e-04

2* (1 - .9976)

[1] 0.0048

2* (1 - .9995)

[1] 0.001

2* (1 - .9879)

[1] 0.0242

2* (1 - .9991)

[1] 0.0018

2* (1 - 1)

[1] 0

2* (1 - .6002)

[1] 0.7996

6 Aim 3 - To investigate whether the number of emotion regulation strategies implemented predicts mental fatigue.

# Monotonic effect of ER strategy count on mental fatigue
d <- d %>%
  group_by(PID) %>%
  mutate(Prev_ER_count_within = Prev_ER_count - mean(Prev_ER_count, na.rm = TRUE)) %>%
  ungroup()

table(d$Prev_ER_count_within)  # check unique values

  -2.19047619047619   -1.86904761904762   -1.76190476190476   -1.73809523809524 
                 14                  10                  11                  39 
  -1.70238095238095                -1.5   -1.46428571428571   -1.39285714285714 
                 23                   4                  30                  32 
  -1.28571428571429   -1.19047619047619   -1.16666666666667    -1.0952380952381 
                 25                  21                  31                  41 
  -1.08333333333333   -1.02380952380952                  -1  -0.952380952380952 
                 46                  79                  33                  39 
 -0.904761904761905  -0.869047619047619  -0.857142857142857  -0.845238095238095 
                 49                  54                  61                  41 
 -0.833333333333333  -0.761904761904762  -0.738095238095238  -0.702380952380952 
                 47                  61                   2                  74 
 -0.666666666666667  -0.630952380952381  -0.619047619047619  -0.607142857142857 
                 58                  46                 106                  97 
 -0.583333333333333   -0.55952380952381  -0.535714285714286  -0.511904761904762 
                 43                  53                 196                  64 
               -0.5  -0.488095238095238  -0.476190476190476  -0.464285714285714 
                202                  46                  53                  12 
 -0.452380952380952  -0.428571428571429  -0.404761904761905  -0.392857142857143 
                 72                 184                 182                 143 
 -0.380952380952381  -0.369047619047619  -0.345238095238095  -0.333333333333333 
                 53                  61                 195                 128 
 -0.321428571428571   -0.30952380952381  -0.297619047619048  -0.285714285714286 
                135                 259                  70                 232 
 -0.273809523809524  -0.261904761904762               -0.25  -0.238095238095238 
                 61                 137                 133                  73 
 -0.226190476190476  -0.214285714285714  -0.202380952380952  -0.190476190476191 
                205                  71                 504                  17 
  -0.19047619047619  -0.178571428571429  -0.166666666666667  -0.154761904761905 
                221                 365                 330                 221 
 -0.142857142857143  -0.130952380952381  -0.119047619047619  -0.107142857142857 
                224                 157                 307                 317 
-0.0952380952380953 -0.0952380952380952 -0.0833333333333333 -0.0714285714285714 
                 12                 397                 476                 399 
-0.0595238095238095 -0.0476190476190476 -0.0357142857142857 -0.0238095238095238 
                319                 730                 731                 657 
-0.0238095238095237 -0.0119047619047619                   0  0.0476190476190477 
                 49                 747                1034                  26 
 0.0952380952380952   0.130952380952381   0.142857142857143   0.154761904761905 
                 11                  65                   6                  19 
  0.166666666666667   0.238095238095238   0.261904761904762   0.297619047619048 
                 18                  63                   6                  31 
  0.333333333333333   0.369047619047619   0.380952380952381   0.392857142857143 
                  8                  30                  25                  45 
  0.416666666666667    0.44047619047619   0.464285714285714   0.488095238095238 
                 33                  17                 100                   9 
                0.5   0.511904761904762   0.523809523809524   0.535714285714286 
                100                  36                  22                  20 
  0.547619047619048   0.571428571428571   0.595238095238095   0.607142857142857 
                  4                  42                  57                  36 
  0.619047619047619   0.630952380952381   0.654761904761905   0.666666666666667 
                 30                  16                  34                  28 
  0.678571428571429    0.69047619047619   0.702380952380952   0.714285714285714 
                 16                  60                   7                  49 
  0.726190476190476   0.738095238095238                0.75   0.761904761904762 
                 23                  20                  30                   4 
  0.773809523809524   0.785714285714286   0.797619047619048   0.809523809523809 
                 41                  11                  60                  13 
   0.80952380952381   0.821428571428571   0.833333333333333   0.845238095238095 
                 18                  44                  32                  25 
  0.857142857142857   0.869047619047619   0.880952380952381   0.892857142857143 
                 22                   3                  20                   8 
  0.904761904761905   0.916666666666667   0.928571428571429    0.94047619047619 
                 34                  46                  14                  14 
  0.952380952380952   0.964285714285714   0.976190476190476   0.988095238095238 
                 19                  23                  29                   9 
                  1    1.04761904761905     1.0952380952381    1.13095238095238 
                 20                  10                  14                  29 
   1.14285714285714     1.1547619047619    1.16666666666667    1.23809523809524 
                  8                  20                  11                  25 
   1.26190476190476    1.29761904761905    1.33333333333333    1.36904761904762 
                 17                  20                  11                   5 
   1.38095238095238    1.39285714285714    1.41666666666667    1.44047619047619 
                 33                  22                   8                  12 
   1.46428571428571    1.48809523809524                 1.5    1.51190476190476 
                 40                   4                  22                   1 
   1.52380952380952    1.53571428571429    1.54761904761905    1.57142857142857 
                  9                  17                   1                  16 
    1.5952380952381    1.60714285714286    1.61904761904762    1.63095238095238 
                  5                  29                   1                   6 
    1.6547619047619    1.66666666666667    1.67857142857143    1.69047619047619 
                 18                   8                  14                   9 
   1.70238095238095    1.71428571428571    1.73809523809524                1.75 
                  3                  20                  10                   3 
   1.76190476190476    1.77380952380952    1.79761904761905    1.80952380952381 
                  5                   4                  16                  21 
   1.82142857142857    1.83333333333333     1.8452380952381    1.85714285714286 
                  5                  15                   4                   4 
   1.86904761904762    1.88095238095238    1.89285714285714     1.9047619047619 
                  5                   7                   5                  10 
   1.91666666666667    1.92857142857143    1.94047619047619    1.95238095238095 
                 14                   5                   3                   5 
   1.96428571428571    1.97619047619048                   2    2.04761904761905 
                  2                  14                   2                   5 
    2.0952380952381    2.13095238095238    2.14285714285714     2.1547619047619 
                  7                   8                   2                   4 
   2.16666666666667    2.23809523809524    2.26190476190476    2.29761904761905 
                  3                   7                  19                  12 
   2.33333333333333    2.36904761904762    2.38095238095238    2.39285714285714 
                  5                   2                   3                   3 
   2.44047619047619    2.48809523809524                 2.5    2.51190476190476 
                  2                   4                   5                   1 
   2.53571428571429    2.54761904761905    2.57142857142857     2.5952380952381 
                  5                   3                   6                   3 
   2.60714285714286    2.63095238095238     2.6547619047619    2.66666666666667 
                 11                   1                   3                   4 
   2.67857142857143    2.69047619047619    2.70238095238095    2.71428571428571 
                  2                   7                   4                   9 
               2.75    2.76190476190476    2.78571428571429    2.79761904761905 
                  2                   2                   1                   6 
   2.80952380952381    2.82142857142857    2.83333333333333     2.8452380952381 
                  6                   4                   9                   2 
   2.85714285714286    2.86904761904762    2.88095238095238    2.89285714285714 
                  2                   3                   2                   6 
    2.9047619047619    2.91666666666667    2.92857142857143    2.95238095238095 
                  7                   4                   2                   1 
   2.97619047619048                   3    3.04761904761905     3.0952380952381 
                  8                   3                   3                   1 
   3.13095238095238    3.16666666666667    3.26190476190476    3.29761904761905 
                  1                   4                   1                   6 
   3.33333333333333    3.38095238095238    3.39285714285714    3.48809523809524 
                  1                   1                   1                   1 
                3.5    3.54761904761905    3.57142857142857     3.5952380952381 
                  2                   1                   4                   3 
   3.60714285714286     3.6547619047619    3.67857142857143    3.73809523809524 
                  1                   2                   1                   1 
   3.77380952380952    3.78571428571429    3.79761904761905    3.80952380952381 
                  2                   1                   1                   3 
   3.82142857142857    3.83333333333333     3.9047619047619    3.91666666666667 
                  1                   3                   2                   1 
   3.95238095238095    3.97619047619048    4.09523809523809    4.13095238095238 
                  1                   3                   1                   1 
   4.14285714285714    4.16666666666667    4.23809523809524    4.29761904761905 
                  1                   1                   1                   2 
   4.48809523809524                 4.5    4.59523809523809    4.69047619047619 
                  2                   1                   1                   1 
   4.71428571428571    4.79761904761905    4.80952380952381    4.82142857142857 
                  1                   1                   2                   1 
   4.91666666666667    5.14285714285714    5.54761904761905    5.90476190476191 
                  1                   4                   2                   1 
   5.97619047619048    6.04761904761905    6.09523809523809    6.14285714285714 
                  1                   1                   1                   1 
   6.33333333333333    6.36904761904762    6.54761904761905    7.14285714285714 
                  1                   1                   1                   1 
   8.59523809523809 
                  1 

d$Prev_ER_count_within_int <- round(d$Prev_ER_count_within) #convert to integer
table(d$Prev_ER_count_within_int)  # check unique values

   -2    -1     0     1     2     3     4     5     6     7     9 
  101  1429 11533  1328   429   149    43    12     9     2     1 

# rerun <- TRUE
# if (rerun) {
# m_MF_ER_count_cap5_mo <- brm(
#   MF ~ mo(Prev_ER_count_within_int) + T1_Gender + Age_2024 +
#     Prev_Str_Overall + Prev_MF_Within + (1 | PID),
#   family = cumulative(link = "logit", threshold = "flexible"), data = d,
#   backend = "cmdstanr", chains = 4, iter = 4000, warmup = 1000, cores = 4)
# }
# saveRDS(m_MF_ER_count_cap5_mo, file = "m_MF_ER_count_cap5_mo.RDS")
m_MF_ER_count_cap5_mo <- readRDS("m_MF_ER_count_cap5_mo.RDS")
summary(m_MF_ER_count_cap5_mo)

 Family: cumulative 
  Links: mu = logit; disc = identity 
Formula: MF ~ mo(Prev_ER_count_within_int) + T1_Gender + Age_2024 + Prev_Str_Overall + Prev_MF_Within + (1 | PID) 
   Data: d (Number of observations: 6492) 
  Draws: 4 chains, each with iter = 4000; warmup = 1000; thin = 1;
         total post-warmup draws = 12000

Multilevel Hyperparameters:
~PID (Number of levels: 179) 
              Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
sd(Intercept)     1.82      0.11     1.61     2.06 1.00     1139     2383

Regression Coefficients:
                           Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS
Intercept[1]                  -2.24      0.78    -3.75    -0.72 1.01      679
Intercept[2]                   0.55      0.78    -0.96     2.07 1.01      675
Intercept[3]                   2.79      0.78     1.28     4.31 1.01      676
Intercept[4]                   4.86      0.78     3.33     6.39 1.01      682
T1_Gender                     -0.31      0.33    -0.96     0.34 1.00      654
Age_2024                      -0.01      0.03    -0.06     0.04 1.01      630
Prev_Str_Overall               0.11      0.02     0.08     0.15 1.00     6558
Prev_MF_Within                 0.94      0.04     0.87     1.01 1.00    10059
moPrev_ER_count_within_int    -0.02      0.04    -0.12     0.06 1.00     5329
                           Tail_ESS
Intercept[1]                   1483
Intercept[2]                   1537
Intercept[3]                   1558
Intercept[4]                   1650
T1_Gender                      1260
Age_2024                       1387
Prev_Str_Overall               8509
Prev_MF_Within                 8807
moPrev_ER_count_within_int     5070

Monotonic Simplex Parameters:
                               Estimate Est.Error l-95% CI u-95% CI Rhat
moPrev_ER_count_within_int1[1]     0.11      0.10     0.00     0.38 1.00
moPrev_ER_count_within_int1[2]     0.10      0.09     0.00     0.34 1.00
moPrev_ER_count_within_int1[3]     0.08      0.08     0.00     0.30 1.00
moPrev_ER_count_within_int1[4]     0.11      0.10     0.00     0.36 1.00
moPrev_ER_count_within_int1[5]     0.10      0.09     0.00     0.35 1.00
moPrev_ER_count_within_int1[6]     0.11      0.10     0.00     0.36 1.00
moPrev_ER_count_within_int1[7]     0.13      0.11     0.00     0.41 1.00
moPrev_ER_count_within_int1[8]     0.13      0.11     0.00     0.42 1.00
moPrev_ER_count_within_int1[9]     0.12      0.11     0.00     0.40 1.00
                               Bulk_ESS Tail_ESS
moPrev_ER_count_within_int1[1]     9102     5004
moPrev_ER_count_within_int1[2]    11460     6842
moPrev_ER_count_within_int1[3]     8033     5885
moPrev_ER_count_within_int1[4]     9001     5554
moPrev_ER_count_within_int1[5]    11417     6053
moPrev_ER_count_within_int1[6]    11262     6452
moPrev_ER_count_within_int1[7]    11962     7094
moPrev_ER_count_within_int1[8]    11792     8426
moPrev_ER_count_within_int1[9]    12104     8689

Further Distributional Parameters:
     Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
disc     1.00      0.00     1.00     1.00   NA       NA       NA

Draws were sampled using sample(hmc). For each parameter, Bulk_ESS
and Tail_ESS are effective sample size measures, and Rhat is the potential
scale reduction factor on split chains (at convergence, Rhat = 1).

exp(fixef(m_MF_ER_count_cap5_mo))

                              Estimate Est.Error        Q2.5       Q97.5
Intercept[1]                 0.1067197  2.174748  0.02341214   0.4886961
Intercept[2]                 1.7325300  2.173082  0.38167318   7.9211290
Intercept[3]                16.2250683  2.176037  3.58046704  74.5818739
Intercept[4]               128.5699389  2.185795 28.00721064 595.6556609
T1_Gender                    0.7350152  1.392194  0.38317247   1.4099692
Age_2024                     0.9880642  1.027245  0.93746269   1.0418739
Prev_Str_Overall             1.1206431  1.017439  1.08360142   1.1597161
Prev_MF_Within               2.5667763  1.037397  2.38854328   2.7577972
moPrev_ER_count_within_int   0.9770308  1.044009  0.88801375   1.0567366

pd_m_MF_ER_count_cap5_mo <- pd(m_MF_ER_count_cap5_mo)
pd_m_MF_ER_count_cap5_mo

Probability of Direction

Parameter                  |     pd
-----------------------------------
Intercept[1]               | 99.81%
Intercept[2]               | 76.10%
Intercept[3]               | 99.97%
Intercept[4]               |   100%
T1_Gender                  | 82.50%
Age_2024                   | 67.03%
Prev_Str_Overall           |   100%
Prev_MF_Within             |   100%
moPrev_ER_count_within_int | 71.01%

2 * (1 - .7101)

[1] 0.5798

2 * (1 - 1)

[1] 0

2 * (1 - 1)

[1] 0

2 * (1 - .8250)

[1] 0.35

2 * (1 - .6703)

[1] 0.6594

7 Sensitivity Analyses and Model Checks

####Aim 1 - To explore whether emotion regulation strategies predict subsequent mental fatigue####
# Sensitivity analysis: robustness to ±1 SD changes in time lag for the main result

# rerun <- TRUE
# if (rerun) {
#   start_time <- Sys.time()
#   print(start_time)
#   m_MF_ERall_time_diff_sensanalysis <- brm(MF ~ Prev_ER_1_Dis + Prev_ER_2_Rum + Prev_ER_3_SBl + Prev_ER_4_ExprS 
#                               + Prev_ER_5_ExperS + Prev_ER_6_Acc + Prev_ER_7_Pla + Prev_ER_8_Rea
#                               + Prev_ER_9_ESu + Prev_ER_10_Rel + T1_Gender + Age_2024 + Prev_Str_Overall 
#                               + Prev_MF_Within + (1 | PID), 
#                               family = cumulative(link = "logit", threshold = "flexible"), data = d_timediff_subset,
#                               backend = "cmdstanr", chains = 4, iter = 4000, warmup = 1000, cores = 4)
#   saveRDS(m_MF_ERall_time_diff_sensanalysis, file = "m_MF_ERall_time_diff_sensanalysis.RDS")
# }

m_MF_ERall_time_diff_sensanalysis <- readRDS("m_MF_ERall_time_diff_sensanalysis.RDS")
summary(m_MF_ERall_time_diff_sensanalysis)

 Family: cumulative 
  Links: mu = logit; disc = identity 
Formula: MF ~ Prev_ER_1_Dis + Prev_ER_2_Rum + Prev_ER_3_SBl + Prev_ER_4_ExprS + Prev_ER_5_ExperS + Prev_ER_6_Acc + Prev_ER_7_Pla + Prev_ER_8_Rea + Prev_ER_9_ESu + Prev_ER_10_Rel + T1_Gender + Age_2024 + Prev_Str_Overall + Prev_MF_Within + (1 | PID) 
   Data: d_timediff_subset (Number of observations: 4335) 
  Draws: 4 chains, each with iter = 4000; warmup = 1000; thin = 1;
         total post-warmup draws = 12000

Multilevel Hyperparameters:
~PID (Number of levels: 172) 
              Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
sd(Intercept)     1.83      0.12     1.61     2.07 1.00     1899     3368

Regression Coefficients:
                 Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
Intercept[1]        -2.46      0.83    -4.05    -0.82 1.00     1261     2069
Intercept[2]         0.34      0.83    -1.25     2.00 1.00     1253     2174
Intercept[3]         2.62      0.83     1.03     4.28 1.00     1255     2125
Intercept[4]         4.67      0.83     3.06     6.35 1.00     1272     2303
Prev_ER_1_Dis        0.23      0.11     0.01     0.46 1.00    14582     9084
Prev_ER_2_Rum       -0.19      0.18    -0.54     0.15 1.00    15324     9295
Prev_ER_3_SBl        0.16      0.22    -0.26     0.59 1.00    17878     9662
Prev_ER_4_ExprS     -0.14      0.17    -0.48     0.19 1.00    14218     8791
Prev_ER_5_ExperS    -0.13      0.15    -0.43     0.16 1.00    16174     8702
Prev_ER_6_Acc       -0.07      0.11    -0.29     0.16 1.00    15001     9394
Prev_ER_7_Pla       -0.23      0.14    -0.50     0.04 1.00    17067     9370
Prev_ER_8_Rea        0.32      0.19    -0.05     0.69 1.00    16340     8807
Prev_ER_9_ESu       -0.18      0.17    -0.52     0.17 1.00    16556     9139
Prev_ER_10_Rel      -0.06      0.12    -0.30     0.18 1.00    15526     8808
T1_Gender           -0.28      0.34    -0.97     0.40 1.00     1336     2019
Age_2024            -0.02      0.03    -0.08     0.04 1.00     1237     1565
Prev_Str_Overall     0.13      0.02     0.08     0.17 1.00    10338     9631
Prev_MF_Within       0.91      0.04     0.82     0.99 1.00    15174     9034

Further Distributional Parameters:
     Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
disc     1.00      0.00     1.00     1.00   NA       NA       NA

Draws were sampled using sample(hmc). For each parameter, Bulk_ESS
and Tail_ESS are effective sample size measures, and Rhat is the potential
scale reduction factor on split chains (at convergence, Rhat = 1).

exp(fixef(m_MF_ERall_time_diff_sensanalysis))

                     Estimate Est.Error        Q2.5       Q97.5
Intercept[1]       0.08534503  2.285388  0.01736805   0.4410808
Intercept[2]       1.40711692  2.284999  0.28590134   7.3669977
Intercept[3]      13.79501458  2.290372  2.80109385  72.2555940
Intercept[4]     106.23373000  2.303709 21.40215783 572.6935461
Prev_ER_1_Dis      1.26235141  1.119754  1.00811190   1.5809535
Prev_ER_2_Rum      0.82615593  1.193193  0.58293934   1.1632899
Prev_ER_3_SBl      1.17438747  1.243272  0.76967508   1.8040720
Prev_ER_4_ExprS    0.86535423  1.183510  0.61900381   1.2082481
Prev_ER_5_ExperS   0.87400589  1.162105  0.64991938   1.1744516
Prev_ER_6_Acc      0.93548481  1.120974  0.74802091   1.1722688
Prev_ER_7_Pla      0.79674084  1.147941  0.60734848   1.0425849
Prev_ER_8_Rea      1.38193968  1.208177  0.95079866   2.0016429
Prev_ER_9_ESu      0.83826926  1.190035  0.59538421   1.1809788
Prev_ER_10_Rel     0.93850937  1.129731  0.73810564   1.1930602
T1_Gender          0.75848184  1.411749  0.38068406   1.4865847
Age_2024           0.97738780  1.029428  0.92483931   1.0379141
Prev_Str_Overall   1.13447063  1.022340  1.08708039   1.1849679
Prev_MF_Within     2.47478424  1.045477  2.26977470   2.7000417

# Adding random slopes to check model convergence and robustness of results

# rerun <- TRUE
# 
# if (rerun) {
#   start_time <- Sys.time()
#   print(start_time)
#   m_MF_ERall_randomslopes2 <- brm(MF ~ Prev_ER_1_Dis + Prev_ER_2_Rum + Prev_ER_3_SBl + Prev_ER_4_ExprS 
#     + Prev_ER_5_ExperS + Prev_ER_6_Acc + Prev_ER_7_Pla + Prev_ER_8_Rea
#     + Prev_ER_9_ESu + Prev_ER_10_Rel + T1_Gender + Age_2024 + Prev_Str_Overall 
#     + Prev_MF_Within + 
#       (1 + Prev_ER_1_Dis + Prev_ER_2_Rum + Prev_ER_3_SBl + Prev_ER_4_ExprS 
#          + Prev_ER_5_ExperS + Prev_ER_6_Acc + Prev_ER_7_Pla + Prev_ER_8_Rea
#          + Prev_ER_9_ESu + Prev_ER_10_Rel + Prev_Str_Overall + Prev_MF_Within | PID), 
#     family = cumulative(link = "logit", threshold = "flexible"), data = d,
#     backend = "cmdstanr", chains = 4, iter = 4000, warmup = 1000, cores = 4)
# saveRDS(m_MF_ERall_randomslopes2, file = "m_MF_ERall_randomslopes2.RDS")
# end_time <- Sys.time()
# print(end_time)
# }
m_MF_ERall_randomslopes2 <- readRDS("m_MF_ERall_randomslopes2.RDS")
summary(m_MF_ERall_randomslopes2)

 Family: cumulative 
  Links: mu = logit; disc = identity 
Formula: MF ~ Prev_ER_1_Dis + Prev_ER_2_Rum + Prev_ER_3_SBl + Prev_ER_4_ExprS + Prev_ER_5_ExperS + Prev_ER_6_Acc + Prev_ER_7_Pla + Prev_ER_8_Rea + Prev_ER_9_ESu + Prev_ER_10_Rel + T1_Gender + Age_2024 + Prev_Str_Overall + Prev_MF_Within + (1 + Prev_ER_1_Dis + Prev_ER_2_Rum + Prev_ER_3_SBl + Prev_ER_4_ExprS + Prev_ER_5_ExperS + Prev_ER_6_Acc + Prev_ER_7_Pla + Prev_ER_8_Rea + Prev_ER_9_ESu + Prev_ER_10_Rel + Prev_Str_Overall + Prev_MF_Within | PID) 
   Data: d (Number of observations: 6492) 
  Draws: 4 chains, each with iter = 4000; warmup = 1000; thin = 1;
         total post-warmup draws = 12000

Multilevel Hyperparameters:
~PID (Number of levels: 179) 
                                       Estimate Est.Error l-95% CI u-95% CI
sd(Intercept)                              1.94      0.13     1.72     2.21
sd(Prev_ER_1_Dis)                          0.24      0.15     0.01     0.57
sd(Prev_ER_2_Rum)                          0.67      0.28     0.10     1.23
sd(Prev_ER_3_SBl)                          0.54      0.31     0.04     1.18
sd(Prev_ER_4_ExprS)                        0.21      0.16     0.01     0.59
sd(Prev_ER_5_ExperS)                       0.26      0.18     0.01     0.68
sd(Prev_ER_6_Acc)                          0.16      0.12     0.01     0.43
sd(Prev_ER_7_Pla)                          0.20      0.14     0.01     0.53
sd(Prev_ER_8_Rea)                          0.27      0.20     0.01     0.74
sd(Prev_ER_9_ESu)                          0.50      0.26     0.04     1.04
sd(Prev_ER_10_Rel)                         0.55      0.21     0.11     0.97
sd(Prev_Str_Overall)                       0.12      0.03     0.07     0.18
sd(Prev_MF_Within)                         0.51      0.05     0.41     0.62
cor(Intercept,Prev_ER_1_Dis)              -0.03      0.24    -0.49     0.45
cor(Intercept,Prev_ER_2_Rum)               0.11      0.20    -0.30     0.49
cor(Prev_ER_1_Dis,Prev_ER_2_Rum)          -0.00      0.26    -0.50     0.50
cor(Intercept,Prev_ER_3_SBl)              -0.13      0.23    -0.56     0.35
cor(Prev_ER_1_Dis,Prev_ER_3_SBl)           0.02      0.26    -0.51     0.52
cor(Prev_ER_2_Rum,Prev_ER_3_SBl)           0.13      0.25    -0.38     0.60
cor(Intercept,Prev_ER_4_ExprS)            -0.06      0.26    -0.54     0.46
cor(Prev_ER_1_Dis,Prev_ER_4_ExprS)        -0.02      0.27    -0.52     0.50
cor(Prev_ER_2_Rum,Prev_ER_4_ExprS)        -0.02      0.26    -0.52     0.50
cor(Prev_ER_3_SBl,Prev_ER_4_ExprS)         0.02      0.27    -0.49     0.53
cor(Intercept,Prev_ER_5_ExperS)           -0.03      0.26    -0.52     0.46
cor(Prev_ER_1_Dis,Prev_ER_5_ExperS)        0.04      0.26    -0.48     0.54
cor(Prev_ER_2_Rum,Prev_ER_5_ExperS)       -0.01      0.27    -0.53     0.51
cor(Prev_ER_3_SBl,Prev_ER_5_ExperS)        0.00      0.26    -0.51     0.51
cor(Prev_ER_4_ExprS,Prev_ER_5_ExperS)      0.00      0.27    -0.50     0.52
cor(Intercept,Prev_ER_6_Acc)              -0.06      0.25    -0.53     0.45
cor(Prev_ER_1_Dis,Prev_ER_6_Acc)           0.03      0.27    -0.50     0.53
cor(Prev_ER_2_Rum,Prev_ER_6_Acc)          -0.06      0.27    -0.56     0.46
cor(Prev_ER_3_SBl,Prev_ER_6_Acc)          -0.02      0.27    -0.53     0.49
cor(Prev_ER_4_ExprS,Prev_ER_6_Acc)        -0.00      0.27    -0.52     0.51
cor(Prev_ER_5_ExperS,Prev_ER_6_Acc)        0.01      0.27    -0.51     0.52
cor(Intercept,Prev_ER_7_Pla)               0.02      0.25    -0.49     0.50
cor(Prev_ER_1_Dis,Prev_ER_7_Pla)          -0.01      0.27    -0.51     0.51
cor(Prev_ER_2_Rum,Prev_ER_7_Pla)          -0.09      0.27    -0.59     0.44
cor(Prev_ER_3_SBl,Prev_ER_7_Pla)          -0.04      0.27    -0.55     0.48
cor(Prev_ER_4_ExprS,Prev_ER_7_Pla)        -0.00      0.27    -0.52     0.51
cor(Prev_ER_5_ExperS,Prev_ER_7_Pla)        0.01      0.27    -0.51     0.52
cor(Prev_ER_6_Acc,Prev_ER_7_Pla)           0.01      0.26    -0.50     0.51
cor(Intercept,Prev_ER_8_Rea)              -0.00      0.25    -0.50     0.49
cor(Prev_ER_1_Dis,Prev_ER_8_Rea)          -0.02      0.27    -0.52     0.49
cor(Prev_ER_2_Rum,Prev_ER_8_Rea)          -0.04      0.26    -0.53     0.48
cor(Prev_ER_3_SBl,Prev_ER_8_Rea)           0.02      0.27    -0.50     0.52
cor(Prev_ER_4_ExprS,Prev_ER_8_Rea)         0.01      0.27    -0.50     0.52
cor(Prev_ER_5_ExperS,Prev_ER_8_Rea)       -0.02      0.27    -0.55     0.50
cor(Prev_ER_6_Acc,Prev_ER_8_Rea)          -0.03      0.27    -0.54     0.49
cor(Prev_ER_7_Pla,Prev_ER_8_Rea)          -0.00      0.26    -0.51     0.50
cor(Intercept,Prev_ER_9_ESu)               0.01      0.23    -0.43     0.45
cor(Prev_ER_1_Dis,Prev_ER_9_ESu)          -0.05      0.26    -0.55     0.46
cor(Prev_ER_2_Rum,Prev_ER_9_ESu)          -0.04      0.25    -0.51     0.46
cor(Prev_ER_3_SBl,Prev_ER_9_ESu)           0.05      0.26    -0.47     0.54
cor(Prev_ER_4_ExprS,Prev_ER_9_ESu)         0.03      0.27    -0.49     0.54
cor(Prev_ER_5_ExperS,Prev_ER_9_ESu)        0.01      0.27    -0.51     0.53
cor(Prev_ER_6_Acc,Prev_ER_9_ESu)          -0.06      0.27    -0.57     0.46
cor(Prev_ER_7_Pla,Prev_ER_9_ESu)           0.01      0.27    -0.50     0.52
cor(Prev_ER_8_Rea,Prev_ER_9_ESu)           0.02      0.27    -0.50     0.53
cor(Intercept,Prev_ER_10_Rel)              0.03      0.19    -0.35     0.41
cor(Prev_ER_1_Dis,Prev_ER_10_Rel)          0.03      0.26    -0.47     0.52
cor(Prev_ER_2_Rum,Prev_ER_10_Rel)          0.04      0.25    -0.44     0.51
cor(Prev_ER_3_SBl,Prev_ER_10_Rel)          0.12      0.26    -0.40     0.60
cor(Prev_ER_4_ExprS,Prev_ER_10_Rel)        0.00      0.27    -0.51     0.51
cor(Prev_ER_5_ExperS,Prev_ER_10_Rel)      -0.01      0.26    -0.52     0.50
cor(Prev_ER_6_Acc,Prev_ER_10_Rel)         -0.05      0.27    -0.56     0.47
cor(Prev_ER_7_Pla,Prev_ER_10_Rel)         -0.06      0.26    -0.55     0.46
cor(Prev_ER_8_Rea,Prev_ER_10_Rel)          0.02      0.26    -0.49     0.52
cor(Prev_ER_9_ESu,Prev_ER_10_Rel)          0.06      0.25    -0.44     0.54
cor(Intercept,Prev_Str_Overall)           -0.47      0.14    -0.71    -0.16
cor(Prev_ER_1_Dis,Prev_Str_Overall)        0.02      0.26    -0.48     0.51
cor(Prev_ER_2_Rum,Prev_Str_Overall)       -0.11      0.24    -0.55     0.37
cor(Prev_ER_3_SBl,Prev_Str_Overall)        0.09      0.25    -0.40     0.56
cor(Prev_ER_4_ExprS,Prev_Str_Overall)     -0.02      0.26    -0.51     0.50
cor(Prev_ER_5_ExperS,Prev_Str_Overall)     0.01      0.26    -0.49     0.51
cor(Prev_ER_6_Acc,Prev_Str_Overall)        0.08      0.26    -0.44     0.58
cor(Prev_ER_7_Pla,Prev_Str_Overall)       -0.03      0.26    -0.52     0.49
cor(Prev_ER_8_Rea,Prev_Str_Overall)       -0.01      0.26    -0.51     0.50
cor(Prev_ER_9_ESu,Prev_Str_Overall)       -0.01      0.24    -0.47     0.47
cor(Prev_ER_10_Rel,Prev_Str_Overall)       0.15      0.23    -0.32     0.58
cor(Intercept,Prev_MF_Within)             -0.09      0.12    -0.32     0.14
cor(Prev_ER_1_Dis,Prev_MF_Within)          0.15      0.24    -0.35     0.59
cor(Prev_ER_2_Rum,Prev_MF_Within)         -0.15      0.22    -0.56     0.30
cor(Prev_ER_3_SBl,Prev_MF_Within)         -0.15      0.23    -0.58     0.33
cor(Prev_ER_4_ExprS,Prev_MF_Within)       -0.05      0.26    -0.54     0.47
cor(Prev_ER_5_ExperS,Prev_MF_Within)      -0.02      0.26    -0.51     0.48
cor(Prev_ER_6_Acc,Prev_MF_Within)          0.01      0.25    -0.48     0.50
cor(Prev_ER_7_Pla,Prev_MF_Within)          0.01      0.25    -0.47     0.51
cor(Prev_ER_8_Rea,Prev_MF_Within)         -0.06      0.26    -0.55     0.44
cor(Prev_ER_9_ESu,Prev_MF_Within)         -0.16      0.23    -0.58     0.32
cor(Prev_ER_10_Rel,Prev_MF_Within)        -0.22      0.21    -0.60     0.21
cor(Prev_Str_Overall,Prev_MF_Within)      -0.32      0.16    -0.62     0.02
                                       Rhat Bulk_ESS Tail_ESS
sd(Intercept)                          1.00     3327     5813
sd(Prev_ER_1_Dis)                      1.00     3681     6440
sd(Prev_ER_2_Rum)                      1.00     3735     3924
sd(Prev_ER_3_SBl)                      1.00     3801     6554
sd(Prev_ER_4_ExprS)                    1.00     6069     7237
sd(Prev_ER_5_ExperS)                   1.00     6025     7717
sd(Prev_ER_6_Acc)                      1.00     5875     7268
sd(Prev_ER_7_Pla)                      1.00     6301     7035
sd(Prev_ER_8_Rea)                      1.00     6143     7903
sd(Prev_ER_9_ESu)                      1.00     4155     4779
sd(Prev_ER_10_Rel)                     1.00     3797     3873
sd(Prev_Str_Overall)                   1.00     3984     5934
sd(Prev_MF_Within)                     1.00     7586    10078
cor(Intercept,Prev_ER_1_Dis)           1.00    23031     8743
cor(Intercept,Prev_ER_2_Rum)           1.00    15485     8984
cor(Prev_ER_1_Dis,Prev_ER_2_Rum)       1.00     7992     9380
cor(Intercept,Prev_ER_3_SBl)           1.00    18978     9270
cor(Prev_ER_1_Dis,Prev_ER_3_SBl)       1.00    10557     8706
cor(Prev_ER_2_Rum,Prev_ER_3_SBl)       1.00    11360     9739
cor(Intercept,Prev_ER_4_ExprS)         1.00    27404     9034
cor(Prev_ER_1_Dis,Prev_ER_4_ExprS)     1.00    18991     8761
cor(Prev_ER_2_Rum,Prev_ER_4_ExprS)     1.00    21078     9913
cor(Prev_ER_3_SBl,Prev_ER_4_ExprS)     1.00    14442     9655
cor(Intercept,Prev_ER_5_ExperS)        1.00    23853     8680
cor(Prev_ER_1_Dis,Prev_ER_5_ExperS)    1.00    15179     9827
cor(Prev_ER_2_Rum,Prev_ER_5_ExperS)    1.00    16556     9455
cor(Prev_ER_3_SBl,Prev_ER_5_ExperS)    1.00    14735     9596
cor(Prev_ER_4_ExprS,Prev_ER_5_ExperS)  1.00    11758     9598
cor(Intercept,Prev_ER_6_Acc)           1.00    23415     8659
cor(Prev_ER_1_Dis,Prev_ER_6_Acc)       1.00    17475     9276
cor(Prev_ER_2_Rum,Prev_ER_6_Acc)       1.00    16759     8892
cor(Prev_ER_3_SBl,Prev_ER_6_Acc)       1.00    15045    10185
cor(Prev_ER_4_ExprS,Prev_ER_6_Acc)     1.00    11394    10452
cor(Prev_ER_5_ExperS,Prev_ER_6_Acc)    1.00    10640    10089
cor(Intercept,Prev_ER_7_Pla)           1.00    25161     7195
cor(Prev_ER_1_Dis,Prev_ER_7_Pla)       1.00    17952     9540
cor(Prev_ER_2_Rum,Prev_ER_7_Pla)       1.00    15904     9991
cor(Prev_ER_3_SBl,Prev_ER_7_Pla)       1.00    14238     9774
cor(Prev_ER_4_ExprS,Prev_ER_7_Pla)     1.00    11997    10068
cor(Prev_ER_5_ExperS,Prev_ER_7_Pla)    1.00    11289    10457
cor(Prev_ER_6_Acc,Prev_ER_7_Pla)       1.00    10190     9809
cor(Intercept,Prev_ER_8_Rea)           1.00    24786     8132
cor(Prev_ER_1_Dis,Prev_ER_8_Rea)       1.00    16482    10200
cor(Prev_ER_2_Rum,Prev_ER_8_Rea)       1.00    16548     9868
cor(Prev_ER_3_SBl,Prev_ER_8_Rea)       1.00    14987    10807
cor(Prev_ER_4_ExprS,Prev_ER_8_Rea)     1.00    12005     9941
cor(Prev_ER_5_ExperS,Prev_ER_8_Rea)    1.00    10580     9476
cor(Prev_ER_6_Acc,Prev_ER_8_Rea)       1.00    10474     9815
cor(Prev_ER_7_Pla,Prev_ER_8_Rea)       1.00     9254     9800
cor(Intercept,Prev_ER_9_ESu)           1.00    20160     9177
cor(Prev_ER_1_Dis,Prev_ER_9_ESu)       1.00    10750     9934
cor(Prev_ER_2_Rum,Prev_ER_9_ESu)       1.00    12688    10193
cor(Prev_ER_3_SBl,Prev_ER_9_ESu)       1.00    10854    10370
cor(Prev_ER_4_ExprS,Prev_ER_9_ESu)     1.00     8269     9812
cor(Prev_ER_5_ExperS,Prev_ER_9_ESu)    1.00    10127    10190
cor(Prev_ER_6_Acc,Prev_ER_9_ESu)       1.00     8683     9188
cor(Prev_ER_7_Pla,Prev_ER_9_ESu)       1.00     9227    10352
cor(Prev_ER_8_Rea,Prev_ER_9_ESu)       1.00     8680    10051
cor(Intercept,Prev_ER_10_Rel)          1.00    19104     9629
cor(Prev_ER_1_Dis,Prev_ER_10_Rel)      1.00     7048     9288
cor(Prev_ER_2_Rum,Prev_ER_10_Rel)      1.00     9400     9541
cor(Prev_ER_3_SBl,Prev_ER_10_Rel)      1.00     6889     9315
cor(Prev_ER_4_ExprS,Prev_ER_10_Rel)    1.00     7222     9442
cor(Prev_ER_5_ExperS,Prev_ER_10_Rel)   1.00     6866     9411
cor(Prev_ER_6_Acc,Prev_ER_10_Rel)      1.00     6574     9057
cor(Prev_ER_7_Pla,Prev_ER_10_Rel)      1.00     7886     9638
cor(Prev_ER_8_Rea,Prev_ER_10_Rel)      1.00     8228     9796
cor(Prev_ER_9_ESu,Prev_ER_10_Rel)      1.00     8939     9880
cor(Intercept,Prev_Str_Overall)        1.00    11317     8978
cor(Prev_ER_1_Dis,Prev_Str_Overall)    1.00     4336     6706
cor(Prev_ER_2_Rum,Prev_Str_Overall)    1.00     5116     6701
cor(Prev_ER_3_SBl,Prev_Str_Overall)    1.00     4804     7513
cor(Prev_ER_4_ExprS,Prev_Str_Overall)  1.00     4599     8251
cor(Prev_ER_5_ExperS,Prev_Str_Overall) 1.00     4853     7434
cor(Prev_ER_6_Acc,Prev_Str_Overall)    1.00     4657     7909
cor(Prev_ER_7_Pla,Prev_Str_Overall)    1.00     5841     9144
cor(Prev_ER_8_Rea,Prev_Str_Overall)    1.00     6024     8554
cor(Prev_ER_9_ESu,Prev_Str_Overall)    1.00     7255    10905
cor(Prev_ER_10_Rel,Prev_Str_Overall)   1.00     6219     9914
cor(Intercept,Prev_MF_Within)          1.00    15067     9920
cor(Prev_ER_1_Dis,Prev_MF_Within)      1.00     1911     3310
cor(Prev_ER_2_Rum,Prev_MF_Within)      1.00     2364     4925
cor(Prev_ER_3_SBl,Prev_MF_Within)      1.00     2658     5199
cor(Prev_ER_4_ExprS,Prev_MF_Within)    1.00     2457     4505
cor(Prev_ER_5_ExperS,Prev_MF_Within)   1.00     2839     5733
cor(Prev_ER_6_Acc,Prev_MF_Within)      1.00     3017     6561
cor(Prev_ER_7_Pla,Prev_MF_Within)      1.00     3107     6666
cor(Prev_ER_8_Rea,Prev_MF_Within)      1.00     3762     7696
cor(Prev_ER_9_ESu,Prev_MF_Within)      1.00     4254     7108
cor(Prev_ER_10_Rel,Prev_MF_Within)     1.00     5731     7992
cor(Prev_Str_Overall,Prev_MF_Within)   1.00     7138     9042

Regression Coefficients:
                 Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
Intercept[1]        -2.67      0.80    -4.22    -1.09 1.00     2390     4669
Intercept[2]         0.23      0.80    -1.32     1.81 1.00     2385     4537
Intercept[3]         2.55      0.80     1.00     4.13 1.00     2391     4642
Intercept[4]         4.77      0.81     3.21     6.36 1.00     2426     4583
Prev_ER_1_Dis        0.19      0.10    -0.02     0.39 1.00    23074     9652
Prev_ER_2_Rum       -0.04      0.19    -0.42     0.32 1.00    15159     9784
Prev_ER_3_SBl       -0.28      0.20    -0.69     0.11 1.00    16839     8587
Prev_ER_4_ExprS      0.04      0.15    -0.25     0.33 1.00    22919     9386
Prev_ER_5_ExperS    -0.01      0.14    -0.28     0.26 1.00    22671     9313
Prev_ER_6_Acc       -0.05      0.10    -0.25     0.15 1.00    22147     9043
Prev_ER_7_Pla       -0.07      0.12    -0.31     0.15 1.00    21110    10197
Prev_ER_8_Rea        0.11      0.17    -0.22     0.44 1.00    18880     9094
Prev_ER_9_ESu       -0.12      0.17    -0.47     0.22 1.00    16073     9043
Prev_ER_10_Rel      -0.09      0.13    -0.35     0.18 1.00    18236     9737
T1_Gender           -0.25      0.33    -0.90     0.40 1.00     2516     4523
Age_2024            -0.03      0.03    -0.08     0.03 1.00     2268     4313
Prev_Str_Overall     0.14      0.02     0.09     0.18 1.00    10811     9840
Prev_MF_Within       0.91      0.06     0.79     1.03 1.00    16802     9723

Further Distributional Parameters:
     Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
disc     1.00      0.00     1.00     1.00   NA       NA       NA

Draws were sampled using sample(hmc). For each parameter, Bulk_ESS
and Tail_ESS are effective sample size measures, and Rhat is the potential
scale reduction factor on split chains (at convergence, Rhat = 1).

exp(fixef(m_MF_ERall_randomslopes2))

                     Estimate Est.Error        Q2.5       Q97.5
Intercept[1]       0.06953855  2.222524  0.01467603   0.3353944
Intercept[2]       1.25803335  2.218743  0.26664909   6.1091155
Intercept[3]      12.84992181  2.222700  2.72875647  62.1464223
Intercept[4]     118.13230508  2.238456 24.70821427 575.5431767
Prev_ER_1_Dis      1.20333466  1.107910  0.98241903   1.4705468
Prev_ER_2_Rum      0.95792365  1.203889  0.65900879   1.3752380
Prev_ER_3_SBl      0.75890045  1.225909  0.50234218   1.1213795
Prev_ER_4_ExprS    1.03606181  1.158696  0.77687130   1.3863350
Prev_ER_5_ExperS   0.99255422  1.147476  0.75741037   1.3027980
Prev_ER_6_Acc      0.95474473  1.106398  0.78250844   1.1625049
Prev_ER_7_Pla      0.92967373  1.125023  0.73610009   1.1665838
Prev_ER_8_Rea      1.11613961  1.184565  0.79870763   1.5501870
Prev_ER_9_ESu      0.88634097  1.190778  0.62508338   1.2441724
Prev_ER_10_Rel     0.91743671  1.143920  0.70542025   1.1987055
T1_Gender          0.77949412  1.386468  0.40752179   1.4879162
Age_2024           0.97114679  1.028045  0.92080098   1.0259214
Prev_Str_Overall   1.14716630  1.023335  1.09657967   1.2017178
Prev_MF_Within     2.47513729  1.062514  2.19838896   2.7921726

p_values_m_MF_ERall_randomslopes2 <- describe_posterior(m_MF_ERall_randomslopes2, test = "p_direction")

p_values_m_MF_ERall_randomslopes2

Summary of Posterior Distribution

Parameter        |    Median |         95% CI |     pd |  Rhat |      ESS
-------------------------------------------------------------------------
Intercept[1]     |     -2.67 | [-4.22, -1.09] | 99.97% | 1.000 |  2385.00
Intercept[2]     |      0.22 | [-1.32,  1.81] | 61.22% | 1.000 |  2380.00
Intercept[3]     |      2.55 | [ 1.00,  4.13] | 99.89% | 1.000 |  2383.00
Intercept[4]     |      4.77 | [ 3.21,  6.36] |   100% | 1.000 |  2421.00
Prev_ER_1_Dis    |      0.19 | [-0.02,  0.39] | 96.39% | 1.000 | 22892.00
Prev_ER_2_Rum    |     -0.04 | [-0.42,  0.32] | 59.17% | 1.000 | 15080.00
Prev_ER_3_SBl    |     -0.27 | [-0.69,  0.11] | 91.71% | 1.000 | 16349.00
Prev_ER_4_ExprS  |      0.04 | [-0.25,  0.33] | 59.77% | 1.000 | 22724.00
Prev_ER_5_ExperS | -6.91e-03 | [-0.28,  0.26] | 51.98% | 1.000 | 22934.00
Prev_ER_6_Acc    |     -0.05 | [-0.25,  0.15] | 67.28% | 1.000 | 22029.00
Prev_ER_7_Pla    |     -0.07 | [-0.31,  0.15] | 72.85% | 1.000 | 21064.00
Prev_ER_8_Rea    |      0.11 | [-0.22,  0.44] | 74.47% | 1.000 | 18603.00
Prev_ER_9_ESu    |     -0.12 | [-0.47,  0.22] | 75.64% | 1.000 | 16071.00
Prev_ER_10_Rel   |     -0.09 | [-0.35,  0.18] | 74.31% | 1.000 | 18554.00
T1_Gender        |     -0.25 | [-0.90,  0.40] | 77.88% | 1.001 |  2495.00
Age_2024         |     -0.03 | [-0.08,  0.03] | 85.64% | 1.000 |  2258.00
Prev_Str_Overall |      0.14 | [ 0.09,  0.18] |   100% | 1.000 | 10889.00
Prev_MF_Within   |      0.91 | [ 0.79,  1.03] |   100% | 1.000 | 16684.00

2* (1 -.9639)

[1] 0.0722

2* (1 -.5917)

[1] 0.8166

2* (1 -.9171)

[1] 0.1658

2* (1 -.5977)

[1] 0.8046

2* (1 -.5198)

[1] 0.9604

2* (1 -.6728)

[1] 0.6544

2* (1 -.7285)

[1] 0.543

2* (1 -.7447)

[1] 0.5106

2* (1 -.7562)

[1] 0.4876

2* (1 -.7431)

[1] 0.5138

2* (1 -.7788)

[1] 0.4424

2* (1 -.8564)

[1] 0.2872

2* (1 -1)

[1] 0

2* (1 -1)

[1] 0

# Robustness analysis: restricting the sample to participants with ≥10 valid pairs
# rerun <- TRUE
# 
# if (rerun) {
#   start_time <- Sys.time()
#   print(start_time)
#   m_MF_ERall_final_10pairs <- brm(MF ~ Prev_ER_1_Dis + Prev_ER_2_Rum + Prev_ER_3_SBl + Prev_ER_4_ExprS 
#                               + Prev_ER_5_ExperS + Prev_ER_6_Acc + Prev_ER_7_Pla + Prev_ER_8_Rea
#                               + Prev_ER_9_ESu + Prev_ER_10_Rel + T1_Gender + Age_2024 + Prev_Str_Overall 
#                               + Prev_MF_Within + (1 | PID), 
#                               family = cumulative(link = "logit", threshold = "flexible"), data = d_10validpairs,
#                               backend = "cmdstanr", chains = 4, iter = 4000, warmup = 1000, cores = 4)
#   saveRDS(m_MF_ERall_final_10pairs, file = "m_MF_ERall_final_10pairs.RDS")
#   end_time <- Sys.time()
#   print(end_time)
# }

m_MF_ERall_final_10pairs <- readRDS("m_MF_ERall_final_10pairs.RDS")
summary(m_MF_ERall_final_10pairs)

 Family: cumulative 
  Links: mu = logit; disc = identity 
Formula: MF ~ Prev_ER_1_Dis + Prev_ER_2_Rum + Prev_ER_3_SBl + Prev_ER_4_ExprS + Prev_ER_5_ExperS + Prev_ER_6_Acc + Prev_ER_7_Pla + Prev_ER_8_Rea + Prev_ER_9_ESu + Prev_ER_10_Rel + T1_Gender + Age_2024 + Prev_Str_Overall + Prev_MF_Within + (1 | PID) 
   Data: d_10validpairs (Number of observations: 6344) 
  Draws: 4 chains, each with iter = 4000; warmup = 1000; thin = 1;
         total post-warmup draws = 12000

Multilevel Hyperparameters:
~PID (Number of levels: 140) 
              Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
sd(Intercept)     1.82      0.12     1.61     2.07 1.00     1371     2203

Regression Coefficients:
                 Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
Intercept[1]        -2.41      0.87    -4.14    -0.65 1.00      855     1877
Intercept[2]         0.40      0.87    -1.34     2.15 1.00      853     1828
Intercept[3]         2.65      0.87     0.92     4.40 1.00      854     1911
Intercept[4]         4.75      0.88     3.01     6.51 1.00      869     1962
Prev_ER_1_Dis        0.23      0.09     0.04     0.41 1.00    14385     8871
Prev_ER_2_Rum       -0.02      0.15    -0.31     0.27 1.00    15913     8354
Prev_ER_3_SBl       -0.11      0.17    -0.44     0.21 1.00    17287     9018
Prev_ER_4_ExprS      0.02      0.14    -0.25     0.29 1.00    19998     9084
Prev_ER_5_ExperS    -0.01      0.13    -0.26     0.24 1.00    16303     8925
Prev_ER_6_Acc       -0.03      0.10    -0.22     0.16 1.00    17011     8969
Prev_ER_7_Pla       -0.07      0.11    -0.28     0.14 1.00    16319     9853
Prev_ER_8_Rea        0.06      0.15    -0.24     0.36 1.00    17104     9959
Prev_ER_9_ESu       -0.05      0.14    -0.32     0.22 1.00    15943     9184
Prev_ER_10_Rel      -0.09      0.10    -0.30     0.11 1.00    14792     8618
T1_Gender           -0.11      0.38    -0.84     0.62 1.00      987     2026
Age_2024            -0.03      0.03    -0.09     0.03 1.01      765     1801
Prev_Str_Overall     0.11      0.02     0.07     0.14 1.00     9749     9378
Prev_MF_Within       0.96      0.04     0.89     1.03 1.00    15337     9200

Further Distributional Parameters:
     Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
disc     1.00      0.00     1.00     1.00   NA       NA       NA

Draws were sampled using sample(hmc). For each parameter, Bulk_ESS
and Tail_ESS are effective sample size measures, and Rhat is the potential
scale reduction factor on split chains (at convergence, Rhat = 1).

exp(fixef(m_MF_ERall_final_10pairs))

                     Estimate Est.Error        Q2.5       Q97.5
Intercept[1]       0.09025801  2.396330  0.01598659   0.5224507
Intercept[2]       1.49323197  2.396194  0.26280558   8.5872732
Intercept[3]      14.14888899  2.398233  2.50764922  81.0486831
Intercept[4]     115.72548496  2.409300 20.38542123 670.5816700
Prev_ER_1_Dis      1.25395914  1.097306  1.04417224   1.5064974
Prev_ER_2_Rum      0.98150326  1.156835  0.73229316   1.3076683
Prev_ER_3_SBl      0.89622009  1.182244  0.64550831   1.2384876
Prev_ER_4_ExprS    1.02019096  1.146785  0.77859904   1.3353260
Prev_ER_5_ExperS   0.99102885  1.137039  0.77051025   1.2731982
Prev_ER_6_Acc      0.97019255  1.100109  0.80527677   1.1716923
Prev_ER_7_Pla      0.93637095  1.113064  0.75617906   1.1541322
Prev_ER_8_Rea      1.06030004  1.165234  0.78904636   1.4273544
Prev_ER_9_ESu      0.95151763  1.146282  0.72607227   1.2436310
Prev_ER_10_Rel     0.91359673  1.109326  0.74340331   1.1191509
T1_Gender          0.89661230  1.457392  0.43230414   1.8580935
Age_2024           0.97326013  1.031116  0.91731541   1.0350549
Prev_Str_Overall   1.11298050  1.018408  1.07390127   1.1525735
Prev_MF_Within     2.60662242  1.038169  2.42318932   2.8055034

#AIM 1 - Stress as outcome
#Adding random slopes to check model convergence and robustness of results

# d$Str_Overall_ordered <- factor(d$Str_Overall, ordered = TRUE)
# 
# if (rerun) {
#   start_time <- Sys.time()
#   print(start_time)
#   m_Stress_ERall_Cov4_randomslopes1 <- brm(Str_Overall_ordered ~ Prev_ER_1_Dis + Prev_ER_2_Rum + Prev_ER_3_SBl + Prev_ER_4_ExprS
#                              + Prev_ER_5_ExperS + Prev_ER_6_Acc + Prev_ER_7_Pla + Prev_ER_8_Rea
#                              + Prev_ER_9_ESu + Prev_ER_10_Rel + T1_Gender + Age_2024 + Prev_Str_Overall_Within
#                              + Prev_MF + 
#                                (1 + Prev_ER_1_Dis + Prev_ER_2_Rum + Prev_ER_3_SBl + Prev_ER_4_ExprS 
#                                 + Prev_ER_5_ExperS + Prev_ER_6_Acc + Prev_ER_7_Pla + Prev_ER_8_Rea
#                                 + Prev_ER_9_ESu + Prev_ER_10_Rel + Prev_Str_Overall + Prev_MF_Within | PID),
#                              family = cumulative(link = "logit", threshold = "flexible"), data = d,
#                              backend = "cmdstanr", chains = 4, iter = 4000, warmup = 1000, cores = 4)
#   saveRDS(m_Stress_ERall_Cov4_randomslopes1, file = "m_Stress_ERall_Cov4_randomslopes1.RDS")
#   end_time <- Sys.time()
#   print(end_time)
# }
m_Stress_ERall_Cov4_randomslopes1 <- readRDS("m_Stress_ERall_Cov4_randomslopes1.RDS")
summary(m_Stress_ERall_Cov4_randomslopes1)

 Family: cumulative 
  Links: mu = logit; disc = identity 
Formula: Str_Overall_ordered ~ Prev_ER_1_Dis + Prev_ER_2_Rum + Prev_ER_3_SBl + Prev_ER_4_ExprS + Prev_ER_5_ExperS + Prev_ER_6_Acc + Prev_ER_7_Pla + Prev_ER_8_Rea + Prev_ER_9_ESu + Prev_ER_10_Rel + T1_Gender + Age_2024 + Prev_Str_Overall_Within + Prev_MF + (1 + Prev_ER_1_Dis + Prev_ER_2_Rum + Prev_ER_3_SBl + Prev_ER_4_ExprS + Prev_ER_5_ExperS + Prev_ER_6_Acc + Prev_ER_7_Pla + Prev_ER_8_Rea + Prev_ER_9_ESu + Prev_ER_10_Rel + Prev_Str_Overall + Prev_MF_Within | PID) 
   Data: d (Number of observations: 6497) 
  Draws: 4 chains, each with iter = 4000; warmup = 1000; thin = 1;
         total post-warmup draws = 12000

Multilevel Hyperparameters:
~PID (Number of levels: 179) 
                                       Estimate Est.Error l-95% CI u-95% CI
sd(Intercept)                              2.43      0.17     2.13     2.78
sd(Prev_ER_1_Dis)                          0.36      0.17     0.04     0.69
sd(Prev_ER_2_Rum)                          0.59      0.31     0.05     1.22
sd(Prev_ER_3_SBl)                          0.41      0.28     0.02     1.02
sd(Prev_ER_4_ExprS)                        0.26      0.19     0.01     0.70
sd(Prev_ER_5_ExperS)                       0.30      0.19     0.01     0.70
sd(Prev_ER_6_Acc)                          0.34      0.17     0.03     0.68
sd(Prev_ER_7_Pla)                          0.47      0.25     0.03     0.96
sd(Prev_ER_8_Rea)                          0.33      0.24     0.01     0.90
sd(Prev_ER_9_ESu)                          0.56      0.28     0.05     1.12
sd(Prev_ER_10_Rel)                         0.38      0.20     0.03     0.77
sd(Prev_Str_Overall)                       0.25      0.03     0.19     0.31
sd(Prev_MF_Within)                         0.08      0.05     0.00     0.20
cor(Intercept,Prev_ER_1_Dis)              -0.20      0.22    -0.59     0.26
cor(Intercept,Prev_ER_2_Rum)              -0.30      0.23    -0.67     0.23
cor(Prev_ER_1_Dis,Prev_ER_2_Rum)          -0.01      0.25    -0.50     0.48
cor(Intercept,Prev_ER_3_SBl)              -0.07      0.25    -0.54     0.43
cor(Prev_ER_1_Dis,Prev_ER_3_SBl)          -0.04      0.26    -0.53     0.47
cor(Prev_ER_2_Rum,Prev_ER_3_SBl)           0.03      0.26    -0.49     0.52
cor(Intercept,Prev_ER_4_ExprS)             0.01      0.25    -0.48     0.49
cor(Prev_ER_1_Dis,Prev_ER_4_ExprS)         0.03      0.27    -0.49     0.55
cor(Prev_ER_2_Rum,Prev_ER_4_ExprS)        -0.01      0.27    -0.51     0.51
cor(Prev_ER_3_SBl,Prev_ER_4_ExprS)         0.01      0.26    -0.50     0.51
cor(Intercept,Prev_ER_5_ExperS)           -0.13      0.25    -0.58     0.39
cor(Prev_ER_1_Dis,Prev_ER_5_ExperS)        0.05      0.26    -0.47     0.54
cor(Prev_ER_2_Rum,Prev_ER_5_ExperS)        0.02      0.26    -0.50     0.52
cor(Prev_ER_3_SBl,Prev_ER_5_ExperS)        0.01      0.27    -0.51     0.51
cor(Prev_ER_4_ExprS,Prev_ER_5_ExperS)     -0.02      0.27    -0.53     0.49
cor(Intercept,Prev_ER_6_Acc)              -0.18      0.22    -0.58     0.28
cor(Prev_ER_1_Dis,Prev_ER_6_Acc)           0.11      0.26    -0.40     0.59
cor(Prev_ER_2_Rum,Prev_ER_6_Acc)          -0.09      0.25    -0.56     0.42
cor(Prev_ER_3_SBl,Prev_ER_6_Acc)           0.03      0.27    -0.49     0.54
cor(Prev_ER_4_ExprS,Prev_ER_6_Acc)        -0.03      0.26    -0.53     0.49
cor(Prev_ER_5_ExperS,Prev_ER_6_Acc)        0.01      0.26    -0.50     0.52
cor(Intercept,Prev_ER_7_Pla)               0.03      0.21    -0.41     0.44
cor(Prev_ER_1_Dis,Prev_ER_7_Pla)           0.03      0.26    -0.47     0.52
cor(Prev_ER_2_Rum,Prev_ER_7_Pla)           0.01      0.25    -0.48     0.48
cor(Prev_ER_3_SBl,Prev_ER_7_Pla)           0.06      0.26    -0.46     0.55
cor(Prev_ER_4_ExprS,Prev_ER_7_Pla)         0.05      0.27    -0.48     0.56
cor(Prev_ER_5_ExperS,Prev_ER_7_Pla)       -0.10      0.27    -0.60     0.44
cor(Prev_ER_6_Acc,Prev_ER_7_Pla)           0.10      0.26    -0.41     0.57
cor(Intercept,Prev_ER_8_Rea)              -0.07      0.25    -0.54     0.43
cor(Prev_ER_1_Dis,Prev_ER_8_Rea)           0.00      0.26    -0.49     0.50
cor(Prev_ER_2_Rum,Prev_ER_8_Rea)           0.01      0.26    -0.50     0.51
cor(Prev_ER_3_SBl,Prev_ER_8_Rea)           0.05      0.27    -0.48     0.55
cor(Prev_ER_4_ExprS,Prev_ER_8_Rea)         0.03      0.26    -0.48     0.54
cor(Prev_ER_5_ExperS,Prev_ER_8_Rea)        0.02      0.26    -0.48     0.52
cor(Prev_ER_6_Acc,Prev_ER_8_Rea)           0.06      0.27    -0.46     0.55
cor(Prev_ER_7_Pla,Prev_ER_8_Rea)           0.09      0.27    -0.44     0.58
cor(Intercept,Prev_ER_9_ESu)               0.16      0.23    -0.32     0.57
cor(Prev_ER_1_Dis,Prev_ER_9_ESu)          -0.15      0.26    -0.62     0.40
cor(Prev_ER_2_Rum,Prev_ER_9_ESu)           0.03      0.25    -0.46     0.51
cor(Prev_ER_3_SBl,Prev_ER_9_ESu)           0.05      0.26    -0.46     0.55
cor(Prev_ER_4_ExprS,Prev_ER_9_ESu)         0.02      0.26    -0.49     0.52
cor(Prev_ER_5_ExperS,Prev_ER_9_ESu)       -0.09      0.26    -0.57     0.43
cor(Prev_ER_6_Acc,Prev_ER_9_ESu)          -0.11      0.25    -0.58     0.40
cor(Prev_ER_7_Pla,Prev_ER_9_ESu)          -0.02      0.25    -0.51     0.47
cor(Prev_ER_8_Rea,Prev_ER_9_ESu)          -0.00      0.26    -0.50     0.51
cor(Intercept,Prev_ER_10_Rel)             -0.19      0.22    -0.59     0.29
cor(Prev_ER_1_Dis,Prev_ER_10_Rel)          0.07      0.26    -0.45     0.55
cor(Prev_ER_2_Rum,Prev_ER_10_Rel)          0.04      0.26    -0.46     0.53
cor(Prev_ER_3_SBl,Prev_ER_10_Rel)          0.02      0.26    -0.50     0.52
cor(Prev_ER_4_ExprS,Prev_ER_10_Rel)       -0.03      0.27    -0.54     0.48
cor(Prev_ER_5_ExperS,Prev_ER_10_Rel)       0.01      0.26    -0.49     0.52
cor(Prev_ER_6_Acc,Prev_ER_10_Rel)          0.04      0.26    -0.46     0.54
cor(Prev_ER_7_Pla,Prev_ER_10_Rel)         -0.05      0.26    -0.53     0.46
cor(Prev_ER_8_Rea,Prev_ER_10_Rel)         -0.02      0.26    -0.52     0.50
cor(Prev_ER_9_ESu,Prev_ER_10_Rel)         -0.08      0.26    -0.57     0.44
cor(Intercept,Prev_Str_Overall)           -0.18      0.12    -0.41     0.06
cor(Prev_ER_1_Dis,Prev_Str_Overall)        0.15      0.24    -0.33     0.59
cor(Prev_ER_2_Rum,Prev_Str_Overall)       -0.23      0.23    -0.62     0.29
cor(Prev_ER_3_SBl,Prev_Str_Overall)       -0.00      0.25    -0.48     0.49
cor(Prev_ER_4_ExprS,Prev_Str_Overall)     -0.04      0.26    -0.54     0.47
cor(Prev_ER_5_ExperS,Prev_Str_Overall)     0.06      0.25    -0.43     0.54
cor(Prev_ER_6_Acc,Prev_Str_Overall)        0.10      0.23    -0.35     0.54
cor(Prev_ER_7_Pla,Prev_Str_Overall)       -0.06      0.23    -0.48     0.40
cor(Prev_ER_8_Rea,Prev_Str_Overall)       -0.07      0.25    -0.53     0.44
cor(Prev_ER_9_ESu,Prev_Str_Overall)       -0.13      0.24    -0.55     0.36
cor(Prev_ER_10_Rel,Prev_Str_Overall)       0.05      0.24    -0.42     0.50
cor(Intercept,Prev_MF_Within)             -0.12      0.24    -0.56     0.40
cor(Prev_ER_1_Dis,Prev_MF_Within)          0.08      0.26    -0.44     0.57
cor(Prev_ER_2_Rum,Prev_MF_Within)         -0.07      0.27    -0.56     0.46
cor(Prev_ER_3_SBl,Prev_MF_Within)          0.02      0.27    -0.50     0.52
cor(Prev_ER_4_ExprS,Prev_MF_Within)        0.02      0.26    -0.49     0.52
cor(Prev_ER_5_ExperS,Prev_MF_Within)       0.05      0.26    -0.47     0.56
cor(Prev_ER_6_Acc,Prev_MF_Within)          0.06      0.26    -0.45     0.56
cor(Prev_ER_7_Pla,Prev_MF_Within)          0.01      0.26    -0.49     0.51
cor(Prev_ER_8_Rea,Prev_MF_Within)          0.04      0.27    -0.49     0.55
cor(Prev_ER_9_ESu,Prev_MF_Within)         -0.07      0.27    -0.56     0.46
cor(Prev_ER_10_Rel,Prev_MF_Within)        -0.02      0.26    -0.52     0.50
cor(Prev_Str_Overall,Prev_MF_Within)       0.13      0.25    -0.38     0.58
                                       Rhat Bulk_ESS Tail_ESS
sd(Intercept)                          1.00     2050     4408
sd(Prev_ER_1_Dis)                      1.00     2018     2105
sd(Prev_ER_2_Rum)                      1.00     1423     2886
sd(Prev_ER_3_SBl)                      1.00     3126     4786
sd(Prev_ER_4_ExprS)                    1.00     4064     4980
sd(Prev_ER_5_ExperS)                   1.00     3382     3990
sd(Prev_ER_6_Acc)                      1.00     2575     3320
sd(Prev_ER_7_Pla)                      1.00     1544     2488
sd(Prev_ER_8_Rea)                      1.00     3264     5469
sd(Prev_ER_9_ESu)                      1.00     2599     2755
sd(Prev_ER_10_Rel)                     1.00     2296     2970
sd(Prev_Str_Overall)                   1.00     2697     5348
sd(Prev_MF_Within)                     1.00     2792     4229
cor(Intercept,Prev_ER_1_Dis)           1.00     9381     7062
cor(Intercept,Prev_ER_2_Rum)           1.00     6600     6250
cor(Prev_ER_1_Dis,Prev_ER_2_Rum)       1.00     5518     7771
cor(Intercept,Prev_ER_3_SBl)           1.00    12698     8673
cor(Prev_ER_1_Dis,Prev_ER_3_SBl)       1.00     9069     8918
cor(Prev_ER_2_Rum,Prev_ER_3_SBl)       1.00    10636     9380
cor(Intercept,Prev_ER_4_ExprS)         1.00    14473     9042
cor(Prev_ER_1_Dis,Prev_ER_4_ExprS)     1.00    10440     8251
cor(Prev_ER_2_Rum,Prev_ER_4_ExprS)     1.00    11779     9856
cor(Prev_ER_3_SBl,Prev_ER_4_ExprS)     1.00    10551    10262
cor(Intercept,Prev_ER_5_ExperS)        1.00    10928     8553
cor(Prev_ER_1_Dis,Prev_ER_5_ExperS)    1.00    10038     9212
cor(Prev_ER_2_Rum,Prev_ER_5_ExperS)    1.00    10432     8668
cor(Prev_ER_3_SBl,Prev_ER_5_ExperS)    1.00     8436     9374
cor(Prev_ER_4_ExprS,Prev_ER_5_ExperS)  1.00     8138     8852
cor(Intercept,Prev_ER_6_Acc)           1.00    10504     7800
cor(Prev_ER_1_Dis,Prev_ER_6_Acc)       1.00     6335     8372
cor(Prev_ER_2_Rum,Prev_ER_6_Acc)       1.00     6549     8723
cor(Prev_ER_3_SBl,Prev_ER_6_Acc)       1.00     7113     8514
cor(Prev_ER_4_ExprS,Prev_ER_6_Acc)     1.00     6337     7376
cor(Prev_ER_5_ExperS,Prev_ER_6_Acc)    1.00     7403     8999
cor(Intercept,Prev_ER_7_Pla)           1.00    11512     8125
cor(Prev_ER_1_Dis,Prev_ER_7_Pla)       1.00     4998     7635
cor(Prev_ER_2_Rum,Prev_ER_7_Pla)       1.00     6782     8789
cor(Prev_ER_3_SBl,Prev_ER_7_Pla)       1.00     5214     7784
cor(Prev_ER_4_ExprS,Prev_ER_7_Pla)     1.00     4935     7183
cor(Prev_ER_5_ExperS,Prev_ER_7_Pla)    1.00     4554     7540
cor(Prev_ER_6_Acc,Prev_ER_7_Pla)       1.00     6187     8652
cor(Intercept,Prev_ER_8_Rea)           1.00    13943     9217
cor(Prev_ER_1_Dis,Prev_ER_8_Rea)       1.00     9712     8745
cor(Prev_ER_2_Rum,Prev_ER_8_Rea)       1.00    10357     8783
cor(Prev_ER_3_SBl,Prev_ER_8_Rea)       1.00     8506     8089
cor(Prev_ER_4_ExprS,Prev_ER_8_Rea)     1.00     7627     9339
cor(Prev_ER_5_ExperS,Prev_ER_8_Rea)    1.00     9184    10112
cor(Prev_ER_6_Acc,Prev_ER_8_Rea)       1.00     9099     9481
cor(Prev_ER_7_Pla,Prev_ER_8_Rea)       1.00     8430     9710
cor(Intercept,Prev_ER_9_ESu)           1.00    12441     8039
cor(Prev_ER_1_Dis,Prev_ER_9_ESu)       1.00     5328     7911
cor(Prev_ER_2_Rum,Prev_ER_9_ESu)       1.00     8728     9039
cor(Prev_ER_3_SBl,Prev_ER_9_ESu)       1.00     7212     8217
cor(Prev_ER_4_ExprS,Prev_ER_9_ESu)     1.00     7284     9144
cor(Prev_ER_5_ExperS,Prev_ER_9_ESu)    1.00     5924     8536
cor(Prev_ER_6_Acc,Prev_ER_9_ESu)       1.00     7655     9114
cor(Prev_ER_7_Pla,Prev_ER_9_ESu)       1.00     8884    10062
cor(Prev_ER_8_Rea,Prev_ER_9_ESu)       1.00     7695     8907
cor(Intercept,Prev_ER_10_Rel)          1.00     7871     7167
cor(Prev_ER_1_Dis,Prev_ER_10_Rel)      1.00     6428     8345
cor(Prev_ER_2_Rum,Prev_ER_10_Rel)      1.00     8039     8336
cor(Prev_ER_3_SBl,Prev_ER_10_Rel)      1.00     7483     8799
cor(Prev_ER_4_ExprS,Prev_ER_10_Rel)    1.00     7203     8636
cor(Prev_ER_5_ExperS,Prev_ER_10_Rel)   1.00     7012     8131
cor(Prev_ER_6_Acc,Prev_ER_10_Rel)      1.00     7517     9323
cor(Prev_ER_7_Pla,Prev_ER_10_Rel)      1.00     7729     8585
cor(Prev_ER_8_Rea,Prev_ER_10_Rel)      1.00     7254    10053
cor(Prev_ER_9_ESu,Prev_ER_10_Rel)      1.00     7179     9264
cor(Intercept,Prev_Str_Overall)        1.00     4663     6795
cor(Prev_ER_1_Dis,Prev_Str_Overall)    1.01     1015     1752
cor(Prev_ER_2_Rum,Prev_Str_Overall)    1.00     1134     1258
cor(Prev_ER_3_SBl,Prev_Str_Overall)    1.00     1663     3798
cor(Prev_ER_4_ExprS,Prev_Str_Overall)  1.00     1486     3720
cor(Prev_ER_5_ExperS,Prev_Str_Overall) 1.00     1532     4168
cor(Prev_ER_6_Acc,Prev_Str_Overall)    1.00     2183     4612
cor(Prev_ER_7_Pla,Prev_Str_Overall)    1.00     2838     5437
cor(Prev_ER_8_Rea,Prev_Str_Overall)    1.00     2398     4869
cor(Prev_ER_9_ESu,Prev_Str_Overall)    1.00     2732     5733
cor(Prev_ER_10_Rel,Prev_Str_Overall)   1.00     3649     7597
cor(Intercept,Prev_MF_Within)          1.00    13031     8190
cor(Prev_ER_1_Dis,Prev_MF_Within)      1.00     8216     8550
cor(Prev_ER_2_Rum,Prev_MF_Within)      1.00     9193     8109
cor(Prev_ER_3_SBl,Prev_MF_Within)      1.00     9501     9475
cor(Prev_ER_4_ExprS,Prev_MF_Within)    1.00     9943     9932
cor(Prev_ER_5_ExperS,Prev_MF_Within)   1.00     7613     8996
cor(Prev_ER_6_Acc,Prev_MF_Within)      1.00     9682    10058
cor(Prev_ER_7_Pla,Prev_MF_Within)      1.00     8776    10198
cor(Prev_ER_8_Rea,Prev_MF_Within)      1.00     7716     8611
cor(Prev_ER_9_ESu,Prev_MF_Within)      1.00     8443     9524
cor(Prev_ER_10_Rel,Prev_MF_Within)     1.00     8516     9334
cor(Prev_Str_Overall,Prev_MF_Within)   1.00    10200     9300

Regression Coefficients:
                        Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS
Intercept[1]               -1.63      1.01    -3.57     0.43 1.01     1146
Intercept[2]                0.06      1.01    -1.88     2.11 1.01     1147
Intercept[3]                1.27      1.01    -0.68     3.32 1.00     1147
Intercept[4]                2.24      1.01     0.30     4.29 1.00     1145
Intercept[5]                3.07      1.02     1.12     5.12 1.00     1146
Intercept[6]                3.96      1.02     2.01     6.01 1.00     1147
Intercept[7]                5.05      1.02     3.10     7.13 1.00     1147
Intercept[8]                6.19      1.02     4.23     8.27 1.00     1161
Intercept[9]                7.57      1.03     5.60     9.65 1.00     1188
Intercept[10]               8.83      1.05     6.84    10.96 1.00     1211
Prev_ER_1_Dis               0.31      0.11     0.11     0.52 1.00     9003
Prev_ER_2_Rum               0.03      0.17    -0.30     0.38 1.00     6901
Prev_ER_3_SBl               0.08      0.19    -0.31     0.44 1.00     8362
Prev_ER_4_ExprS            -0.03      0.15    -0.31     0.26 1.00    12637
Prev_ER_5_ExperS            0.06      0.13    -0.19     0.33 1.00    12098
Prev_ER_6_Acc               0.16      0.11    -0.05     0.37 1.00    10613
Prev_ER_7_Pla               0.01      0.13    -0.25     0.26 1.00     9843
Prev_ER_8_Rea               0.19      0.17    -0.15     0.53 1.00    10787
Prev_ER_9_ESu              -0.25      0.17    -0.59     0.08 1.00    10530
Prev_ER_10_Rel             -0.04      0.12    -0.27     0.19 1.00    10173
T1_Gender                  -0.02      0.42    -0.84     0.82 1.01      820
Age_2024                   -0.01      0.04    -0.08     0.06 1.00     1165
Prev_Str_Overall_Within     0.42      0.03     0.35     0.48 1.00     4554
Prev_MF                     0.15      0.04     0.08     0.22 1.00    10419
                        Tail_ESS
Intercept[1]                1992
Intercept[2]                2003
Intercept[3]                1985
Intercept[4]                2007
Intercept[5]                1981
Intercept[6]                1993
Intercept[7]                2005
Intercept[8]                2067
Intercept[9]                2138
Intercept[10]               2285
Prev_ER_1_Dis               8959
Prev_ER_2_Rum               7323
Prev_ER_3_SBl               7344
Prev_ER_4_ExprS             9099
Prev_ER_5_ExperS            8587
Prev_ER_6_Acc               8929
Prev_ER_7_Pla               7844
Prev_ER_8_Rea               8832
Prev_ER_9_ESu               8911
Prev_ER_10_Rel              8183
T1_Gender                   2353
Age_2024                    2188
Prev_Str_Overall_Within     6817
Prev_MF                     8479

Further Distributional Parameters:
     Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
disc     1.00      0.00     1.00     1.00   NA       NA       NA

Draws were sampled using sample(hmc). For each parameter, Bulk_ESS
and Tail_ESS are effective sample size measures, and Rhat is the potential
scale reduction factor on split chains (at convergence, Rhat = 1).

exp(fixef(m_Stress_ERall_Cov4_randomslopes1))

                            Estimate Est.Error         Q2.5        Q97.5
Intercept[1]               0.1966249  2.758399   0.02813863     1.540047
Intercept[2]               1.0663449  2.757653   0.15234300     8.270910
Intercept[3]               3.5562915  2.757463   0.50846542    27.751607
Intercept[4]               9.4003211  2.758249   1.35101944    73.175671
Intercept[5]              21.4624459  2.760968   3.05483017   167.342356
Intercept[6]              52.4271261  2.764416   7.48311014   408.527712
Intercept[7]             156.4426569  2.770866  22.21753851  1243.342691
Intercept[8]             485.8486590  2.781753  68.63861313  3885.582744
Intercept[9]            1946.2193358  2.807761 270.53750789 15551.684660
Intercept[10]           6841.9086847  2.849168 931.79399048 57567.733072
Prev_ER_1_Dis              1.3630101  1.111354   1.11302660     1.677429
Prev_ER_2_Rum              1.0337944  1.187438   0.74363387     1.458432
Prev_ER_3_SBl              1.0851402  1.209895   0.73648326     1.551870
Prev_ER_4_ExprS            0.9731615  1.156093   0.73390429     1.298272
Prev_ER_5_ExperS           1.0660867  1.141546   0.82407002     1.387008
Prev_ER_6_Acc              1.1744999  1.111698   0.95269429     1.448022
Prev_ER_7_Pla              1.0075295  1.138883   0.77502651     1.297776
Prev_ER_8_Rea              1.2146019  1.190099   0.86298200     1.707263
Prev_ER_9_ESu              0.7802131  1.184691   0.55616128     1.080029
Prev_ER_10_Rel             0.9591976  1.123104   0.76425515     1.204422
T1_Gender                  0.9805031  1.520624   0.43369171     2.268444
Age_2024                   0.9928299  1.035764   0.92664456     1.064184
Prev_Str_Overall_Within    1.5187669  1.033995   1.41990210     1.617029
Prev_MF                    1.1614751  1.036090   1.08399464     1.245745

p_values_m_Stress_ERall_Cov4_randomslopes1 <- describe_posterior(m_Stress_ERall_Cov4_randomslopes1, test = "p_direction")
p_values_m_Stress_ERall_Cov4_randomslopes1

Summary of Posterior Distribution

Parameter               |    Median |         95% CI |     pd |  Rhat |      ESS
--------------------------------------------------------------------------------
Intercept[1]            |     -1.63 | [-3.57,  0.43] | 94.35% | 1.005 |  1138.00
Intercept[2]            |      0.06 | [-1.88,  2.11] | 52.30% | 1.005 |  1138.00
Intercept[3]            |      1.26 | [-0.68,  3.32] | 89.47% | 1.005 |  1138.00
Intercept[4]            |      2.23 | [ 0.30,  4.29] | 98.95% | 1.005 |  1137.00
Intercept[5]            |      3.06 | [ 1.12,  5.12] | 99.86% | 1.005 |  1138.00
Intercept[6]            |      3.95 | [ 2.01,  6.01] |   100% | 1.005 |  1139.00
Intercept[7]            |      5.04 | [ 3.10,  7.13] |   100% | 1.005 |  1138.00
Intercept[8]            |      6.17 | [ 4.23,  8.27] |   100% | 1.005 |  1151.00
Intercept[9]            |      7.56 | [ 5.60,  9.65] |   100% | 1.005 |  1178.00
Intercept[10]           |      8.82 | [ 6.84, 10.96] |   100% | 1.005 |  1200.00
Prev_ER_1_Dis           |      0.31 | [ 0.11,  0.52] | 99.85% | 1.000 |  8943.00
Prev_ER_2_Rum           |      0.03 | [-0.30,  0.38] | 57.37% | 1.000 |  6827.00
Prev_ER_3_SBl           |      0.09 | [-0.31,  0.44] | 67.13% | 1.000 |  8284.00
Prev_ER_4_ExprS         |     -0.03 | [-0.31,  0.26] | 58.07% | 1.000 | 12558.00
Prev_ER_5_ExperS        |      0.06 | [-0.19,  0.33] | 68.42% | 1.000 | 11962.00
Prev_ER_6_Acc           |      0.16 | [-0.05,  0.37] | 93.48% | 1.000 | 10601.00
Prev_ER_7_Pla           |  9.07e-03 | [-0.25,  0.26] | 52.84% | 1.000 |  9734.00
Prev_ER_8_Rea           |      0.19 | [-0.15,  0.53] | 87.05% | 1.000 | 10747.00
Prev_ER_9_ESu           |     -0.25 | [-0.59,  0.08] | 93.31% | 1.000 | 10425.00
Prev_ER_10_Rel          |     -0.04 | [-0.27,  0.19] | 64.23% | 1.000 | 10122.00
T1_Gender               |     -0.02 | [-0.84,  0.82] | 51.80% | 1.007 |   800.00
Age_2024                | -7.18e-03 | [-0.08,  0.06] | 57.91% | 1.004 |  1161.00
Prev_Str_Overall_Within |      0.42 | [ 0.35,  0.48] |   100% | 1.001 |  4504.00
Prev_MF                 |      0.15 | [ 0.08,  0.22] |   100% | 1.000 | 10315.00

2 * (1 - .9985)

[1] 0.003

2 * (1 - .5737)

[1] 0.8526

2 * (1 - .6713)

[1] 0.6574

2 * (1 - .5807)

[1] 0.8386

2 * (1 - .6842)

[1] 0.6316

2 * (1 - .9348)

[1] 0.1304

2 * (1 - .5284)

[1] 0.9432

2 * (1 - .8705)

[1] 0.259

2 * (1 - .9331)

[1] 0.1338

2 * (1 - .6423)

[1] 0.7154

2 * (1 - .5180)

[1] 0.964

2 * (1 - .5791)

[1] 0.8418

2 * (1 - 1)

[1] 0

2 * (1 - 1)

[1] 0

# Robustness analysis: restricting the sample to participants with ≥10 valid pairs

# d_10validpairs$Str_Overall <- ordered(d_10validpairs$Str_Overall)
# 
# if (rerun) {
#   start_time <- Sys.time()
#   print(start_time)
#   m_Stress_ERall_Cov4_10pairs <- brm(Str_Overall ~ Prev_ER_1_Dis + Prev_ER_2_Rum + Prev_ER_3_SBl + Prev_ER_4_ExprS
#                              + Prev_ER_5_ExperS + Prev_ER_6_Acc + Prev_ER_7_Pla + Prev_ER_8_Rea
#                              + Prev_ER_9_ESu + Prev_ER_10_Rel + T1_Gender + Age_2024 + Prev_Str_Overall_Within
#                              + Prev_MF + (1 | PID),
#                              family = cumulative(link = "logit", threshold = "flexible"), data = d_10validpairs,
#                              backend = "cmdstanr", chains = 4, iter = 4000, warmup = 1000, cores = 4)
#   saveRDS(m_Stress_ERall_Cov4_10pairs, file = "m_Stress_ERall_Cov4_10pairs.RDS")
#   end_time <- Sys.time()
#   print(end_time)
# }
m_Stress_ERall_Cov4_10pairs <- readRDS("m_Stress_ERall_Cov4_10pairs.RDS")
summary(m_Stress_ERall_Cov4_10pairs)

 Family: cumulative 
  Links: mu = logit; disc = identity 
Formula: Str_Overall ~ Prev_ER_1_Dis + Prev_ER_2_Rum + Prev_ER_3_SBl + Prev_ER_4_ExprS + Prev_ER_5_ExperS + Prev_ER_6_Acc + Prev_ER_7_Pla + Prev_ER_8_Rea + Prev_ER_9_ESu + Prev_ER_10_Rel + T1_Gender + Age_2024 + Prev_Str_Overall_Within + Prev_MF + (1 | PID) 
   Data: d_10validpairs (Number of observations: 6348) 
  Draws: 4 chains, each with iter = 4000; warmup = 1000; thin = 1;
         total post-warmup draws = 12000

Multilevel Hyperparameters:
~PID (Number of levels: 140) 
              Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
sd(Intercept)     2.31      0.15     2.04     2.63 1.00      757     1473

Regression Coefficients:
                        Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS
Intercept[1]               -1.08      1.13    -3.33     1.11 1.01      411
Intercept[2]                0.55      1.13    -1.70     2.73 1.01      412
Intercept[3]                1.73      1.13    -0.52     3.91 1.01      412
Intercept[4]                2.66      1.13     0.41     4.86 1.01      412
Intercept[5]                3.45      1.13     1.20     5.64 1.01      413
Intercept[6]                4.32      1.13     2.06     6.51 1.01      413
Intercept[7]                5.36      1.13     3.11     7.56 1.01      414
Intercept[8]                6.49      1.14     4.23     8.70 1.01      416
Intercept[9]                7.81      1.14     5.55    10.00 1.01      421
Intercept[10]               8.99      1.15     6.70    11.24 1.01      433
Prev_ER_1_Dis               0.29      0.09     0.12     0.47 1.00    10703
Prev_ER_2_Rum               0.00      0.14    -0.26     0.27 1.00     9980
Prev_ER_3_SBl               0.13      0.16    -0.17     0.44 1.00     9450
Prev_ER_4_ExprS            -0.12      0.13    -0.37     0.14 1.00    10236
Prev_ER_5_ExperS            0.05      0.12    -0.17     0.27 1.00    10670
Prev_ER_6_Acc               0.15      0.09    -0.02     0.32 1.00    12093
Prev_ER_7_Pla              -0.01      0.10    -0.21     0.19 1.00    10070
Prev_ER_8_Rea               0.11      0.14    -0.17     0.39 1.00    11519
Prev_ER_9_ESu              -0.17      0.13    -0.44     0.09 1.00    10570
Prev_ER_10_Rel             -0.04      0.10    -0.24     0.15 1.00    10261
T1_Gender                   0.10      0.45    -0.78     0.98 1.01      431
Age_2024                    0.00      0.04    -0.08     0.08 1.01      358
Prev_Str_Overall_Within     0.46      0.02     0.43     0.50 1.00     9651
Prev_MF                     0.14      0.03     0.08     0.21 1.00     8172
                        Tail_ESS
Intercept[1]                 896
Intercept[2]                 890
Intercept[3]                 888
Intercept[4]                 881
Intercept[5]                 884
Intercept[6]                 878
Intercept[7]                 883
Intercept[8]                 879
Intercept[9]                 869
Intercept[10]                920
Prev_ER_1_Dis               9796
Prev_ER_2_Rum               8600
Prev_ER_3_SBl               9231
Prev_ER_4_ExprS             8743
Prev_ER_5_ExperS            8873
Prev_ER_6_Acc               9465
Prev_ER_7_Pla               8657
Prev_ER_8_Rea               9975
Prev_ER_9_ESu               8921
Prev_ER_10_Rel              8646
T1_Gender                   1032
Age_2024                     856
Prev_Str_Overall_Within     9547
Prev_MF                     8654

Further Distributional Parameters:
     Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
disc     1.00      0.00     1.00     1.00   NA       NA       NA

Draws were sampled using sample(hmc). For each parameter, Bulk_ESS
and Tail_ESS are effective sample size measures, and Rhat is the potential
scale reduction factor on split chains (at convergence, Rhat = 1).

unique(d_10validpairs$Str_Overall)

 [1]  0 NA  1  4  3  2  7  6  5  8  9 10

####AIM 2 - To explore whether mental fatigue and stress predicts subsequent emotion regulation strategies####

# Sensitivity analysis: robustness to ±1 SD changes in time lag for the main result
# 
# rerun <- TRUE
# 
# if (rerun) {
#   Sys.time()
#   m_ERall_MF_Cov2_r2_time_diff_sensanalysis <- brm(mvbind(ER_1_Dis, ER_2_Rum, ER_3_SBl, ER_4_ExprS, 
#                                    ER_5_ExperS, ER_6_Acc, ER_7_Pla, ER_8_Rea,
#                                    ER_9_ESu, ER_10_Rel) ~ Prev_MF + T1_Gender + Age_2024 + 
#                               Prev_Str_Overall + (1 | p | PID),
#                             family = bernoulli(link = "logit"), data = d_timediff_subset,
#                             backend = "cmdstanr", chains = 4, iter = 4000, warmup = 1000, cores = 4)
#   saveRDS(m_ERall_MF_Cov2_r2_time_diff_sensanalysis, file = "m_ERall_MF_Cov2_r2_time_diff_sensanalysis.RDS")
#   Sys.time()
# }

m_ERall_MF_Cov2_r2_time_diff_sensanalysis <- readRDS("m_ERall_MF_Cov2_r2_time_diff_sensanalysis.RDS")
summary(m_ERall_MF_Cov2_r2_time_diff_sensanalysis)

 Family: MV(bernoulli, bernoulli, bernoulli, bernoulli, bernoulli, bernoulli, bernoulli, bernoulli, bernoulli, bernoulli) 
  Links: mu = logit
         mu = logit
         mu = logit
         mu = logit
         mu = logit
         mu = logit
         mu = logit
         mu = logit
         mu = logit
         mu = logit 
Formula: ER_1_Dis ~ Prev_MF + T1_Gender + Age_2024 + Prev_Str_Overall + (1 | p | PID) 
         ER_2_Rum ~ Prev_MF + T1_Gender + Age_2024 + Prev_Str_Overall + (1 | p | PID) 
         ER_3_SBl ~ Prev_MF + T1_Gender + Age_2024 + Prev_Str_Overall + (1 | p | PID) 
         ER_4_ExprS ~ Prev_MF + T1_Gender + Age_2024 + Prev_Str_Overall + (1 | p | PID) 
         ER_5_ExperS ~ Prev_MF + T1_Gender + Age_2024 + Prev_Str_Overall + (1 | p | PID) 
         ER_6_Acc ~ Prev_MF + T1_Gender + Age_2024 + Prev_Str_Overall + (1 | p | PID) 
         ER_7_Pla ~ Prev_MF + T1_Gender + Age_2024 + Prev_Str_Overall + (1 | p | PID) 
         ER_8_Rea ~ Prev_MF + T1_Gender + Age_2024 + Prev_Str_Overall + (1 | p | PID) 
         ER_9_ESu ~ Prev_MF + T1_Gender + Age_2024 + Prev_Str_Overall + (1 | p | PID) 
         ER_10_Rel ~ Prev_MF + T1_Gender + Age_2024 + Prev_Str_Overall + (1 | p | PID) 
   Data: d_timediff_subset (Number of observations: 4242) 
  Draws: 4 chains, each with iter = 4000; warmup = 1000; thin = 1;
         total post-warmup draws = 12000

Multilevel Hyperparameters:
~PID (Number of levels: 140) 
                                            Estimate Est.Error l-95% CI
sd(ER1Dis_Intercept)                            1.83      0.17     1.52
sd(ER2Rum_Intercept)                            1.57      0.23     1.17
sd(ER3SBl_Intercept)                            1.69      0.25     1.26
sd(ER4ExprS_Intercept)                          1.40      0.20     1.05
sd(ER5ExperS_Intercept)                         1.70      0.20     1.35
sd(ER6Acc_Intercept)                            1.45      0.14     1.20
sd(ER7Pla_Intercept)                            1.35      0.14     1.09
sd(ER8Rea_Intercept)                            1.55      0.22     1.16
sd(ER9ESu_Intercept)                            1.62      0.22     1.23
sd(ER10Rel_Intercept)                           1.81      0.18     1.50
cor(ER1Dis_Intercept,ER2Rum_Intercept)          0.35      0.12     0.11
cor(ER1Dis_Intercept,ER3SBl_Intercept)          0.33      0.12     0.08
cor(ER2Rum_Intercept,ER3SBl_Intercept)          0.68      0.11     0.44
cor(ER1Dis_Intercept,ER4ExprS_Intercept)        0.19      0.13    -0.05
cor(ER2Rum_Intercept,ER4ExprS_Intercept)        0.26      0.14    -0.01
cor(ER3SBl_Intercept,ER4ExprS_Intercept)        0.29      0.14     0.00
cor(ER1Dis_Intercept,ER5ExperS_Intercept)       0.44      0.10     0.22
cor(ER2Rum_Intercept,ER5ExperS_Intercept)       0.35      0.12     0.10
cor(ER3SBl_Intercept,ER5ExperS_Intercept)       0.42      0.12     0.16
cor(ER4ExprS_Intercept,ER5ExperS_Intercept)     0.77      0.08     0.58
cor(ER1Dis_Intercept,ER6Acc_Intercept)          0.34      0.10     0.14
cor(ER2Rum_Intercept,ER6Acc_Intercept)          0.35      0.11     0.12
cor(ER3SBl_Intercept,ER6Acc_Intercept)          0.30      0.12     0.06
cor(ER4ExprS_Intercept,ER6Acc_Intercept)        0.57      0.10     0.36
cor(ER5ExperS_Intercept,ER6Acc_Intercept)       0.53      0.10     0.33
cor(ER1Dis_Intercept,ER7Pla_Intercept)          0.42      0.10     0.22
cor(ER2Rum_Intercept,ER7Pla_Intercept)          0.42      0.12     0.17
cor(ER3SBl_Intercept,ER7Pla_Intercept)          0.32      0.13     0.05
cor(ER4ExprS_Intercept,ER7Pla_Intercept)        0.26      0.13    -0.00
cor(ER5ExperS_Intercept,ER7Pla_Intercept)       0.30      0.12     0.05
cor(ER6Acc_Intercept,ER7Pla_Intercept)          0.72      0.08     0.56
cor(ER1Dis_Intercept,ER8Rea_Intercept)          0.03      0.13    -0.22
cor(ER2Rum_Intercept,ER8Rea_Intercept)          0.39      0.13     0.13
cor(ER3SBl_Intercept,ER8Rea_Intercept)          0.29      0.14     0.01
cor(ER4ExprS_Intercept,ER8Rea_Intercept)        0.35      0.13     0.08
cor(ER5ExperS_Intercept,ER8Rea_Intercept)       0.16      0.13    -0.11
cor(ER6Acc_Intercept,ER8Rea_Intercept)          0.57      0.11     0.35
cor(ER7Pla_Intercept,ER8Rea_Intercept)          0.63      0.10     0.40
cor(ER1Dis_Intercept,ER9ESu_Intercept)          0.35      0.12     0.11
cor(ER2Rum_Intercept,ER9ESu_Intercept)          0.56      0.12     0.31
cor(ER3SBl_Intercept,ER9ESu_Intercept)          0.49      0.13     0.21
cor(ER4ExprS_Intercept,ER9ESu_Intercept)        0.24      0.14    -0.03
cor(ER5ExperS_Intercept,ER9ESu_Intercept)       0.30      0.13     0.03
cor(ER6Acc_Intercept,ER9ESu_Intercept)          0.64      0.09     0.43
cor(ER7Pla_Intercept,ER9ESu_Intercept)          0.53      0.11     0.30
cor(ER8Rea_Intercept,ER9ESu_Intercept)          0.56      0.12     0.31
cor(ER1Dis_Intercept,ER10Rel_Intercept)         0.58      0.08     0.41
cor(ER2Rum_Intercept,ER10Rel_Intercept)         0.24      0.12     0.00
cor(ER3SBl_Intercept,ER10Rel_Intercept)         0.23      0.13    -0.03
cor(ER4ExprS_Intercept,ER10Rel_Intercept)       0.10      0.13    -0.14
cor(ER5ExperS_Intercept,ER10Rel_Intercept)      0.27      0.11     0.05
cor(ER6Acc_Intercept,ER10Rel_Intercept)         0.36      0.10     0.15
cor(ER7Pla_Intercept,ER10Rel_Intercept)         0.41      0.10     0.20
cor(ER8Rea_Intercept,ER10Rel_Intercept)         0.20      0.12    -0.05
cor(ER9ESu_Intercept,ER10Rel_Intercept)         0.41      0.12     0.17
                                            u-95% CI Rhat Bulk_ESS Tail_ESS
sd(ER1Dis_Intercept)                            2.19 1.00     3893     7238
sd(ER2Rum_Intercept)                            2.05 1.00     5143     7653
sd(ER3SBl_Intercept)                            2.23 1.00     6771     8710
sd(ER4ExprS_Intercept)                          1.83 1.00     7349     9139
sd(ER5ExperS_Intercept)                         2.13 1.00     6923     9197
sd(ER6Acc_Intercept)                            1.76 1.00     8200     8916
sd(ER7Pla_Intercept)                            1.65 1.00     8521     9282
sd(ER8Rea_Intercept)                            2.03 1.00     7848     8880
sd(ER9ESu_Intercept)                            2.10 1.00     7328     9077
sd(ER10Rel_Intercept)                           2.19 1.00     6895     9146
cor(ER1Dis_Intercept,ER2Rum_Intercept)          0.57 1.00     6170     8529
cor(ER1Dis_Intercept,ER3SBl_Intercept)          0.56 1.00     7258     9203
cor(ER2Rum_Intercept,ER3SBl_Intercept)          0.86 1.00     4676     6708
cor(ER1Dis_Intercept,ER4ExprS_Intercept)        0.43 1.00     6570     8491
cor(ER2Rum_Intercept,ER4ExprS_Intercept)        0.52 1.00     3222     5728
cor(ER3SBl_Intercept,ER4ExprS_Intercept)        0.55 1.00     3421     6181
cor(ER1Dis_Intercept,ER5ExperS_Intercept)       0.63 1.00     6553     7770
cor(ER2Rum_Intercept,ER5ExperS_Intercept)       0.58 1.00     3344     6347
cor(ER3SBl_Intercept,ER5ExperS_Intercept)       0.64 1.00     3844     6465
cor(ER4ExprS_Intercept,ER5ExperS_Intercept)     0.90 1.00     5626     8064
cor(ER1Dis_Intercept,ER6Acc_Intercept)          0.53 1.00     5141     8336
cor(ER2Rum_Intercept,ER6Acc_Intercept)          0.57 1.00     2765     5798
cor(ER3SBl_Intercept,ER6Acc_Intercept)          0.53 1.00     2821     6251
cor(ER4ExprS_Intercept,ER6Acc_Intercept)        0.76 1.00     2609     5809
cor(ER5ExperS_Intercept,ER6Acc_Intercept)       0.71 1.00     4208     7749
cor(ER1Dis_Intercept,ER7Pla_Intercept)          0.61 1.00     6803     8508
cor(ER2Rum_Intercept,ER7Pla_Intercept)          0.64 1.00     4208     6986
cor(ER3SBl_Intercept,ER7Pla_Intercept)          0.56 1.00     3661     7006
cor(ER4ExprS_Intercept,ER7Pla_Intercept)        0.50 1.00     3236     7581
cor(ER5ExperS_Intercept,ER7Pla_Intercept)       0.52 1.00     4550     8089
cor(ER6Acc_Intercept,ER7Pla_Intercept)          0.85 1.00     7763     9949
cor(ER1Dis_Intercept,ER8Rea_Intercept)          0.28 1.00     8181     9649
cor(ER2Rum_Intercept,ER8Rea_Intercept)          0.63 1.00     4819     7820
cor(ER3SBl_Intercept,ER8Rea_Intercept)          0.55 1.00     5225     8224
cor(ER4ExprS_Intercept,ER8Rea_Intercept)        0.59 1.00     4384     7613
cor(ER5ExperS_Intercept,ER8Rea_Intercept)       0.41 1.00     6082     9103
cor(ER6Acc_Intercept,ER8Rea_Intercept)          0.75 1.00     8097     9417
cor(ER7Pla_Intercept,ER8Rea_Intercept)          0.81 1.00     8684    10728
cor(ER1Dis_Intercept,ER9ESu_Intercept)          0.57 1.00     7114     9223
cor(ER2Rum_Intercept,ER9ESu_Intercept)          0.76 1.00     3859     7985
cor(ER3SBl_Intercept,ER9ESu_Intercept)          0.73 1.00     3996     6912
cor(ER4ExprS_Intercept,ER9ESu_Intercept)        0.50 1.00     4899     8456
cor(ER5ExperS_Intercept,ER9ESu_Intercept)       0.54 1.00     4609     8093
cor(ER6Acc_Intercept,ER9ESu_Intercept)          0.80 1.00     8811    10090
cor(ER7Pla_Intercept,ER9ESu_Intercept)          0.73 1.00     8058     9699
cor(ER8Rea_Intercept,ER9ESu_Intercept)          0.77 1.00     7829     9090
cor(ER1Dis_Intercept,ER10Rel_Intercept)         0.73 1.00     6251     8844
cor(ER2Rum_Intercept,ER10Rel_Intercept)         0.48 1.00     4691     7336
cor(ER3SBl_Intercept,ER10Rel_Intercept)         0.47 1.00     4375     8242
cor(ER4ExprS_Intercept,ER10Rel_Intercept)       0.36 1.00     2828     6822
cor(ER5ExperS_Intercept,ER10Rel_Intercept)      0.49 1.00     5691     7887
cor(ER6Acc_Intercept,ER10Rel_Intercept)         0.54 1.00     7103     8485
cor(ER7Pla_Intercept,ER10Rel_Intercept)         0.59 1.00     6486     9488
cor(ER8Rea_Intercept,ER10Rel_Intercept)         0.43 1.00     4920     9295
cor(ER9ESu_Intercept,ER10Rel_Intercept)         0.62 1.00     4383     8062

Regression Coefficients:
                           Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS
ER1Dis_Intercept              -5.80      1.05    -7.93    -3.79 1.00     3233
ER2Rum_Intercept              -6.11      1.15    -8.45    -3.93 1.00     6685
ER3SBl_Intercept              -4.92      1.23    -7.37    -2.55 1.00     8096
ER4ExprS_Intercept            -5.69      1.06    -7.83    -3.65 1.00     7199
ER5ExperS_Intercept           -5.22      1.13    -7.49    -3.09 1.00     7354
ER6Acc_Intercept              -4.87      0.85    -6.59    -3.22 1.00     6117
ER7Pla_Intercept              -4.24      0.84    -5.91    -2.60 1.00     7016
ER8Rea_Intercept              -5.97      1.13    -8.20    -3.84 1.00     9779
ER9ESu_Intercept              -9.14      1.30   -11.82    -6.74 1.00     8656
ER10Rel_Intercept             -4.07      1.03    -6.13    -2.04 1.00     6683
ER1Dis_Prev_MF                 0.04      0.07    -0.10     0.19 1.00    16999
ER1Dis_T1_Gender               0.75      0.44    -0.10     1.62 1.00     4024
ER1Dis_Age_2024                0.07      0.03     0.00     0.14 1.00     2878
ER1Dis_Prev_Str_Overall        0.12      0.03     0.05     0.19 1.00    15199
ER2Rum_Prev_MF                -0.06      0.11    -0.27     0.14 1.00    17242
ER2Rum_T1_Gender               0.21      0.46    -0.70     1.12 1.00     6403
ER2Rum_Age_2024                0.04      0.04    -0.03     0.12 1.00     6252
ER2Rum_Prev_Str_Overall        0.21      0.05     0.11     0.31 1.00    12289
ER3SBl_Prev_MF                 0.10      0.11    -0.13     0.32 1.00    20221
ER3SBl_T1_Gender              -0.06      0.50    -1.03     0.94 1.00     6936
ER3SBl_Age_2024               -0.01      0.04    -0.09     0.07 1.00     7696
ER3SBl_Prev_Str_Overall        0.11      0.05     0.01     0.22 1.00    19574
ER4ExprS_Prev_MF              -0.21      0.12    -0.45     0.02 1.00    18258
ER4ExprS_T1_Gender             0.22      0.41    -0.58     1.03 1.00     7935
ER4ExprS_Age_2024              0.05      0.03    -0.02     0.12 1.00     6913
ER4ExprS_Prev_Str_Overall      0.17      0.05     0.07     0.26 1.00    15598
ER5ExperS_Prev_MF             -0.12      0.10    -0.33     0.08 1.00    19677
ER5ExperS_T1_Gender            0.11      0.45    -0.77     1.00 1.00     7009
ER5ExperS_Age_2024             0.03      0.04    -0.04     0.11 1.00     6778
ER5ExperS_Prev_Str_Overall     0.17      0.04     0.08     0.25 1.00    18901
ER6Acc_Prev_MF                 0.02      0.07    -0.12     0.16 1.00    17978
ER6Acc_T1_Gender               0.38      0.36    -0.32     1.10 1.00     6038
ER6Acc_Age_2024                0.05      0.03    -0.01     0.10 1.00     5662
ER6Acc_Prev_Str_Overall        0.11      0.03     0.05     0.17 1.00    19977
ER7Pla_Prev_MF                -0.09      0.08    -0.24     0.07 1.00    19541
ER7Pla_T1_Gender               1.02      0.37     0.29     1.76 1.00     7196
ER7Pla_Age_2024                0.00      0.03    -0.05     0.06 1.00     6238
ER7Pla_Prev_Str_Overall        0.13      0.03     0.06     0.19 1.00    19907
ER8Rea_Prev_MF                 0.07      0.11    -0.15     0.27 1.00    22585
ER8Rea_T1_Gender               0.45      0.47    -0.47     1.38 1.00     9350
ER8Rea_Age_2024                0.02      0.04    -0.05     0.09 1.00     9053
ER8Rea_Prev_Str_Overall        0.15      0.05     0.07     0.24 1.00    23540
ER9ESu_Prev_MF                 0.07      0.10    -0.12     0.26 1.00    21128
ER9ESu_T1_Gender               2.77      0.71     1.51     4.28 1.00    10636
ER9ESu_Age_2024                0.07      0.04    -0.00     0.14 1.00     7720
ER9ESu_Prev_Str_Overall        0.13      0.05     0.04     0.22 1.00    20315
ER10Rel_Prev_MF                0.02      0.07    -0.13     0.17 1.00    22504
ER10Rel_T1_Gender              0.94      0.45     0.07     1.82 1.00     7250
ER10Rel_Age_2024               0.01      0.04    -0.06     0.08 1.00     6051
ER10Rel_Prev_Str_Overall      -0.04      0.04    -0.11     0.03 1.00    20845
                           Tail_ESS
ER1Dis_Intercept               6287
ER2Rum_Intercept               8528
ER3SBl_Intercept               9370
ER4ExprS_Intercept             8844
ER5ExperS_Intercept            9156
ER6Acc_Intercept               8681
ER7Pla_Intercept               8863
ER8Rea_Intercept               9737
ER9ESu_Intercept               8949
ER10Rel_Intercept              8185
ER1Dis_Prev_MF                 9590
ER1Dis_T1_Gender               6040
ER1Dis_Age_2024                5541
ER1Dis_Prev_Str_Overall        9469
ER2Rum_Prev_MF                 8916
ER2Rum_T1_Gender               8091
ER2Rum_Age_2024                8116
ER2Rum_Prev_Str_Overall        9315
ER3SBl_Prev_MF                 9192
ER3SBl_T1_Gender               8261
ER3SBl_Age_2024                9025
ER3SBl_Prev_Str_Overall        8841
ER4ExprS_Prev_MF               9701
ER4ExprS_T1_Gender             9416
ER4ExprS_Age_2024              8356
ER4ExprS_Prev_Str_Overall      9795
ER5ExperS_Prev_MF              9166
ER5ExperS_T1_Gender            7776
ER5ExperS_Age_2024             8591
ER5ExperS_Prev_Str_Overall     8907
ER6Acc_Prev_MF                 9683
ER6Acc_T1_Gender               7564
ER6Acc_Age_2024                8303
ER6Acc_Prev_Str_Overall        9135
ER7Pla_Prev_MF                 9250
ER7Pla_T1_Gender               8757
ER7Pla_Age_2024                8580
ER7Pla_Prev_Str_Overall        9894
ER8Rea_Prev_MF                 9935
ER8Rea_T1_Gender               9030
ER8Rea_Age_2024                9345
ER8Rea_Prev_Str_Overall        9405
ER9ESu_Prev_MF                 9529
ER9ESu_T1_Gender               8793
ER9ESu_Age_2024                9338
ER9ESu_Prev_Str_Overall        9161
ER10Rel_Prev_MF                8437
ER10Rel_T1_Gender              8388
ER10Rel_Age_2024               7731
ER10Rel_Prev_Str_Overall       9589

Draws were sampled using sample(hmc). For each parameter, Bulk_ESS
and Tail_ESS are effective sample size measures, and Rhat is the potential
scale reduction factor on split chains (at convergence, Rhat = 1).

exp(fixef(m_ERall_MF_Cov2_r2_time_diff_sensanalysis))

                               Estimate Est.Error         Q2.5       Q97.5
ER1Dis_Intercept           3.019459e-03  2.854551 3.581462e-04  0.02257684
ER2Rum_Intercept           2.229037e-03  3.167408 2.148887e-04  0.01958288
ER3SBl_Intercept           7.264236e-03  3.413489 6.303874e-04  0.07800392
ER4ExprS_Intercept         3.363961e-03  2.894476 3.958049e-04  0.02600312
ER5ExperS_Intercept        5.419660e-03  3.085102 5.565755e-04  0.04552786
ER6Acc_Intercept           7.710532e-03  2.349784 1.368538e-03  0.04000844
ER7Pla_Intercept           1.443943e-02  2.315331 2.723166e-03  0.07432009
ER8Rea_Intercept           2.564369e-03  3.105147 2.732932e-04  0.02156123
ER9ESu_Intercept           1.071543e-04  3.654724 7.355516e-06  0.00118244
ER10Rel_Intercept          1.712145e-02  2.802672 2.171603e-03  0.13009325
ER1Dis_Prev_MF             1.045688e+00  1.077501 9.031948e-01  1.21112060
ER1Dis_T1_Gender           2.126548e+00  1.555658 9.013469e-01  5.04543564
ER1Dis_Age_2024            1.071112e+00  1.035279 1.000174e+00  1.14719740
ER1Dis_Prev_Str_Overall    1.129251e+00  1.035588 1.054733e+00  1.20837043
ER2Rum_Prev_MF             9.409458e-01  1.111780 7.622657e-01  1.15498063
ER2Rum_T1_Gender           1.228329e+00  1.589976 4.951327e-01  3.05869468
ER2Rum_Age_2024            1.041729e+00  1.038284 9.688662e-01  1.12324490
ER2Rum_Prev_Str_Overall    1.231006e+00  1.050983 1.115447e+00  1.35805873
ER3SBl_Prev_MF             1.100545e+00  1.121145 8.789079e-01  1.37755350
ER3SBl_T1_Gender           9.390566e-01  1.649659 3.563670e-01  2.56034464
ER3SBl_Age_2024            9.897022e-01  1.041597 9.129078e-01  1.07181422
ER3SBl_Prev_Str_Overall    1.120747e+00  1.054712 1.008393e+00  1.24232190
ER4ExprS_Prev_MF           8.140942e-01  1.126202 6.405109e-01  1.02257614
ER4ExprS_T1_Gender         1.241314e+00  1.507130 5.617483e-01  2.81191642
ER4ExprS_Age_2024          1.048894e+00  1.034517 9.812050e-01  1.12235741
ER4ExprS_Prev_Str_Overall  1.182241e+00  1.048476 1.075535e+00  1.29704879
ER5ExperS_Prev_MF          8.836602e-01  1.108400 7.205993e-01  1.07929773
ER5ExperS_T1_Gender        1.115847e+00  1.571456 4.616804e-01  2.70477228
ER5ExperS_Age_2024         1.034372e+00  1.037575 9.637307e-01  1.11313847
ER5ExperS_Prev_Str_Overall 1.183194e+00  1.044921 1.084391e+00  1.28779129
ER6Acc_Prev_MF             1.020332e+00  1.073508 8.888775e-01  1.17241072
ER6Acc_T1_Gender           1.468754e+00  1.431668 7.293173e-01  3.01199003
ER6Acc_Age_2024            1.049331e+00  1.028927 9.922555e-01  1.10981061
ER6Acc_Prev_Str_Overall    1.115851e+00  1.032935 1.046769e+00  1.18938893
ER7Pla_Prev_MF             9.162329e-01  1.081899 7.846115e-01  1.06817727
ER7Pla_T1_Gender           2.762234e+00  1.451117 1.342284e+00  5.79520586
ER7Pla_Age_2024            1.001785e+00  1.028368 9.476573e-01  1.05788287
ER7Pla_Prev_Str_Overall    1.135200e+00  1.035319 1.058997e+00  1.21375286
ER8Rea_Prev_MF             1.067366e+00  1.113539 8.632668e-01  1.31341862
ER8Rea_T1_Gender           1.566184e+00  1.603114 6.244022e-01  3.98065051
ER8Rea_Age_2024            1.021877e+00  1.037127 9.514985e-01  1.09790800
ER8Rea_Prev_Str_Overall    1.167394e+00  1.046617 1.067901e+00  1.27545056
ER9ESu_Prev_MF             1.074358e+00  1.103052 8.857739e-01  1.29718872
ER9ESu_T1_Gender           1.598514e+01  2.032921 4.506753e+00 71.88067841
ER9ESu_Age_2024            1.071254e+00  1.038191 9.957399e-01  1.15296213
ER9ESu_Prev_Str_Overall    1.142173e+00  1.046416 1.044779e+00  1.24861764
ER10Rel_Prev_MF            1.019891e+00  1.077434 8.794653e-01  1.17945923
ER10Rel_T1_Gender          2.552194e+00  1.561027 1.073182e+00  6.17171958
ER10Rel_Age_2024           1.007710e+00  1.035696 9.392182e-01  1.07821658
ER10Rel_Prev_Str_Overall   9.617651e-01  1.037678 8.933638e-01  1.03358451

# Adding random slopes to check model convergence and robustness of results
# rerun <- TRUE
# 
# if (rerun) {
#   Sys.time()
#   m_ERall_MF_final_randomslopes1 <- brm(mvbind(ER_1_Dis, ER_2_Rum, ER_3_SBl, ER_4_ExprS, 
#                                  ER_5_ExperS, ER_6_Acc, ER_7_Pla, ER_8_Rea,
#                                  ER_9_ESu, ER_10_Rel) ~ Prev_MF + T1_Gender + Age_2024 + 
#                             Prev_Str_Overall + (1 + Prev_MF + Prev_Str_Overall | p | PID),
#                           family = bernoulli(link = "logit"), data = d,
#                           backend = "cmdstanr", chains = 4, iter = 4000, warmup = 1000, cores = 4)
#   saveRDS(m_ERall_MF_final_randomslopes1, file = "m_ERall_MF_final_randomslopes1.RDS")
#   Sys.time()
# }
m_ERall_MF_final_randomslopes1 <- readRDS("m_ERall_MF_final_randomslopes1.RDS")
summary(m_ERall_MF_final_randomslopes1)

Warning: There were 1 divergent transitions after warmup. Increasing
adapt_delta above 0.8 may help. See
http://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup

 Family: MV(bernoulli, bernoulli, bernoulli, bernoulli, bernoulli, bernoulli, bernoulli, bernoulli, bernoulli, bernoulli) 
  Links: mu = logit
         mu = logit
         mu = logit
         mu = logit
         mu = logit
         mu = logit
         mu = logit
         mu = logit
         mu = logit
         mu = logit 
Formula: ER_1_Dis ~ Prev_MF + T1_Gender + Age_2024 + Prev_Str_Overall + (1 + Prev_MF + Prev_Str_Overall | p | PID) 
         ER_2_Rum ~ Prev_MF + T1_Gender + Age_2024 + Prev_Str_Overall + (1 + Prev_MF + Prev_Str_Overall | p | PID) 
         ER_3_SBl ~ Prev_MF + T1_Gender + Age_2024 + Prev_Str_Overall + (1 + Prev_MF + Prev_Str_Overall | p | PID) 
         ER_4_ExprS ~ Prev_MF + T1_Gender + Age_2024 + Prev_Str_Overall + (1 + Prev_MF + Prev_Str_Overall | p | PID) 
         ER_5_ExperS ~ Prev_MF + T1_Gender + Age_2024 + Prev_Str_Overall + (1 + Prev_MF + Prev_Str_Overall | p | PID) 
         ER_6_Acc ~ Prev_MF + T1_Gender + Age_2024 + Prev_Str_Overall + (1 + Prev_MF + Prev_Str_Overall | p | PID) 
         ER_7_Pla ~ Prev_MF + T1_Gender + Age_2024 + Prev_Str_Overall + (1 + Prev_MF + Prev_Str_Overall | p | PID) 
         ER_8_Rea ~ Prev_MF + T1_Gender + Age_2024 + Prev_Str_Overall + (1 + Prev_MF + Prev_Str_Overall | p | PID) 
         ER_9_ESu ~ Prev_MF + T1_Gender + Age_2024 + Prev_Str_Overall + (1 + Prev_MF + Prev_Str_Overall | p | PID) 
         ER_10_Rel ~ Prev_MF + T1_Gender + Age_2024 + Prev_Str_Overall + (1 + Prev_MF + Prev_Str_Overall | p | PID) 
   Data: d (Number of observations: 6497) 
  Draws: 4 chains, each with iter = 4000; warmup = 1000; thin = 1;
         total post-warmup draws = 12000

Multilevel Hyperparameters:
~PID (Number of levels: 179) 
                                                          Estimate Est.Error
sd(ER1Dis_Intercept)                                          1.68      0.19
sd(ER1Dis_Prev_MF)                                            0.29      0.11
sd(ER1Dis_Prev_Str_Overall)                                   0.25      0.05
sd(ER2Rum_Intercept)                                          1.31      0.21
sd(ER2Rum_Prev_MF)                                            0.12      0.09
sd(ER2Rum_Prev_Str_Overall)                                   0.20      0.07
sd(ER3SBl_Intercept)                                          1.51      0.25
sd(ER3SBl_Prev_MF)                                            0.21      0.13
sd(ER3SBl_Prev_Str_Overall)                                   0.08      0.06
sd(ER4ExprS_Intercept)                                        1.30      0.17
sd(ER4ExprS_Prev_MF)                                          0.11      0.08
sd(ER4ExprS_Prev_Str_Overall)                                 0.12      0.07
sd(ER5ExperS_Intercept)                                       1.70      0.20
sd(ER5ExperS_Prev_MF)                                         0.10      0.08
sd(ER5ExperS_Prev_Str_Overall)                                0.22      0.06
sd(ER6Acc_Intercept)                                          1.26      0.12
sd(ER6Acc_Prev_MF)                                            0.12      0.08
sd(ER6Acc_Prev_Str_Overall)                                   0.05      0.03
sd(ER7Pla_Intercept)                                          1.25      0.14
sd(ER7Pla_Prev_MF)                                            0.21      0.09
sd(ER7Pla_Prev_Str_Overall)                                   0.06      0.04
sd(ER8Rea_Intercept)                                          1.36      0.25
sd(ER8Rea_Prev_MF)                                            0.31      0.15
sd(ER8Rea_Prev_Str_Overall)                                   0.06      0.05
sd(ER9ESu_Intercept)                                          1.47      0.22
sd(ER9ESu_Prev_MF)                                            0.29      0.14
sd(ER9ESu_Prev_Str_Overall)                                   0.12      0.07
sd(ER10Rel_Intercept)                                         1.72      0.17
sd(ER10Rel_Prev_MF)                                           0.21      0.12
sd(ER10Rel_Prev_Str_Overall)                                  0.15      0.05
cor(ER1Dis_Intercept,ER1Dis_Prev_MF)                         -0.05      0.16
cor(ER1Dis_Intercept,ER1Dis_Prev_Str_Overall)                -0.11      0.14
cor(ER1Dis_Prev_MF,ER1Dis_Prev_Str_Overall)                  -0.07      0.16
cor(ER1Dis_Intercept,ER2Rum_Intercept)                        0.12      0.12
cor(ER1Dis_Prev_MF,ER2Rum_Intercept)                          0.19      0.15
cor(ER1Dis_Prev_Str_Overall,ER2Rum_Intercept)                 0.05      0.14
cor(ER1Dis_Intercept,ER2Rum_Prev_MF)                          0.01      0.18
cor(ER1Dis_Prev_MF,ER2Rum_Prev_MF)                            0.05      0.18
cor(ER1Dis_Prev_Str_Overall,ER2Rum_Prev_MF)                   0.04      0.18
cor(ER2Rum_Intercept,ER2Rum_Prev_MF)                         -0.01      0.18
cor(ER1Dis_Intercept,ER2Rum_Prev_Str_Overall)                -0.01      0.16
cor(ER1Dis_Prev_MF,ER2Rum_Prev_Str_Overall)                   0.07      0.17
cor(ER1Dis_Prev_Str_Overall,ER2Rum_Prev_Str_Overall)          0.06      0.16
cor(ER2Rum_Intercept,ER2Rum_Prev_Str_Overall)                 0.07      0.16
cor(ER2Rum_Prev_MF,ER2Rum_Prev_Str_Overall)                  -0.02      0.18
cor(ER1Dis_Intercept,ER3SBl_Intercept)                        0.15      0.12
cor(ER1Dis_Prev_MF,ER3SBl_Intercept)                          0.15      0.15
cor(ER1Dis_Prev_Str_Overall,ER3SBl_Intercept)                -0.09      0.14
cor(ER2Rum_Intercept,ER3SBl_Intercept)                        0.48      0.11
cor(ER2Rum_Prev_MF,ER3SBl_Intercept)                          0.08      0.19
cor(ER2Rum_Prev_Str_Overall,ER3SBl_Intercept)                 0.12      0.16
cor(ER1Dis_Intercept,ER3SBl_Prev_MF)                          0.02      0.17
cor(ER1Dis_Prev_MF,ER3SBl_Prev_MF)                            0.02      0.17
cor(ER1Dis_Prev_Str_Overall,ER3SBl_Prev_MF)                  -0.01      0.17
cor(ER2Rum_Intercept,ER3SBl_Prev_MF)                          0.13      0.18
cor(ER2Rum_Prev_MF,ER3SBl_Prev_MF)                            0.04      0.18
cor(ER2Rum_Prev_Str_Overall,ER3SBl_Prev_MF)                  -0.00      0.18
cor(ER3SBl_Intercept,ER3SBl_Prev_MF)                          0.00      0.18
cor(ER1Dis_Intercept,ER3SBl_Prev_Str_Overall)                 0.05      0.18
cor(ER1Dis_Prev_MF,ER3SBl_Prev_Str_Overall)                   0.02      0.18
cor(ER1Dis_Prev_Str_Overall,ER3SBl_Prev_Str_Overall)          0.03      0.17
cor(ER2Rum_Intercept,ER3SBl_Prev_Str_Overall)                 0.07      0.18
cor(ER2Rum_Prev_MF,ER3SBl_Prev_Str_Overall)                   0.02      0.18
cor(ER2Rum_Prev_Str_Overall,ER3SBl_Prev_Str_Overall)          0.04      0.18
cor(ER3SBl_Intercept,ER3SBl_Prev_Str_Overall)                -0.01      0.18
cor(ER3SBl_Prev_MF,ER3SBl_Prev_Str_Overall)                  -0.01      0.18
cor(ER1Dis_Intercept,ER4ExprS_Intercept)                      0.10      0.12
cor(ER1Dis_Prev_MF,ER4ExprS_Intercept)                        0.10      0.15
cor(ER1Dis_Prev_Str_Overall,ER4ExprS_Intercept)               0.06      0.14
cor(ER2Rum_Intercept,ER4ExprS_Intercept)                      0.20      0.12
cor(ER2Rum_Prev_MF,ER4ExprS_Intercept)                        0.04      0.18
cor(ER2Rum_Prev_Str_Overall,ER4ExprS_Intercept)               0.04      0.15
cor(ER3SBl_Intercept,ER4ExprS_Intercept)                      0.26      0.12
cor(ER3SBl_Prev_MF,ER4ExprS_Intercept)                        0.05      0.17
cor(ER3SBl_Prev_Str_Overall,ER4ExprS_Intercept)              -0.05      0.18
cor(ER1Dis_Intercept,ER4ExprS_Prev_MF)                        0.01      0.18
cor(ER1Dis_Prev_MF,ER4ExprS_Prev_MF)                          0.03      0.18
cor(ER1Dis_Prev_Str_Overall,ER4ExprS_Prev_MF)                 0.03      0.18
cor(ER2Rum_Intercept,ER4ExprS_Prev_MF)                        0.02      0.18
cor(ER2Rum_Prev_MF,ER4ExprS_Prev_MF)                          0.02      0.18
cor(ER2Rum_Prev_Str_Overall,ER4ExprS_Prev_MF)                 0.00      0.18
cor(ER3SBl_Intercept,ER4ExprS_Prev_MF)                        0.03      0.18
cor(ER3SBl_Prev_MF,ER4ExprS_Prev_MF)                          0.01      0.18
cor(ER3SBl_Prev_Str_Overall,ER4ExprS_Prev_MF)                -0.00      0.18
cor(ER4ExprS_Intercept,ER4ExprS_Prev_MF)                     -0.05      0.18
cor(ER1Dis_Intercept,ER4ExprS_Prev_Str_Overall)               0.05      0.17
cor(ER1Dis_Prev_MF,ER4ExprS_Prev_Str_Overall)                -0.00      0.17
cor(ER1Dis_Prev_Str_Overall,ER4ExprS_Prev_Str_Overall)        0.16      0.18
cor(ER2Rum_Intercept,ER4ExprS_Prev_Str_Overall)              -0.07      0.16
cor(ER2Rum_Prev_MF,ER4ExprS_Prev_Str_Overall)                -0.01      0.18
cor(ER2Rum_Prev_Str_Overall,ER4ExprS_Prev_Str_Overall)        0.07      0.18
cor(ER3SBl_Intercept,ER4ExprS_Prev_Str_Overall)              -0.06      0.16
cor(ER3SBl_Prev_MF,ER4ExprS_Prev_Str_Overall)                -0.02      0.18
cor(ER3SBl_Prev_Str_Overall,ER4ExprS_Prev_Str_Overall)        0.02      0.18
cor(ER4ExprS_Intercept,ER4ExprS_Prev_Str_Overall)            -0.12      0.17
cor(ER4ExprS_Prev_MF,ER4ExprS_Prev_Str_Overall)              -0.03      0.18
cor(ER1Dis_Intercept,ER5ExperS_Intercept)                     0.23      0.11
cor(ER1Dis_Prev_MF,ER5ExperS_Intercept)                       0.13      0.15
cor(ER1Dis_Prev_Str_Overall,ER5ExperS_Intercept)              0.04      0.13
cor(ER2Rum_Intercept,ER5ExperS_Intercept)                     0.30      0.12
cor(ER2Rum_Prev_MF,ER5ExperS_Intercept)                       0.07      0.18
cor(ER2Rum_Prev_Str_Overall,ER5ExperS_Intercept)             -0.02      0.15
cor(ER3SBl_Intercept,ER5ExperS_Intercept)                     0.30      0.12
cor(ER3SBl_Prev_MF,ER5ExperS_Intercept)                       0.09      0.17
cor(ER3SBl_Prev_Str_Overall,ER5ExperS_Intercept)              0.01      0.17
cor(ER4ExprS_Intercept,ER5ExperS_Intercept)                   0.59      0.10
cor(ER4ExprS_Prev_MF,ER5ExperS_Intercept)                     0.06      0.18
cor(ER4ExprS_Prev_Str_Overall,ER5ExperS_Intercept)           -0.01      0.16
cor(ER1Dis_Intercept,ER5ExperS_Prev_MF)                       0.01      0.18
cor(ER1Dis_Prev_MF,ER5ExperS_Prev_MF)                         0.02      0.18
cor(ER1Dis_Prev_Str_Overall,ER5ExperS_Prev_MF)                0.01      0.18
cor(ER2Rum_Intercept,ER5ExperS_Prev_MF)                      -0.00      0.18
cor(ER2Rum_Prev_MF,ER5ExperS_Prev_MF)                         0.01      0.18
cor(ER2Rum_Prev_Str_Overall,ER5ExperS_Prev_MF)                0.01      0.18
cor(ER3SBl_Intercept,ER5ExperS_Prev_MF)                       0.02      0.18
cor(ER3SBl_Prev_MF,ER5ExperS_Prev_MF)                         0.01      0.18
cor(ER3SBl_Prev_Str_Overall,ER5ExperS_Prev_MF)               -0.00      0.18
cor(ER4ExprS_Intercept,ER5ExperS_Prev_MF)                     0.06      0.18
cor(ER4ExprS_Prev_MF,ER5ExperS_Prev_MF)                       0.02      0.18
cor(ER4ExprS_Prev_Str_Overall,ER5ExperS_Prev_MF)              0.01      0.18
cor(ER5ExperS_Intercept,ER5ExperS_Prev_MF)                   -0.03      0.18
cor(ER1Dis_Intercept,ER5ExperS_Prev_Str_Overall)             -0.09      0.15
cor(ER1Dis_Prev_MF,ER5ExperS_Prev_Str_Overall)               -0.04      0.17
cor(ER1Dis_Prev_Str_Overall,ER5ExperS_Prev_Str_Overall)       0.11      0.16
cor(ER2Rum_Intercept,ER5ExperS_Prev_Str_Overall)             -0.11      0.15
cor(ER2Rum_Prev_MF,ER5ExperS_Prev_Str_Overall)               -0.03      0.18
cor(ER2Rum_Prev_Str_Overall,ER5ExperS_Prev_Str_Overall)       0.15      0.16
cor(ER3SBl_Intercept,ER5ExperS_Prev_Str_Overall)             -0.02      0.15
cor(ER3SBl_Prev_MF,ER5ExperS_Prev_Str_Overall)               -0.03      0.17
cor(ER3SBl_Prev_Str_Overall,ER5ExperS_Prev_Str_Overall)      -0.01      0.18
cor(ER4ExprS_Intercept,ER5ExperS_Prev_Str_Overall)            0.11      0.16
cor(ER4ExprS_Prev_MF,ER5ExperS_Prev_Str_Overall)              0.02      0.18
cor(ER4ExprS_Prev_Str_Overall,ER5ExperS_Prev_Str_Overall)     0.14      0.18
cor(ER5ExperS_Intercept,ER5ExperS_Prev_Str_Overall)          -0.10      0.15
cor(ER5ExperS_Prev_MF,ER5ExperS_Prev_Str_Overall)             0.00      0.18
cor(ER1Dis_Intercept,ER6Acc_Intercept)                        0.13      0.10
cor(ER1Dis_Prev_MF,ER6Acc_Intercept)                          0.05      0.15
cor(ER1Dis_Prev_Str_Overall,ER6Acc_Intercept)                 0.07      0.13
cor(ER2Rum_Intercept,ER6Acc_Intercept)                        0.29      0.11
cor(ER2Rum_Prev_MF,ER6Acc_Intercept)                          0.00      0.17
cor(ER2Rum_Prev_Str_Overall,ER6Acc_Intercept)                 0.06      0.15
cor(ER3SBl_Intercept,ER6Acc_Intercept)                        0.25      0.12
cor(ER3SBl_Prev_MF,ER6Acc_Intercept)                          0.05      0.17
cor(ER3SBl_Prev_Str_Overall,ER6Acc_Intercept)                -0.03      0.17
cor(ER4ExprS_Intercept,ER6Acc_Intercept)                      0.48      0.10
cor(ER4ExprS_Prev_MF,ER6Acc_Intercept)                        0.01      0.18
cor(ER4ExprS_Prev_Str_Overall,ER6Acc_Intercept)              -0.02      0.16
cor(ER5ExperS_Intercept,ER6Acc_Intercept)                     0.39      0.10
cor(ER5ExperS_Prev_MF,ER6Acc_Intercept)                       0.02      0.18
cor(ER5ExperS_Prev_Str_Overall,ER6Acc_Intercept)              0.09      0.15
cor(ER1Dis_Intercept,ER6Acc_Prev_MF)                          0.00      0.17
cor(ER1Dis_Prev_MF,ER6Acc_Prev_MF)                            0.01      0.18
cor(ER1Dis_Prev_Str_Overall,ER6Acc_Prev_MF)                   0.03      0.18
cor(ER2Rum_Intercept,ER6Acc_Prev_MF)                         -0.00      0.17
cor(ER2Rum_Prev_MF,ER6Acc_Prev_MF)                            0.00      0.18
cor(ER2Rum_Prev_Str_Overall,ER6Acc_Prev_MF)                   0.03      0.18
cor(ER3SBl_Intercept,ER6Acc_Prev_MF)                         -0.04      0.17
cor(ER3SBl_Prev_MF,ER6Acc_Prev_MF)                           -0.02      0.18
cor(ER3SBl_Prev_Str_Overall,ER6Acc_Prev_MF)                  -0.01      0.18
cor(ER4ExprS_Intercept,ER6Acc_Prev_MF)                        0.02      0.17
cor(ER4ExprS_Prev_MF,ER6Acc_Prev_MF)                          0.01      0.18
cor(ER4ExprS_Prev_Str_Overall,ER6Acc_Prev_MF)                 0.01      0.18
cor(ER5ExperS_Intercept,ER6Acc_Prev_MF)                      -0.03      0.17
cor(ER5ExperS_Prev_MF,ER6Acc_Prev_MF)                         0.01      0.18
cor(ER5ExperS_Prev_Str_Overall,ER6Acc_Prev_MF)                0.03      0.18
cor(ER6Acc_Intercept,ER6Acc_Prev_MF)                         -0.09      0.18
cor(ER1Dis_Intercept,ER6Acc_Prev_Str_Overall)                 0.01      0.17
cor(ER1Dis_Prev_MF,ER6Acc_Prev_Str_Overall)                  -0.02      0.18
cor(ER1Dis_Prev_Str_Overall,ER6Acc_Prev_Str_Overall)          0.08      0.18
cor(ER2Rum_Intercept,ER6Acc_Prev_Str_Overall)                 0.01      0.18
cor(ER2Rum_Prev_MF,ER6Acc_Prev_Str_Overall)                   0.00      0.18
cor(ER2Rum_Prev_Str_Overall,ER6Acc_Prev_Str_Overall)          0.05      0.18
cor(ER3SBl_Intercept,ER6Acc_Prev_Str_Overall)                -0.02      0.18
cor(ER3SBl_Prev_MF,ER6Acc_Prev_Str_Overall)                  -0.01      0.18
cor(ER3SBl_Prev_Str_Overall,ER6Acc_Prev_Str_Overall)          0.01      0.18
cor(ER4ExprS_Intercept,ER6Acc_Prev_Str_Overall)              -0.02      0.18
cor(ER4ExprS_Prev_MF,ER6Acc_Prev_Str_Overall)                -0.00      0.18
cor(ER4ExprS_Prev_Str_Overall,ER6Acc_Prev_Str_Overall)        0.05      0.18
cor(ER5ExperS_Intercept,ER6Acc_Prev_Str_Overall)             -0.02      0.18
cor(ER5ExperS_Prev_MF,ER6Acc_Prev_Str_Overall)                0.00      0.18
cor(ER5ExperS_Prev_Str_Overall,ER6Acc_Prev_Str_Overall)       0.04      0.18
cor(ER6Acc_Intercept,ER6Acc_Prev_Str_Overall)                -0.02      0.17
cor(ER6Acc_Prev_MF,ER6Acc_Prev_Str_Overall)                   0.00      0.18
cor(ER1Dis_Intercept,ER7Pla_Intercept)                        0.29      0.11
cor(ER1Dis_Prev_MF,ER7Pla_Intercept)                          0.08      0.15
cor(ER1Dis_Prev_Str_Overall,ER7Pla_Intercept)                -0.06      0.14
cor(ER2Rum_Intercept,ER7Pla_Intercept)                        0.32      0.12
cor(ER2Rum_Prev_MF,ER7Pla_Intercept)                         -0.01      0.17
cor(ER2Rum_Prev_Str_Overall,ER7Pla_Intercept)                 0.08      0.15
cor(ER3SBl_Intercept,ER7Pla_Intercept)                        0.23      0.12
cor(ER3SBl_Prev_MF,ER7Pla_Intercept)                          0.03      0.17
cor(ER3SBl_Prev_Str_Overall,ER7Pla_Intercept)                -0.01      0.17
cor(ER4ExprS_Intercept,ER7Pla_Intercept)                      0.26      0.11
cor(ER4ExprS_Prev_MF,ER7Pla_Intercept)                       -0.01      0.18
cor(ER4ExprS_Prev_Str_Overall,ER7Pla_Intercept)              -0.05      0.16
cor(ER5ExperS_Intercept,ER7Pla_Intercept)                     0.25      0.11
cor(ER5ExperS_Prev_MF,ER7Pla_Intercept)                      -0.00      0.18
cor(ER5ExperS_Prev_Str_Overall,ER7Pla_Intercept)             -0.04      0.15
cor(ER6Acc_Intercept,ER7Pla_Intercept)                        0.57      0.09
cor(ER6Acc_Prev_MF,ER7Pla_Intercept)                         -0.01      0.18
cor(ER6Acc_Prev_Str_Overall,ER7Pla_Intercept)                -0.03      0.17
cor(ER1Dis_Intercept,ER7Pla_Prev_MF)                          0.03      0.16
cor(ER1Dis_Prev_MF,ER7Pla_Prev_MF)                            0.02      0.17
cor(ER1Dis_Prev_Str_Overall,ER7Pla_Prev_MF)                  -0.03      0.17
cor(ER2Rum_Intercept,ER7Pla_Prev_MF)                          0.14      0.16
cor(ER2Rum_Prev_MF,ER7Pla_Prev_MF)                            0.02      0.18
cor(ER2Rum_Prev_Str_Overall,ER7Pla_Prev_MF)                   0.01      0.17
cor(ER3SBl_Intercept,ER7Pla_Prev_MF)                          0.11      0.16
cor(ER3SBl_Prev_MF,ER7Pla_Prev_MF)                            0.03      0.18
cor(ER3SBl_Prev_Str_Overall,ER7Pla_Prev_MF)                  -0.01      0.18
cor(ER4ExprS_Intercept,ER7Pla_Prev_MF)                        0.02      0.16
cor(ER4ExprS_Prev_MF,ER7Pla_Prev_MF)                          0.01      0.18
cor(ER4ExprS_Prev_Str_Overall,ER7Pla_Prev_MF)                -0.03      0.17
cor(ER5ExperS_Intercept,ER7Pla_Prev_MF)                       0.02      0.16
cor(ER5ExperS_Prev_MF,ER7Pla_Prev_MF)                         0.02      0.18
cor(ER5ExperS_Prev_Str_Overall,ER7Pla_Prev_MF)               -0.07      0.17
cor(ER6Acc_Intercept,ER7Pla_Prev_MF)                          0.12      0.16
cor(ER6Acc_Prev_MF,ER7Pla_Prev_MF)                            0.04      0.18
cor(ER6Acc_Prev_Str_Overall,ER7Pla_Prev_MF)                   0.01      0.18
cor(ER7Pla_Intercept,ER7Pla_Prev_MF)                         -0.09      0.17
cor(ER1Dis_Intercept,ER7Pla_Prev_Str_Overall)                 0.02      0.17
cor(ER1Dis_Prev_MF,ER7Pla_Prev_Str_Overall)                  -0.03      0.18
cor(ER1Dis_Prev_Str_Overall,ER7Pla_Prev_Str_Overall)          0.05      0.18
cor(ER2Rum_Intercept,ER7Pla_Prev_Str_Overall)                 0.01      0.17
cor(ER2Rum_Prev_MF,ER7Pla_Prev_Str_Overall)                  -0.01      0.18
cor(ER2Rum_Prev_Str_Overall,ER7Pla_Prev_Str_Overall)          0.02      0.18
cor(ER3SBl_Intercept,ER7Pla_Prev_Str_Overall)                -0.05      0.18
cor(ER3SBl_Prev_MF,ER7Pla_Prev_Str_Overall)                  -0.01      0.18
cor(ER3SBl_Prev_Str_Overall,ER7Pla_Prev_Str_Overall)          0.00      0.18
cor(ER4ExprS_Intercept,ER7Pla_Prev_Str_Overall)              -0.08      0.18
cor(ER4ExprS_Prev_MF,ER7Pla_Prev_Str_Overall)                -0.00      0.18
cor(ER4ExprS_Prev_Str_Overall,ER7Pla_Prev_Str_Overall)        0.03      0.18
cor(ER5ExperS_Intercept,ER7Pla_Prev_Str_Overall)             -0.05      0.17
cor(ER5ExperS_Prev_MF,ER7Pla_Prev_Str_Overall)               -0.01      0.18
cor(ER5ExperS_Prev_Str_Overall,ER7Pla_Prev_Str_Overall)       0.01      0.18
cor(ER6Acc_Intercept,ER7Pla_Prev_Str_Overall)                 0.01      0.17
cor(ER6Acc_Prev_MF,ER7Pla_Prev_Str_Overall)                   0.02      0.18
cor(ER6Acc_Prev_Str_Overall,ER7Pla_Prev_Str_Overall)          0.03      0.18
cor(ER7Pla_Intercept,ER7Pla_Prev_Str_Overall)                -0.02      0.17
cor(ER7Pla_Prev_MF,ER7Pla_Prev_Str_Overall)                  -0.02      0.18
cor(ER1Dis_Intercept,ER8Rea_Intercept)                        0.07      0.13
cor(ER1Dis_Prev_MF,ER8Rea_Intercept)                          0.08      0.16
cor(ER1Dis_Prev_Str_Overall,ER8Rea_Intercept)                -0.04      0.14
cor(ER2Rum_Intercept,ER8Rea_Intercept)                        0.32      0.13
cor(ER2Rum_Prev_MF,ER8Rea_Intercept)                          0.01      0.18
cor(ER2Rum_Prev_Str_Overall,ER8Rea_Intercept)                 0.14      0.16
cor(ER3SBl_Intercept,ER8Rea_Intercept)                        0.24      0.13
cor(ER3SBl_Prev_MF,ER8Rea_Intercept)                          0.02      0.17
cor(ER3SBl_Prev_Str_Overall,ER8Rea_Intercept)                -0.02      0.18
cor(ER4ExprS_Intercept,ER8Rea_Intercept)                      0.19      0.13
cor(ER4ExprS_Prev_MF,ER8Rea_Intercept)                       -0.01      0.18
cor(ER4ExprS_Prev_Str_Overall,ER8Rea_Intercept)              -0.05      0.17
cor(ER5ExperS_Intercept,ER8Rea_Intercept)                     0.07      0.13
cor(ER5ExperS_Prev_MF,ER8Rea_Intercept)                       0.00      0.18
cor(ER5ExperS_Prev_Str_Overall,ER8Rea_Intercept)             -0.02      0.16
cor(ER6Acc_Intercept,ER8Rea_Intercept)                        0.37      0.12
cor(ER6Acc_Prev_MF,ER8Rea_Intercept)                          0.02      0.17
cor(ER6Acc_Prev_Str_Overall,ER8Rea_Intercept)                 0.00      0.17
cor(ER7Pla_Intercept,ER8Rea_Intercept)                        0.40      0.12
cor(ER7Pla_Prev_MF,ER8Rea_Intercept)                          0.15      0.17
cor(ER7Pla_Prev_Str_Overall,ER8Rea_Intercept)                 0.00      0.17
cor(ER1Dis_Intercept,ER8Rea_Prev_MF)                         -0.02      0.16
cor(ER1Dis_Prev_MF,ER8Rea_Prev_MF)                            0.01      0.17
cor(ER1Dis_Prev_Str_Overall,ER8Rea_Prev_MF)                  -0.02      0.16
cor(ER2Rum_Intercept,ER8Rea_Prev_MF)                          0.08      0.16
cor(ER2Rum_Prev_MF,ER8Rea_Prev_MF)                            0.01      0.18
cor(ER2Rum_Prev_Str_Overall,ER8Rea_Prev_MF)                   0.05      0.17
cor(ER3SBl_Intercept,ER8Rea_Prev_MF)                          0.08      0.16
cor(ER3SBl_Prev_MF,ER8Rea_Prev_MF)                            0.01      0.18
cor(ER3SBl_Prev_Str_Overall,ER8Rea_Prev_MF)                  -0.02      0.18
cor(ER4ExprS_Intercept,ER8Rea_Prev_MF)                        0.11      0.16
cor(ER4ExprS_Prev_MF,ER8Rea_Prev_MF)                         -0.01      0.18
cor(ER4ExprS_Prev_Str_Overall,ER8Rea_Prev_MF)                -0.03      0.17
cor(ER5ExperS_Intercept,ER8Rea_Prev_MF)                      -0.01      0.16
cor(ER5ExperS_Prev_MF,ER8Rea_Prev_MF)                         0.02      0.18
cor(ER5ExperS_Prev_Str_Overall,ER8Rea_Prev_MF)                0.03      0.17
cor(ER6Acc_Intercept,ER8Rea_Prev_MF)                          0.17      0.17
cor(ER6Acc_Prev_MF,ER8Rea_Prev_MF)                            0.06      0.18
cor(ER6Acc_Prev_Str_Overall,ER8Rea_Prev_MF)                   0.01      0.18
cor(ER7Pla_Intercept,ER8Rea_Prev_MF)                          0.11      0.16
cor(ER7Pla_Prev_MF,ER8Rea_Prev_MF)                            0.12      0.18
cor(ER7Pla_Prev_Str_Overall,ER8Rea_Prev_MF)                  -0.05      0.18
cor(ER8Rea_Intercept,ER8Rea_Prev_MF)                         -0.02      0.17
cor(ER1Dis_Intercept,ER8Rea_Prev_Str_Overall)                -0.00      0.17
cor(ER1Dis_Prev_MF,ER8Rea_Prev_Str_Overall)                  -0.00      0.18
cor(ER1Dis_Prev_Str_Overall,ER8Rea_Prev_Str_Overall)          0.02      0.18
cor(ER2Rum_Intercept,ER8Rea_Prev_Str_Overall)                 0.02      0.18
cor(ER2Rum_Prev_MF,ER8Rea_Prev_Str_Overall)                  -0.00      0.18
cor(ER2Rum_Prev_Str_Overall,ER8Rea_Prev_Str_Overall)          0.06      0.18
cor(ER3SBl_Intercept,ER8Rea_Prev_Str_Overall)                 0.03      0.18
cor(ER3SBl_Prev_MF,ER8Rea_Prev_Str_Overall)                  -0.00      0.18
cor(ER3SBl_Prev_Str_Overall,ER8Rea_Prev_Str_Overall)          0.00      0.18
cor(ER4ExprS_Intercept,ER8Rea_Prev_Str_Overall)               0.02      0.18
cor(ER4ExprS_Prev_MF,ER8Rea_Prev_Str_Overall)                -0.01      0.18
cor(ER4ExprS_Prev_Str_Overall,ER8Rea_Prev_Str_Overall)        0.01      0.18
cor(ER5ExperS_Intercept,ER8Rea_Prev_Str_Overall)             -0.02      0.17
cor(ER5ExperS_Prev_MF,ER8Rea_Prev_Str_Overall)                0.00      0.18
cor(ER5ExperS_Prev_Str_Overall,ER8Rea_Prev_Str_Overall)       0.04      0.18
cor(ER6Acc_Intercept,ER8Rea_Prev_Str_Overall)                 0.04      0.18
cor(ER6Acc_Prev_MF,ER8Rea_Prev_Str_Overall)                   0.00      0.18
cor(ER6Acc_Prev_Str_Overall,ER8Rea_Prev_Str_Overall)          0.01      0.18
cor(ER7Pla_Intercept,ER8Rea_Prev_Str_Overall)                 0.03      0.17
cor(ER7Pla_Prev_MF,ER8Rea_Prev_Str_Overall)                   0.01      0.18
cor(ER7Pla_Prev_Str_Overall,ER8Rea_Prev_Str_Overall)          0.00      0.18
cor(ER8Rea_Intercept,ER8Rea_Prev_Str_Overall)                 0.01      0.17
cor(ER8Rea_Prev_MF,ER8Rea_Prev_Str_Overall)                  -0.00      0.18
cor(ER1Dis_Intercept,ER9ESu_Intercept)                        0.17      0.12
cor(ER1Dis_Prev_MF,ER9ESu_Intercept)                          0.16      0.15
cor(ER1Dis_Prev_Str_Overall,ER9ESu_Intercept)                -0.02      0.14
cor(ER2Rum_Intercept,ER9ESu_Intercept)                        0.39      0.12
cor(ER2Rum_Prev_MF,ER9ESu_Intercept)                          0.03      0.18
cor(ER2Rum_Prev_Str_Overall,ER9ESu_Intercept)                 0.03      0.16
cor(ER3SBl_Intercept,ER9ESu_Intercept)                        0.33      0.13
cor(ER3SBl_Prev_MF,ER9ESu_Intercept)                          0.08      0.18
cor(ER3SBl_Prev_Str_Overall,ER9ESu_Intercept)                 0.01      0.18
cor(ER4ExprS_Intercept,ER9ESu_Intercept)                      0.30      0.12
cor(ER4ExprS_Prev_MF,ER9ESu_Intercept)                        0.02      0.17
cor(ER4ExprS_Prev_Str_Overall,ER9ESu_Intercept)              -0.07      0.16
cor(ER5ExperS_Intercept,ER9ESu_Intercept)                     0.28      0.12
cor(ER5ExperS_Prev_MF,ER9ESu_Intercept)                       0.02      0.18
cor(ER5ExperS_Prev_Str_Overall,ER9ESu_Intercept)             -0.10      0.15
cor(ER6Acc_Intercept,ER9ESu_Intercept)                        0.47      0.10
cor(ER6Acc_Prev_MF,ER9ESu_Intercept)                          0.04      0.17
cor(ER6Acc_Prev_Str_Overall,ER9ESu_Intercept)                 0.01      0.17
cor(ER7Pla_Intercept,ER9ESu_Intercept)                        0.36      0.11
cor(ER7Pla_Prev_MF,ER9ESu_Intercept)                          0.16      0.17
cor(ER7Pla_Prev_Str_Overall,ER9ESu_Intercept)                -0.03      0.17
cor(ER8Rea_Intercept,ER9ESu_Intercept)                        0.35      0.13
cor(ER8Rea_Prev_MF,ER9ESu_Intercept)                          0.14      0.17
cor(ER8Rea_Prev_Str_Overall,ER9ESu_Intercept)                 0.02      0.18
cor(ER1Dis_Intercept,ER9ESu_Prev_MF)                         -0.03      0.16
cor(ER1Dis_Prev_MF,ER9ESu_Prev_MF)                            0.03      0.18
cor(ER1Dis_Prev_Str_Overall,ER9ESu_Prev_MF)                   0.11      0.17
cor(ER2Rum_Intercept,ER9ESu_Prev_MF)                          0.01      0.17
cor(ER2Rum_Prev_MF,ER9ESu_Prev_MF)                            0.02      0.18
cor(ER2Rum_Prev_Str_Overall,ER9ESu_Prev_MF)                   0.01      0.17
cor(ER3SBl_Intercept,ER9ESu_Prev_MF)                         -0.01      0.16
cor(ER3SBl_Prev_MF,ER9ESu_Prev_MF)                            0.03      0.18
cor(ER3SBl_Prev_Str_Overall,ER9ESu_Prev_MF)                   0.02      0.18
cor(ER4ExprS_Intercept,ER9ESu_Prev_MF)                       -0.00      0.17
cor(ER4ExprS_Prev_MF,ER9ESu_Prev_MF)                          0.00      0.18
cor(ER4ExprS_Prev_Str_Overall,ER9ESu_Prev_MF)                 0.04      0.18
cor(ER5ExperS_Intercept,ER9ESu_Prev_MF)                      -0.06      0.16
cor(ER5ExperS_Prev_MF,ER9ESu_Prev_MF)                         0.01      0.18
cor(ER5ExperS_Prev_Str_Overall,ER9ESu_Prev_MF)               -0.02      0.17
cor(ER6Acc_Intercept,ER9ESu_Prev_MF)                          0.02      0.17
cor(ER6Acc_Prev_MF,ER9ESu_Prev_MF)                           -0.00      0.18
cor(ER6Acc_Prev_Str_Overall,ER9ESu_Prev_MF)                   0.02      0.18
cor(ER7Pla_Intercept,ER9ESu_Prev_MF)                         -0.03      0.17
cor(ER7Pla_Prev_MF,ER9ESu_Prev_MF)                            0.06      0.18
cor(ER7Pla_Prev_Str_Overall,ER9ESu_Prev_MF)                   0.00      0.18
cor(ER8Rea_Intercept,ER9ESu_Prev_MF)                          0.05      0.17
cor(ER8Rea_Prev_MF,ER9ESu_Prev_MF)                            0.09      0.18
cor(ER8Rea_Prev_Str_Overall,ER9ESu_Prev_MF)                   0.00      0.18
cor(ER9ESu_Intercept,ER9ESu_Prev_MF)                         -0.16      0.18
cor(ER1Dis_Intercept,ER9ESu_Prev_Str_Overall)                -0.10      0.17
cor(ER1Dis_Prev_MF,ER9ESu_Prev_Str_Overall)                  -0.08      0.18
cor(ER1Dis_Prev_Str_Overall,ER9ESu_Prev_Str_Overall)          0.08      0.17
cor(ER2Rum_Intercept,ER9ESu_Prev_Str_Overall)                -0.01      0.16
cor(ER2Rum_Prev_MF,ER9ESu_Prev_Str_Overall)                  -0.01      0.18
cor(ER2Rum_Prev_Str_Overall,ER9ESu_Prev_Str_Overall)          0.05      0.18
cor(ER3SBl_Intercept,ER9ESu_Prev_Str_Overall)                -0.02      0.17
cor(ER3SBl_Prev_MF,ER9ESu_Prev_Str_Overall)                  -0.01      0.18
cor(ER3SBl_Prev_Str_Overall,ER9ESu_Prev_Str_Overall)          0.01      0.18
cor(ER4ExprS_Intercept,ER9ESu_Prev_Str_Overall)              -0.06      0.17
cor(ER4ExprS_Prev_MF,ER9ESu_Prev_Str_Overall)                -0.01      0.18
cor(ER4ExprS_Prev_Str_Overall,ER9ESu_Prev_Str_Overall)        0.01      0.18
cor(ER5ExperS_Intercept,ER9ESu_Prev_Str_Overall)             -0.11      0.17
cor(ER5ExperS_Prev_MF,ER9ESu_Prev_Str_Overall)               -0.01      0.18
cor(ER5ExperS_Prev_Str_Overall,ER9ESu_Prev_Str_Overall)       0.04      0.18
cor(ER6Acc_Intercept,ER9ESu_Prev_Str_Overall)                 0.00      0.17
cor(ER6Acc_Prev_MF,ER9ESu_Prev_Str_Overall)                   0.01      0.18
cor(ER6Acc_Prev_Str_Overall,ER9ESu_Prev_Str_Overall)          0.04      0.18
cor(ER7Pla_Intercept,ER9ESu_Prev_Str_Overall)                 0.01      0.17
cor(ER7Pla_Prev_MF,ER9ESu_Prev_Str_Overall)                   0.02      0.18
cor(ER7Pla_Prev_Str_Overall,ER9ESu_Prev_Str_Overall)          0.03      0.18
cor(ER8Rea_Intercept,ER9ESu_Prev_Str_Overall)                 0.08      0.17
cor(ER8Rea_Prev_MF,ER9ESu_Prev_Str_Overall)                   0.06      0.18
cor(ER8Rea_Prev_Str_Overall,ER9ESu_Prev_Str_Overall)          0.03      0.18
cor(ER9ESu_Intercept,ER9ESu_Prev_Str_Overall)                -0.09      0.17
cor(ER9ESu_Prev_MF,ER9ESu_Prev_Str_Overall)                   0.01      0.18
cor(ER1Dis_Intercept,ER10Rel_Intercept)                       0.41      0.10
cor(ER1Dis_Prev_MF,ER10Rel_Intercept)                         0.09      0.15
cor(ER1Dis_Prev_Str_Overall,ER10Rel_Intercept)               -0.03      0.13
cor(ER2Rum_Intercept,ER10Rel_Intercept)                       0.21      0.11
cor(ER2Rum_Prev_MF,ER10Rel_Intercept)                         0.01      0.17
cor(ER2Rum_Prev_Str_Overall,ER10Rel_Intercept)               -0.08      0.15
cor(ER3SBl_Intercept,ER10Rel_Intercept)                       0.15      0.12
cor(ER3SBl_Prev_MF,ER10Rel_Intercept)                         0.03      0.17
cor(ER3SBl_Prev_Str_Overall,ER10Rel_Intercept)                0.03      0.17
cor(ER4ExprS_Intercept,ER10Rel_Intercept)                     0.01      0.12
cor(ER4ExprS_Prev_MF,ER10Rel_Intercept)                       0.02      0.17
cor(ER4ExprS_Prev_Str_Overall,ER10Rel_Intercept)              0.02      0.16
cor(ER5ExperS_Intercept,ER10Rel_Intercept)                    0.17      0.10
cor(ER5ExperS_Prev_MF,ER10Rel_Intercept)                      0.00      0.18
cor(ER5ExperS_Prev_Str_Overall,ER10Rel_Intercept)            -0.09      0.15
cor(ER6Acc_Intercept,ER10Rel_Intercept)                       0.19      0.10
cor(ER6Acc_Prev_MF,ER10Rel_Intercept)                         0.07      0.17
cor(ER6Acc_Prev_Str_Overall,ER10Rel_Intercept)                0.05      0.17
cor(ER7Pla_Intercept,ER10Rel_Intercept)                       0.24      0.11
cor(ER7Pla_Prev_MF,ER10Rel_Intercept)                         0.20      0.16
cor(ER7Pla_Prev_Str_Overall,ER10Rel_Intercept)                0.06      0.17
cor(ER8Rea_Intercept,ER10Rel_Intercept)                       0.20      0.12
cor(ER8Rea_Prev_MF,ER10Rel_Intercept)                         0.00      0.16
cor(ER8Rea_Prev_Str_Overall,ER10Rel_Intercept)               -0.03      0.17
cor(ER9ESu_Intercept,ER10Rel_Intercept)                       0.28      0.11
cor(ER9ESu_Prev_MF,ER10Rel_Intercept)                        -0.05      0.16
cor(ER9ESu_Prev_Str_Overall,ER10Rel_Intercept)               -0.07      0.16
cor(ER1Dis_Intercept,ER10Rel_Prev_MF)                        -0.07      0.17
cor(ER1Dis_Prev_MF,ER10Rel_Prev_MF)                           0.01      0.18
cor(ER1Dis_Prev_Str_Overall,ER10Rel_Prev_MF)                  0.08      0.18
cor(ER2Rum_Intercept,ER10Rel_Prev_MF)                        -0.04      0.16
cor(ER2Rum_Prev_MF,ER10Rel_Prev_MF)                          -0.01      0.18
cor(ER2Rum_Prev_Str_Overall,ER10Rel_Prev_MF)                 -0.05      0.18
cor(ER3SBl_Intercept,ER10Rel_Prev_MF)                        -0.04      0.16
cor(ER3SBl_Prev_MF,ER10Rel_Prev_MF)                          -0.01      0.18
cor(ER3SBl_Prev_Str_Overall,ER10Rel_Prev_MF)                 -0.02      0.18
cor(ER4ExprS_Intercept,ER10Rel_Prev_MF)                       0.07      0.17
cor(ER4ExprS_Prev_MF,ER10Rel_Prev_MF)                         0.00      0.18
cor(ER4ExprS_Prev_Str_Overall,ER10Rel_Prev_MF)                0.04      0.18
cor(ER5ExperS_Intercept,ER10Rel_Prev_MF)                      0.03      0.16
cor(ER5ExperS_Prev_MF,ER10Rel_Prev_MF)                        0.00      0.18
cor(ER5ExperS_Prev_Str_Overall,ER10Rel_Prev_MF)              -0.00      0.17
cor(ER6Acc_Intercept,ER10Rel_Prev_MF)                         0.08      0.16
cor(ER6Acc_Prev_MF,ER10Rel_Prev_MF)                           0.01      0.18
cor(ER6Acc_Prev_Str_Overall,ER10Rel_Prev_MF)                  0.00      0.18
cor(ER7Pla_Intercept,ER10Rel_Prev_MF)                         0.04      0.16
cor(ER7Pla_Prev_MF,ER10Rel_Prev_MF)                           0.01      0.18
cor(ER7Pla_Prev_Str_Overall,ER10Rel_Prev_MF)                 -0.01      0.18
cor(ER8Rea_Intercept,ER10Rel_Prev_MF)                        -0.01      0.17
cor(ER8Rea_Prev_MF,ER10Rel_Prev_MF)                           0.02      0.17
cor(ER8Rea_Prev_Str_Overall,ER10Rel_Prev_MF)                 -0.01      0.18
cor(ER9ESu_Intercept,ER10Rel_Prev_MF)                         0.08      0.17
cor(ER9ESu_Prev_MF,ER10Rel_Prev_MF)                           0.06      0.18
cor(ER9ESu_Prev_Str_Overall,ER10Rel_Prev_MF)                  0.00      0.18
cor(ER10Rel_Intercept,ER10Rel_Prev_MF)                       -0.18      0.18
cor(ER1Dis_Intercept,ER10Rel_Prev_Str_Overall)               -0.15      0.16
cor(ER1Dis_Prev_MF,ER10Rel_Prev_Str_Overall)                 -0.06      0.17
cor(ER1Dis_Prev_Str_Overall,ER10Rel_Prev_Str_Overall)         0.20      0.17
cor(ER2Rum_Intercept,ER10Rel_Prev_Str_Overall)                0.06      0.15
cor(ER2Rum_Prev_MF,ER10Rel_Prev_Str_Overall)                  0.01      0.18
cor(ER2Rum_Prev_Str_Overall,ER10Rel_Prev_Str_Overall)         0.01      0.17
cor(ER3SBl_Intercept,ER10Rel_Prev_Str_Overall)                0.05      0.16
cor(ER3SBl_Prev_MF,ER10Rel_Prev_Str_Overall)                  0.02      0.18
cor(ER3SBl_Prev_Str_Overall,ER10Rel_Prev_Str_Overall)         0.01      0.18
cor(ER4ExprS_Intercept,ER10Rel_Prev_Str_Overall)              0.09      0.15
cor(ER4ExprS_Prev_MF,ER10Rel_Prev_Str_Overall)                0.01      0.18
cor(ER4ExprS_Prev_Str_Overall,ER10Rel_Prev_Str_Overall)       0.04      0.18
cor(ER5ExperS_Intercept,ER10Rel_Prev_Str_Overall)             0.06      0.15
cor(ER5ExperS_Prev_MF,ER10Rel_Prev_Str_Overall)               0.00      0.18
cor(ER5ExperS_Prev_Str_Overall,ER10Rel_Prev_Str_Overall)      0.04      0.17
cor(ER6Acc_Intercept,ER10Rel_Prev_Str_Overall)                0.07      0.15
cor(ER6Acc_Prev_MF,ER10Rel_Prev_Str_Overall)                 -0.02      0.18
cor(ER6Acc_Prev_Str_Overall,ER10Rel_Prev_Str_Overall)         0.00      0.18
cor(ER7Pla_Intercept,ER10Rel_Prev_Str_Overall)               -0.01      0.15
cor(ER7Pla_Prev_MF,ER10Rel_Prev_Str_Overall)                  0.01      0.17
cor(ER7Pla_Prev_Str_Overall,ER10Rel_Prev_Str_Overall)         0.02      0.18
cor(ER8Rea_Intercept,ER10Rel_Prev_Str_Overall)               -0.02      0.16
cor(ER8Rea_Prev_MF,ER10Rel_Prev_Str_Overall)                  0.01      0.17
cor(ER8Rea_Prev_Str_Overall,ER10Rel_Prev_Str_Overall)         0.01      0.18
cor(ER9ESu_Intercept,ER10Rel_Prev_Str_Overall)                0.04      0.16
cor(ER9ESu_Prev_MF,ER10Rel_Prev_Str_Overall)                  0.06      0.17
cor(ER9ESu_Prev_Str_Overall,ER10Rel_Prev_Str_Overall)         0.03      0.18
cor(ER10Rel_Intercept,ER10Rel_Prev_Str_Overall)              -0.19      0.16
cor(ER10Rel_Prev_MF,ER10Rel_Prev_Str_Overall)                 0.04      0.17
                                                          l-95% CI u-95% CI
sd(ER1Dis_Intercept)                                          1.33     2.07
sd(ER1Dis_Prev_MF)                                            0.06     0.51
sd(ER1Dis_Prev_Str_Overall)                                   0.15     0.36
sd(ER2Rum_Intercept)                                          0.94     1.75
sd(ER2Rum_Prev_MF)                                            0.00     0.34
sd(ER2Rum_Prev_Str_Overall)                                   0.05     0.34
sd(ER3SBl_Intercept)                                          1.03     2.01
sd(ER3SBl_Prev_MF)                                            0.01     0.50
sd(ER3SBl_Prev_Str_Overall)                                   0.00     0.21
sd(ER4ExprS_Intercept)                                        0.99     1.65
sd(ER4ExprS_Prev_MF)                                          0.00     0.29
sd(ER4ExprS_Prev_Str_Overall)                                 0.01     0.25
sd(ER5ExperS_Intercept)                                       1.33     2.13
sd(ER5ExperS_Prev_MF)                                         0.00     0.29
sd(ER5ExperS_Prev_Str_Overall)                                0.11     0.35
sd(ER6Acc_Intercept)                                          1.04     1.52
sd(ER6Acc_Prev_MF)                                            0.01     0.28
sd(ER6Acc_Prev_Str_Overall)                                   0.00     0.13
sd(ER7Pla_Intercept)                                          0.98     1.54
sd(ER7Pla_Prev_MF)                                            0.03     0.39
sd(ER7Pla_Prev_Str_Overall)                                   0.00     0.16
sd(ER8Rea_Intercept)                                          0.88     1.84
sd(ER8Rea_Prev_MF)                                            0.03     0.61
sd(ER8Rea_Prev_Str_Overall)                                   0.00     0.17
sd(ER9ESu_Intercept)                                          1.08     1.94
sd(ER9ESu_Prev_MF)                                            0.03     0.57
sd(ER9ESu_Prev_Str_Overall)                                   0.01     0.26
sd(ER10Rel_Intercept)                                         1.42     2.08
sd(ER10Rel_Prev_MF)                                           0.01     0.45
sd(ER10Rel_Prev_Str_Overall)                                  0.04     0.24
cor(ER1Dis_Intercept,ER1Dis_Prev_MF)                         -0.36     0.27
cor(ER1Dis_Intercept,ER1Dis_Prev_Str_Overall)                -0.37     0.17
cor(ER1Dis_Prev_MF,ER1Dis_Prev_Str_Overall)                  -0.38     0.25
cor(ER1Dis_Intercept,ER2Rum_Intercept)                       -0.12     0.36
cor(ER1Dis_Prev_MF,ER2Rum_Intercept)                         -0.11     0.47
cor(ER1Dis_Prev_Str_Overall,ER2Rum_Intercept)                -0.23     0.32
cor(ER1Dis_Intercept,ER2Rum_Prev_MF)                         -0.33     0.35
cor(ER1Dis_Prev_MF,ER2Rum_Prev_MF)                           -0.31     0.39
cor(ER1Dis_Prev_Str_Overall,ER2Rum_Prev_MF)                  -0.31     0.38
cor(ER2Rum_Intercept,ER2Rum_Prev_MF)                         -0.35     0.34
cor(ER1Dis_Intercept,ER2Rum_Prev_Str_Overall)                -0.31     0.30
cor(ER1Dis_Prev_MF,ER2Rum_Prev_Str_Overall)                  -0.27     0.40
cor(ER1Dis_Prev_Str_Overall,ER2Rum_Prev_Str_Overall)         -0.26     0.37
cor(ER2Rum_Intercept,ER2Rum_Prev_Str_Overall)                -0.25     0.37
cor(ER2Rum_Prev_MF,ER2Rum_Prev_Str_Overall)                  -0.37     0.32
cor(ER1Dis_Intercept,ER3SBl_Intercept)                       -0.10     0.38
cor(ER1Dis_Prev_MF,ER3SBl_Intercept)                         -0.16     0.45
cor(ER1Dis_Prev_Str_Overall,ER3SBl_Intercept)                -0.36     0.19
cor(ER2Rum_Intercept,ER3SBl_Intercept)                        0.24     0.69
cor(ER2Rum_Prev_MF,ER3SBl_Intercept)                         -0.29     0.43
cor(ER2Rum_Prev_Str_Overall,ER3SBl_Intercept)                -0.20     0.42
cor(ER1Dis_Intercept,ER3SBl_Prev_MF)                         -0.31     0.35
cor(ER1Dis_Prev_MF,ER3SBl_Prev_MF)                           -0.32     0.36
cor(ER1Dis_Prev_Str_Overall,ER3SBl_Prev_MF)                  -0.35     0.33
cor(ER2Rum_Intercept,ER3SBl_Prev_MF)                         -0.24     0.46
cor(ER2Rum_Prev_MF,ER3SBl_Prev_MF)                           -0.32     0.40
cor(ER2Rum_Prev_Str_Overall,ER3SBl_Prev_MF)                  -0.35     0.34
cor(ER3SBl_Intercept,ER3SBl_Prev_MF)                         -0.34     0.35
cor(ER1Dis_Intercept,ER3SBl_Prev_Str_Overall)                -0.31     0.38
cor(ER1Dis_Prev_MF,ER3SBl_Prev_Str_Overall)                  -0.32     0.36
cor(ER1Dis_Prev_Str_Overall,ER3SBl_Prev_Str_Overall)         -0.31     0.36
cor(ER2Rum_Intercept,ER3SBl_Prev_Str_Overall)                -0.29     0.40
cor(ER2Rum_Prev_MF,ER3SBl_Prev_Str_Overall)                  -0.33     0.36
cor(ER2Rum_Prev_Str_Overall,ER3SBl_Prev_Str_Overall)         -0.31     0.38
cor(ER3SBl_Intercept,ER3SBl_Prev_Str_Overall)                -0.35     0.34
cor(ER3SBl_Prev_MF,ER3SBl_Prev_Str_Overall)                  -0.35     0.34
cor(ER1Dis_Intercept,ER4ExprS_Intercept)                     -0.13     0.33
cor(ER1Dis_Prev_MF,ER4ExprS_Intercept)                       -0.20     0.40
cor(ER1Dis_Prev_Str_Overall,ER4ExprS_Intercept)              -0.21     0.33
cor(ER2Rum_Intercept,ER4ExprS_Intercept)                     -0.05     0.43
cor(ER2Rum_Prev_MF,ER4ExprS_Intercept)                       -0.31     0.38
cor(ER2Rum_Prev_Str_Overall,ER4ExprS_Intercept)              -0.26     0.33
cor(ER3SBl_Intercept,ER4ExprS_Intercept)                      0.02     0.50
cor(ER3SBl_Prev_MF,ER4ExprS_Intercept)                       -0.29     0.38
cor(ER3SBl_Prev_Str_Overall,ER4ExprS_Intercept)              -0.39     0.30
cor(ER1Dis_Intercept,ER4ExprS_Prev_MF)                       -0.33     0.35
cor(ER1Dis_Prev_MF,ER4ExprS_Prev_MF)                         -0.32     0.38
cor(ER1Dis_Prev_Str_Overall,ER4ExprS_Prev_MF)                -0.32     0.38
cor(ER2Rum_Intercept,ER4ExprS_Prev_MF)                       -0.33     0.36
cor(ER2Rum_Prev_MF,ER4ExprS_Prev_MF)                         -0.34     0.37
cor(ER2Rum_Prev_Str_Overall,ER4ExprS_Prev_MF)                -0.34     0.35
cor(ER3SBl_Intercept,ER4ExprS_Prev_MF)                       -0.33     0.38
cor(ER3SBl_Prev_MF,ER4ExprS_Prev_MF)                         -0.34     0.36
cor(ER3SBl_Prev_Str_Overall,ER4ExprS_Prev_MF)                -0.35     0.35
cor(ER4ExprS_Intercept,ER4ExprS_Prev_MF)                     -0.41     0.31
cor(ER1Dis_Intercept,ER4ExprS_Prev_Str_Overall)              -0.29     0.38
cor(ER1Dis_Prev_MF,ER4ExprS_Prev_Str_Overall)                -0.34     0.33
cor(ER1Dis_Prev_Str_Overall,ER4ExprS_Prev_Str_Overall)       -0.21     0.48
cor(ER2Rum_Intercept,ER4ExprS_Prev_Str_Overall)              -0.39     0.25
cor(ER2Rum_Prev_MF,ER4ExprS_Prev_Str_Overall)                -0.35     0.34
cor(ER2Rum_Prev_Str_Overall,ER4ExprS_Prev_Str_Overall)       -0.28     0.41
cor(ER3SBl_Intercept,ER4ExprS_Prev_Str_Overall)              -0.37     0.26
cor(ER3SBl_Prev_MF,ER4ExprS_Prev_Str_Overall)                -0.37     0.33
cor(ER3SBl_Prev_Str_Overall,ER4ExprS_Prev_Str_Overall)       -0.33     0.36
cor(ER4ExprS_Intercept,ER4ExprS_Prev_Str_Overall)            -0.44     0.23
cor(ER4ExprS_Prev_MF,ER4ExprS_Prev_Str_Overall)              -0.37     0.33
cor(ER1Dis_Intercept,ER5ExperS_Intercept)                     0.01     0.44
cor(ER1Dis_Prev_MF,ER5ExperS_Intercept)                      -0.18     0.42
cor(ER1Dis_Prev_Str_Overall,ER5ExperS_Intercept)             -0.23     0.30
cor(ER2Rum_Intercept,ER5ExperS_Intercept)                     0.06     0.51
cor(ER2Rum_Prev_MF,ER5ExperS_Intercept)                      -0.28     0.40
cor(ER2Rum_Prev_Str_Overall,ER5ExperS_Intercept)             -0.31     0.27
cor(ER3SBl_Intercept,ER5ExperS_Intercept)                     0.06     0.51
cor(ER3SBl_Prev_MF,ER5ExperS_Intercept)                      -0.27     0.42
cor(ER3SBl_Prev_Str_Overall,ER5ExperS_Intercept)             -0.33     0.34
cor(ER4ExprS_Intercept,ER5ExperS_Intercept)                   0.38     0.75
cor(ER4ExprS_Prev_MF,ER5ExperS_Intercept)                    -0.30     0.41
cor(ER4ExprS_Prev_Str_Overall,ER5ExperS_Intercept)           -0.34     0.31
cor(ER1Dis_Intercept,ER5ExperS_Prev_MF)                      -0.34     0.36
cor(ER1Dis_Prev_MF,ER5ExperS_Prev_MF)                        -0.33     0.36
cor(ER1Dis_Prev_Str_Overall,ER5ExperS_Prev_MF)               -0.34     0.36
cor(ER2Rum_Intercept,ER5ExperS_Prev_MF)                      -0.35     0.34
cor(ER2Rum_Prev_MF,ER5ExperS_Prev_MF)                        -0.34     0.35
cor(ER2Rum_Prev_Str_Overall,ER5ExperS_Prev_MF)               -0.34     0.35
cor(ER3SBl_Intercept,ER5ExperS_Prev_MF)                      -0.32     0.36
cor(ER3SBl_Prev_MF,ER5ExperS_Prev_MF)                        -0.34     0.35
cor(ER3SBl_Prev_Str_Overall,ER5ExperS_Prev_MF)               -0.35     0.34
cor(ER4ExprS_Intercept,ER5ExperS_Prev_MF)                    -0.30     0.41
cor(ER4ExprS_Prev_MF,ER5ExperS_Prev_MF)                      -0.33     0.36
cor(ER4ExprS_Prev_Str_Overall,ER5ExperS_Prev_MF)             -0.34     0.36
cor(ER5ExperS_Intercept,ER5ExperS_Prev_MF)                   -0.37     0.32
cor(ER1Dis_Intercept,ER5ExperS_Prev_Str_Overall)             -0.38     0.20
cor(ER1Dis_Prev_MF,ER5ExperS_Prev_Str_Overall)               -0.37     0.29
cor(ER1Dis_Prev_Str_Overall,ER5ExperS_Prev_Str_Overall)      -0.21     0.41
cor(ER2Rum_Intercept,ER5ExperS_Prev_Str_Overall)             -0.40     0.19
cor(ER2Rum_Prev_MF,ER5ExperS_Prev_Str_Overall)               -0.37     0.32
cor(ER2Rum_Prev_Str_Overall,ER5ExperS_Prev_Str_Overall)      -0.19     0.46
cor(ER3SBl_Intercept,ER5ExperS_Prev_Str_Overall)             -0.31     0.27
cor(ER3SBl_Prev_MF,ER5ExperS_Prev_Str_Overall)               -0.36     0.32
cor(ER3SBl_Prev_Str_Overall,ER5ExperS_Prev_Str_Overall)      -0.35     0.34
cor(ER4ExprS_Intercept,ER5ExperS_Prev_Str_Overall)           -0.20     0.41
cor(ER4ExprS_Prev_MF,ER5ExperS_Prev_Str_Overall)             -0.34     0.37
cor(ER4ExprS_Prev_Str_Overall,ER5ExperS_Prev_Str_Overall)    -0.22     0.48
cor(ER5ExperS_Intercept,ER5ExperS_Prev_Str_Overall)          -0.39     0.20
cor(ER5ExperS_Prev_MF,ER5ExperS_Prev_Str_Overall)            -0.34     0.35
cor(ER1Dis_Intercept,ER6Acc_Intercept)                       -0.07     0.34
cor(ER1Dis_Prev_MF,ER6Acc_Intercept)                         -0.24     0.33
cor(ER1Dis_Prev_Str_Overall,ER6Acc_Intercept)                -0.20     0.31
cor(ER2Rum_Intercept,ER6Acc_Intercept)                        0.07     0.51
cor(ER2Rum_Prev_MF,ER6Acc_Intercept)                         -0.34     0.33
cor(ER2Rum_Prev_Str_Overall,ER6Acc_Intercept)                -0.24     0.34
cor(ER3SBl_Intercept,ER6Acc_Intercept)                        0.01     0.47
cor(ER3SBl_Prev_MF,ER6Acc_Intercept)                         -0.30     0.37
cor(ER3SBl_Prev_Str_Overall,ER6Acc_Intercept)                -0.36     0.31
cor(ER4ExprS_Intercept,ER6Acc_Intercept)                      0.28     0.66
cor(ER4ExprS_Prev_MF,ER6Acc_Intercept)                       -0.34     0.35
cor(ER4ExprS_Prev_Str_Overall,ER6Acc_Intercept)              -0.33     0.30
cor(ER5ExperS_Intercept,ER6Acc_Intercept)                     0.19     0.58
cor(ER5ExperS_Prev_MF,ER6Acc_Intercept)                      -0.32     0.36
cor(ER5ExperS_Prev_Str_Overall,ER6Acc_Intercept)             -0.21     0.37
cor(ER1Dis_Intercept,ER6Acc_Prev_MF)                         -0.33     0.34
cor(ER1Dis_Prev_MF,ER6Acc_Prev_MF)                           -0.34     0.35
cor(ER1Dis_Prev_Str_Overall,ER6Acc_Prev_MF)                  -0.32     0.37
cor(ER2Rum_Intercept,ER6Acc_Prev_MF)                         -0.34     0.33
cor(ER2Rum_Prev_MF,ER6Acc_Prev_MF)                           -0.35     0.35
cor(ER2Rum_Prev_Str_Overall,ER6Acc_Prev_MF)                  -0.33     0.37
cor(ER3SBl_Intercept,ER6Acc_Prev_MF)                         -0.38     0.31
cor(ER3SBl_Prev_MF,ER6Acc_Prev_MF)                           -0.37     0.33
cor(ER3SBl_Prev_Str_Overall,ER6Acc_Prev_MF)                  -0.36     0.34
cor(ER4ExprS_Intercept,ER6Acc_Prev_MF)                       -0.32     0.35
cor(ER4ExprS_Prev_MF,ER6Acc_Prev_MF)                         -0.35     0.35
cor(ER4ExprS_Prev_Str_Overall,ER6Acc_Prev_MF)                -0.34     0.35
cor(ER5ExperS_Intercept,ER6Acc_Prev_MF)                      -0.37     0.31
cor(ER5ExperS_Prev_MF,ER6Acc_Prev_MF)                        -0.34     0.36
cor(ER5ExperS_Prev_Str_Overall,ER6Acc_Prev_MF)               -0.33     0.37
cor(ER6Acc_Intercept,ER6Acc_Prev_MF)                         -0.43     0.28
cor(ER1Dis_Intercept,ER6Acc_Prev_Str_Overall)                -0.33     0.34
cor(ER1Dis_Prev_MF,ER6Acc_Prev_Str_Overall)                  -0.37     0.32
cor(ER1Dis_Prev_Str_Overall,ER6Acc_Prev_Str_Overall)         -0.29     0.42
cor(ER2Rum_Intercept,ER6Acc_Prev_Str_Overall)                -0.34     0.34
cor(ER2Rum_Prev_MF,ER6Acc_Prev_Str_Overall)                  -0.34     0.35
cor(ER2Rum_Prev_Str_Overall,ER6Acc_Prev_Str_Overall)         -0.31     0.39
cor(ER3SBl_Intercept,ER6Acc_Prev_Str_Overall)                -0.35     0.32
cor(ER3SBl_Prev_MF,ER6Acc_Prev_Str_Overall)                  -0.37     0.34
cor(ER3SBl_Prev_Str_Overall,ER6Acc_Prev_Str_Overall)         -0.33     0.36
cor(ER4ExprS_Intercept,ER6Acc_Prev_Str_Overall)              -0.36     0.33
cor(ER4ExprS_Prev_MF,ER6Acc_Prev_Str_Overall)                -0.35     0.35
cor(ER4ExprS_Prev_Str_Overall,ER6Acc_Prev_Str_Overall)       -0.31     0.39
cor(ER5ExperS_Intercept,ER6Acc_Prev_Str_Overall)             -0.36     0.32
cor(ER5ExperS_Prev_MF,ER6Acc_Prev_Str_Overall)               -0.35     0.35
cor(ER5ExperS_Prev_Str_Overall,ER6Acc_Prev_Str_Overall)      -0.30     0.39
cor(ER6Acc_Intercept,ER6Acc_Prev_Str_Overall)                -0.35     0.33
cor(ER6Acc_Prev_MF,ER6Acc_Prev_Str_Overall)                  -0.35     0.35
cor(ER1Dis_Intercept,ER7Pla_Intercept)                        0.08     0.50
cor(ER1Dis_Prev_MF,ER7Pla_Intercept)                         -0.22     0.38
cor(ER1Dis_Prev_Str_Overall,ER7Pla_Intercept)                -0.33     0.21
cor(ER2Rum_Intercept,ER7Pla_Intercept)                        0.09     0.54
cor(ER2Rum_Prev_MF,ER7Pla_Intercept)                         -0.35     0.33
cor(ER2Rum_Prev_Str_Overall,ER7Pla_Intercept)                -0.22     0.38
cor(ER3SBl_Intercept,ER7Pla_Intercept)                       -0.02     0.46
cor(ER3SBl_Prev_MF,ER7Pla_Intercept)                         -0.30     0.36
cor(ER3SBl_Prev_Str_Overall,ER7Pla_Intercept)                -0.34     0.32
cor(ER4ExprS_Intercept,ER7Pla_Intercept)                      0.03     0.48
cor(ER4ExprS_Prev_MF,ER7Pla_Intercept)                       -0.35     0.33
cor(ER4ExprS_Prev_Str_Overall,ER7Pla_Intercept)              -0.37     0.27
cor(ER5ExperS_Intercept,ER7Pla_Intercept)                     0.03     0.46
cor(ER5ExperS_Prev_MF,ER7Pla_Intercept)                      -0.35     0.34
cor(ER5ExperS_Prev_Str_Overall,ER7Pla_Intercept)             -0.34     0.26
cor(ER6Acc_Intercept,ER7Pla_Intercept)                        0.39     0.73
cor(ER6Acc_Prev_MF,ER7Pla_Intercept)                         -0.35     0.33
cor(ER6Acc_Prev_Str_Overall,ER7Pla_Intercept)                -0.37     0.31
cor(ER1Dis_Intercept,ER7Pla_Prev_MF)                         -0.29     0.34
cor(ER1Dis_Prev_MF,ER7Pla_Prev_MF)                           -0.31     0.35
cor(ER1Dis_Prev_Str_Overall,ER7Pla_Prev_MF)                  -0.36     0.31
cor(ER2Rum_Intercept,ER7Pla_Prev_MF)                         -0.20     0.44
cor(ER2Rum_Prev_MF,ER7Pla_Prev_MF)                           -0.33     0.37
cor(ER2Rum_Prev_Str_Overall,ER7Pla_Prev_MF)                  -0.33     0.35
cor(ER3SBl_Intercept,ER7Pla_Prev_MF)                         -0.21     0.42
cor(ER3SBl_Prev_MF,ER7Pla_Prev_MF)                           -0.33     0.37
cor(ER3SBl_Prev_Str_Overall,ER7Pla_Prev_MF)                  -0.35     0.34
cor(ER4ExprS_Intercept,ER7Pla_Prev_MF)                       -0.30     0.33
cor(ER4ExprS_Prev_MF,ER7Pla_Prev_MF)                         -0.34     0.36
cor(ER4ExprS_Prev_Str_Overall,ER7Pla_Prev_MF)                -0.37     0.31
cor(ER5ExperS_Intercept,ER7Pla_Prev_MF)                      -0.29     0.33
cor(ER5ExperS_Prev_MF,ER7Pla_Prev_MF)                        -0.33     0.36
cor(ER5ExperS_Prev_Str_Overall,ER7Pla_Prev_MF)               -0.40     0.27
cor(ER6Acc_Intercept,ER7Pla_Prev_MF)                         -0.21     0.43
cor(ER6Acc_Prev_MF,ER7Pla_Prev_MF)                           -0.31     0.38
cor(ER6Acc_Prev_Str_Overall,ER7Pla_Prev_MF)                  -0.33     0.36
cor(ER7Pla_Intercept,ER7Pla_Prev_MF)                         -0.42     0.26
cor(ER1Dis_Intercept,ER7Pla_Prev_Str_Overall)                -0.33     0.35
cor(ER1Dis_Prev_MF,ER7Pla_Prev_Str_Overall)                  -0.37     0.31
cor(ER1Dis_Prev_Str_Overall,ER7Pla_Prev_Str_Overall)         -0.30     0.39
cor(ER2Rum_Intercept,ER7Pla_Prev_Str_Overall)                -0.33     0.34
cor(ER2Rum_Prev_MF,ER7Pla_Prev_Str_Overall)                  -0.35     0.34
cor(ER2Rum_Prev_Str_Overall,ER7Pla_Prev_Str_Overall)         -0.33     0.37
cor(ER3SBl_Intercept,ER7Pla_Prev_Str_Overall)                -0.38     0.30
cor(ER3SBl_Prev_MF,ER7Pla_Prev_Str_Overall)                  -0.35     0.33
cor(ER3SBl_Prev_Str_Overall,ER7Pla_Prev_Str_Overall)         -0.35     0.35
cor(ER4ExprS_Intercept,ER7Pla_Prev_Str_Overall)              -0.41     0.27
cor(ER4ExprS_Prev_MF,ER7Pla_Prev_Str_Overall)                -0.35     0.34
cor(ER4ExprS_Prev_Str_Overall,ER7Pla_Prev_Str_Overall)       -0.32     0.38
cor(ER5ExperS_Intercept,ER7Pla_Prev_Str_Overall)             -0.38     0.29
cor(ER5ExperS_Prev_MF,ER7Pla_Prev_Str_Overall)               -0.35     0.34
cor(ER5ExperS_Prev_Str_Overall,ER7Pla_Prev_Str_Overall)      -0.33     0.35
cor(ER6Acc_Intercept,ER7Pla_Prev_Str_Overall)                -0.33     0.35
cor(ER6Acc_Prev_MF,ER7Pla_Prev_Str_Overall)                  -0.34     0.36
cor(ER6Acc_Prev_Str_Overall,ER7Pla_Prev_Str_Overall)         -0.33     0.38
cor(ER7Pla_Intercept,ER7Pla_Prev_Str_Overall)                -0.36     0.32
cor(ER7Pla_Prev_MF,ER7Pla_Prev_Str_Overall)                  -0.37     0.33
cor(ER1Dis_Intercept,ER8Rea_Intercept)                       -0.19     0.32
cor(ER1Dis_Prev_MF,ER8Rea_Intercept)                         -0.24     0.38
cor(ER1Dis_Prev_Str_Overall,ER8Rea_Intercept)                -0.32     0.25
cor(ER2Rum_Intercept,ER8Rea_Intercept)                        0.06     0.56
cor(ER2Rum_Prev_MF,ER8Rea_Intercept)                         -0.34     0.35
cor(ER2Rum_Prev_Str_Overall,ER8Rea_Intercept)                -0.18     0.43
cor(ER3SBl_Intercept,ER8Rea_Intercept)                       -0.03     0.48
cor(ER3SBl_Prev_MF,ER8Rea_Intercept)                         -0.32     0.35
cor(ER3SBl_Prev_Str_Overall,ER8Rea_Intercept)                -0.35     0.33
cor(ER4ExprS_Intercept,ER8Rea_Intercept)                     -0.07     0.43
cor(ER4ExprS_Prev_MF,ER8Rea_Intercept)                       -0.35     0.33
cor(ER4ExprS_Prev_Str_Overall,ER8Rea_Intercept)              -0.37     0.28
cor(ER5ExperS_Intercept,ER8Rea_Intercept)                    -0.17     0.32
cor(ER5ExperS_Prev_MF,ER8Rea_Intercept)                      -0.34     0.34
cor(ER5ExperS_Prev_Str_Overall,ER8Rea_Intercept)             -0.33     0.29
cor(ER6Acc_Intercept,ER8Rea_Intercept)                        0.13     0.58
cor(ER6Acc_Prev_MF,ER8Rea_Intercept)                         -0.31     0.36
cor(ER6Acc_Prev_Str_Overall,ER8Rea_Intercept)                -0.33     0.34
cor(ER7Pla_Intercept,ER8Rea_Intercept)                        0.15     0.62
cor(ER7Pla_Prev_MF,ER8Rea_Intercept)                         -0.19     0.47
cor(ER7Pla_Prev_Str_Overall,ER8Rea_Intercept)                -0.33     0.33
cor(ER1Dis_Intercept,ER8Rea_Prev_MF)                         -0.32     0.30
cor(ER1Dis_Prev_MF,ER8Rea_Prev_MF)                           -0.32     0.34
cor(ER1Dis_Prev_Str_Overall,ER8Rea_Prev_MF)                  -0.34     0.30
cor(ER2Rum_Intercept,ER8Rea_Prev_MF)                         -0.25     0.39
cor(ER2Rum_Prev_MF,ER8Rea_Prev_MF)                           -0.34     0.36
cor(ER2Rum_Prev_Str_Overall,ER8Rea_Prev_MF)                  -0.30     0.38
cor(ER3SBl_Intercept,ER8Rea_Prev_MF)                         -0.25     0.39
cor(ER3SBl_Prev_MF,ER8Rea_Prev_MF)                           -0.34     0.37
cor(ER3SBl_Prev_Str_Overall,ER8Rea_Prev_MF)                  -0.36     0.34
cor(ER4ExprS_Intercept,ER8Rea_Prev_MF)                       -0.22     0.42
cor(ER4ExprS_Prev_MF,ER8Rea_Prev_MF)                         -0.35     0.34
cor(ER4ExprS_Prev_Str_Overall,ER8Rea_Prev_MF)                -0.36     0.31
cor(ER5ExperS_Intercept,ER8Rea_Prev_MF)                      -0.32     0.30
cor(ER5ExperS_Prev_MF,ER8Rea_Prev_MF)                        -0.34     0.36
cor(ER5ExperS_Prev_Str_Overall,ER8Rea_Prev_MF)               -0.30     0.36
cor(ER6Acc_Intercept,ER8Rea_Prev_MF)                         -0.17     0.47
cor(ER6Acc_Prev_MF,ER8Rea_Prev_MF)                           -0.30     0.41
cor(ER6Acc_Prev_Str_Overall,ER8Rea_Prev_MF)                  -0.34     0.35
cor(ER7Pla_Intercept,ER8Rea_Prev_MF)                         -0.23     0.42
cor(ER7Pla_Prev_MF,ER8Rea_Prev_MF)                           -0.24     0.45
cor(ER7Pla_Prev_Str_Overall,ER8Rea_Prev_MF)                  -0.38     0.31
cor(ER8Rea_Intercept,ER8Rea_Prev_MF)                         -0.35     0.33
cor(ER1Dis_Intercept,ER8Rea_Prev_Str_Overall)                -0.34     0.33
cor(ER1Dis_Prev_MF,ER8Rea_Prev_Str_Overall)                  -0.35     0.35
cor(ER1Dis_Prev_Str_Overall,ER8Rea_Prev_Str_Overall)         -0.32     0.36
cor(ER2Rum_Intercept,ER8Rea_Prev_Str_Overall)                -0.32     0.36
cor(ER2Rum_Prev_MF,ER8Rea_Prev_Str_Overall)                  -0.35     0.34
cor(ER2Rum_Prev_Str_Overall,ER8Rea_Prev_Str_Overall)         -0.30     0.42
cor(ER3SBl_Intercept,ER8Rea_Prev_Str_Overall)                -0.32     0.37
cor(ER3SBl_Prev_MF,ER8Rea_Prev_Str_Overall)                  -0.34     0.35
cor(ER3SBl_Prev_Str_Overall,ER8Rea_Prev_Str_Overall)         -0.35     0.35
cor(ER4ExprS_Intercept,ER8Rea_Prev_Str_Overall)              -0.33     0.36
cor(ER4ExprS_Prev_MF,ER8Rea_Prev_Str_Overall)                -0.35     0.35
cor(ER4ExprS_Prev_Str_Overall,ER8Rea_Prev_Str_Overall)       -0.34     0.36
cor(ER5ExperS_Intercept,ER8Rea_Prev_Str_Overall)             -0.35     0.32
cor(ER5ExperS_Prev_MF,ER8Rea_Prev_Str_Overall)               -0.34     0.35
cor(ER5ExperS_Prev_Str_Overall,ER8Rea_Prev_Str_Overall)      -0.31     0.39
cor(ER6Acc_Intercept,ER8Rea_Prev_Str_Overall)                -0.30     0.38
cor(ER6Acc_Prev_MF,ER8Rea_Prev_Str_Overall)                  -0.34     0.35
cor(ER6Acc_Prev_Str_Overall,ER8Rea_Prev_Str_Overall)         -0.34     0.36
cor(ER7Pla_Intercept,ER8Rea_Prev_Str_Overall)                -0.31     0.37
cor(ER7Pla_Prev_MF,ER8Rea_Prev_Str_Overall)                  -0.34     0.36
cor(ER7Pla_Prev_Str_Overall,ER8Rea_Prev_Str_Overall)         -0.34     0.35
cor(ER8Rea_Intercept,ER8Rea_Prev_Str_Overall)                -0.34     0.35
cor(ER8Rea_Prev_MF,ER8Rea_Prev_Str_Overall)                  -0.34     0.34
cor(ER1Dis_Intercept,ER9ESu_Intercept)                       -0.07     0.41
cor(ER1Dis_Prev_MF,ER9ESu_Intercept)                         -0.15     0.45
cor(ER1Dis_Prev_Str_Overall,ER9ESu_Intercept)                -0.30     0.26
cor(ER2Rum_Intercept,ER9ESu_Intercept)                        0.15     0.60
cor(ER2Rum_Prev_MF,ER9ESu_Intercept)                         -0.31     0.37
cor(ER2Rum_Prev_Str_Overall,ER9ESu_Intercept)                -0.28     0.33
cor(ER3SBl_Intercept,ER9ESu_Intercept)                        0.07     0.56
cor(ER3SBl_Prev_MF,ER9ESu_Intercept)                         -0.27     0.41
cor(ER3SBl_Prev_Str_Overall,ER9ESu_Intercept)                -0.34     0.34
cor(ER4ExprS_Intercept,ER9ESu_Intercept)                      0.06     0.52
cor(ER4ExprS_Prev_MF,ER9ESu_Intercept)                       -0.32     0.36
cor(ER4ExprS_Prev_Str_Overall,ER9ESu_Intercept)              -0.39     0.25
cor(ER5ExperS_Intercept,ER9ESu_Intercept)                     0.04     0.51
cor(ER5ExperS_Prev_MF,ER9ESu_Intercept)                      -0.33     0.36
cor(ER5ExperS_Prev_Str_Overall,ER9ESu_Intercept)             -0.39     0.20
cor(ER6Acc_Intercept,ER9ESu_Intercept)                        0.25     0.65
cor(ER6Acc_Prev_MF,ER9ESu_Intercept)                         -0.30     0.38
cor(ER6Acc_Prev_Str_Overall,ER9ESu_Intercept)                -0.34     0.34
cor(ER7Pla_Intercept,ER9ESu_Intercept)                        0.12     0.57
cor(ER7Pla_Prev_MF,ER9ESu_Intercept)                         -0.18     0.47
cor(ER7Pla_Prev_Str_Overall,ER9ESu_Intercept)                -0.36     0.31
cor(ER8Rea_Intercept,ER9ESu_Intercept)                        0.08     0.58
cor(ER8Rea_Prev_MF,ER9ESu_Intercept)                         -0.20     0.45
cor(ER8Rea_Prev_Str_Overall,ER9ESu_Intercept)                -0.33     0.36
cor(ER1Dis_Intercept,ER9ESu_Prev_MF)                         -0.34     0.29
cor(ER1Dis_Prev_MF,ER9ESu_Prev_MF)                           -0.32     0.36
cor(ER1Dis_Prev_Str_Overall,ER9ESu_Prev_MF)                  -0.23     0.43
cor(ER2Rum_Intercept,ER9ESu_Prev_MF)                         -0.32     0.32
cor(ER2Rum_Prev_MF,ER9ESu_Prev_MF)                           -0.33     0.37
cor(ER2Rum_Prev_Str_Overall,ER9ESu_Prev_MF)                  -0.33     0.34
cor(ER3SBl_Intercept,ER9ESu_Prev_MF)                         -0.33     0.31
cor(ER3SBl_Prev_MF,ER9ESu_Prev_MF)                           -0.32     0.38
cor(ER3SBl_Prev_Str_Overall,ER9ESu_Prev_MF)                  -0.34     0.37
cor(ER4ExprS_Intercept,ER9ESu_Prev_MF)                       -0.33     0.32
cor(ER4ExprS_Prev_MF,ER9ESu_Prev_MF)                         -0.34     0.35
cor(ER4ExprS_Prev_Str_Overall,ER9ESu_Prev_MF)                -0.31     0.38
cor(ER5ExperS_Intercept,ER9ESu_Prev_MF)                      -0.37     0.27
cor(ER5ExperS_Prev_MF,ER9ESu_Prev_MF)                        -0.34     0.36
cor(ER5ExperS_Prev_Str_Overall,ER9ESu_Prev_MF)               -0.35     0.32
cor(ER6Acc_Intercept,ER9ESu_Prev_MF)                         -0.31     0.34
cor(ER6Acc_Prev_MF,ER9ESu_Prev_MF)                           -0.36     0.34
cor(ER6Acc_Prev_Str_Overall,ER9ESu_Prev_MF)                  -0.33     0.37
cor(ER7Pla_Intercept,ER9ESu_Prev_MF)                         -0.35     0.30
cor(ER7Pla_Prev_MF,ER9ESu_Prev_MF)                           -0.29     0.40
cor(ER7Pla_Prev_Str_Overall,ER9ESu_Prev_MF)                  -0.35     0.36
cor(ER8Rea_Intercept,ER9ESu_Prev_MF)                         -0.29     0.37
cor(ER8Rea_Prev_MF,ER9ESu_Prev_MF)                           -0.27     0.42
cor(ER8Rea_Prev_Str_Overall,ER9ESu_Prev_MF)                  -0.35     0.35
cor(ER9ESu_Intercept,ER9ESu_Prev_MF)                         -0.48     0.21
cor(ER1Dis_Intercept,ER9ESu_Prev_Str_Overall)                -0.42     0.24
cor(ER1Dis_Prev_MF,ER9ESu_Prev_Str_Overall)                  -0.42     0.28
cor(ER1Dis_Prev_Str_Overall,ER9ESu_Prev_Str_Overall)         -0.27     0.40
cor(ER2Rum_Intercept,ER9ESu_Prev_Str_Overall)                -0.33     0.31
cor(ER2Rum_Prev_MF,ER9ESu_Prev_Str_Overall)                  -0.36     0.35
cor(ER2Rum_Prev_Str_Overall,ER9ESu_Prev_Str_Overall)         -0.30     0.39
cor(ER3SBl_Intercept,ER9ESu_Prev_Str_Overall)                -0.35     0.31
cor(ER3SBl_Prev_MF,ER9ESu_Prev_Str_Overall)                  -0.35     0.34
cor(ER3SBl_Prev_Str_Overall,ER9ESu_Prev_Str_Overall)         -0.34     0.35
cor(ER4ExprS_Intercept,ER9ESu_Prev_Str_Overall)              -0.38     0.27
cor(ER4ExprS_Prev_MF,ER9ESu_Prev_Str_Overall)                -0.36     0.33
cor(ER4ExprS_Prev_Str_Overall,ER9ESu_Prev_Str_Overall)       -0.34     0.36
cor(ER5ExperS_Intercept,ER9ESu_Prev_Str_Overall)             -0.43     0.23
cor(ER5ExperS_Prev_MF,ER9ESu_Prev_Str_Overall)               -0.36     0.34
cor(ER5ExperS_Prev_Str_Overall,ER9ESu_Prev_Str_Overall)      -0.31     0.37
cor(ER6Acc_Intercept,ER9ESu_Prev_Str_Overall)                -0.32     0.33
cor(ER6Acc_Prev_MF,ER9ESu_Prev_Str_Overall)                  -0.33     0.37
cor(ER6Acc_Prev_Str_Overall,ER9ESu_Prev_Str_Overall)         -0.32     0.38
cor(ER7Pla_Intercept,ER9ESu_Prev_Str_Overall)                -0.31     0.33
cor(ER7Pla_Prev_MF,ER9ESu_Prev_Str_Overall)                  -0.32     0.36
cor(ER7Pla_Prev_Str_Overall,ER9ESu_Prev_Str_Overall)         -0.32     0.37
cor(ER8Rea_Intercept,ER9ESu_Prev_Str_Overall)                -0.26     0.40
cor(ER8Rea_Prev_MF,ER9ESu_Prev_Str_Overall)                  -0.29     0.40
cor(ER8Rea_Prev_Str_Overall,ER9ESu_Prev_Str_Overall)         -0.33     0.38
cor(ER9ESu_Intercept,ER9ESu_Prev_Str_Overall)                -0.41     0.25
cor(ER9ESu_Prev_MF,ER9ESu_Prev_Str_Overall)                  -0.33     0.35
cor(ER1Dis_Intercept,ER10Rel_Intercept)                       0.21     0.59
cor(ER1Dis_Prev_MF,ER10Rel_Intercept)                        -0.21     0.38
cor(ER1Dis_Prev_Str_Overall,ER10Rel_Intercept)               -0.29     0.24
cor(ER2Rum_Intercept,ER10Rel_Intercept)                      -0.01     0.43
cor(ER2Rum_Prev_MF,ER10Rel_Intercept)                        -0.33     0.34
cor(ER2Rum_Prev_Str_Overall,ER10Rel_Intercept)               -0.38     0.23
cor(ER3SBl_Intercept,ER10Rel_Intercept)                      -0.09     0.38
cor(ER3SBl_Prev_MF,ER10Rel_Intercept)                        -0.30     0.36
cor(ER3SBl_Prev_Str_Overall,ER10Rel_Intercept)               -0.31     0.37
cor(ER4ExprS_Intercept,ER10Rel_Intercept)                    -0.21     0.24
cor(ER4ExprS_Prev_MF,ER10Rel_Intercept)                      -0.31     0.36
cor(ER4ExprS_Prev_Str_Overall,ER10Rel_Intercept)             -0.30     0.33
cor(ER5ExperS_Intercept,ER10Rel_Intercept)                   -0.03     0.37
cor(ER5ExperS_Prev_MF,ER10Rel_Intercept)                     -0.34     0.35
cor(ER5ExperS_Prev_Str_Overall,ER10Rel_Intercept)            -0.37     0.21
cor(ER6Acc_Intercept,ER10Rel_Intercept)                      -0.02     0.37
cor(ER6Acc_Prev_MF,ER10Rel_Intercept)                        -0.28     0.39
cor(ER6Acc_Prev_Str_Overall,ER10Rel_Intercept)               -0.30     0.38
cor(ER7Pla_Intercept,ER10Rel_Intercept)                       0.03     0.44
cor(ER7Pla_Prev_MF,ER10Rel_Intercept)                        -0.15     0.49
cor(ER7Pla_Prev_Str_Overall,ER10Rel_Intercept)               -0.28     0.40
cor(ER8Rea_Intercept,ER10Rel_Intercept)                      -0.04     0.43
cor(ER8Rea_Prev_MF,ER10Rel_Intercept)                        -0.30     0.31
cor(ER8Rea_Prev_Str_Overall,ER10Rel_Intercept)               -0.37     0.31
cor(ER9ESu_Intercept,ER10Rel_Intercept)                       0.06     0.49
cor(ER9ESu_Prev_MF,ER10Rel_Intercept)                        -0.35     0.26
cor(ER9ESu_Prev_Str_Overall,ER10Rel_Intercept)               -0.39     0.26
cor(ER1Dis_Intercept,ER10Rel_Prev_MF)                        -0.39     0.26
cor(ER1Dis_Prev_MF,ER10Rel_Prev_MF)                          -0.34     0.35
cor(ER1Dis_Prev_Str_Overall,ER10Rel_Prev_MF)                 -0.27     0.42
cor(ER2Rum_Intercept,ER10Rel_Prev_MF)                        -0.35     0.28
cor(ER2Rum_Prev_MF,ER10Rel_Prev_MF)                          -0.36     0.34
cor(ER2Rum_Prev_Str_Overall,ER10Rel_Prev_MF)                 -0.38     0.30
cor(ER3SBl_Intercept,ER10Rel_Prev_MF)                        -0.36     0.29
cor(ER3SBl_Prev_MF,ER10Rel_Prev_MF)                          -0.35     0.34
cor(ER3SBl_Prev_Str_Overall,ER10Rel_Prev_MF)                 -0.37     0.32
cor(ER4ExprS_Intercept,ER10Rel_Prev_MF)                      -0.26     0.39
cor(ER4ExprS_Prev_MF,ER10Rel_Prev_MF)                        -0.36     0.36
cor(ER4ExprS_Prev_Str_Overall,ER10Rel_Prev_MF)               -0.31     0.38
cor(ER5ExperS_Intercept,ER10Rel_Prev_MF)                     -0.29     0.34
cor(ER5ExperS_Prev_MF,ER10Rel_Prev_MF)                       -0.34     0.35
cor(ER5ExperS_Prev_Str_Overall,ER10Rel_Prev_MF)              -0.35     0.33
cor(ER6Acc_Intercept,ER10Rel_Prev_MF)                        -0.25     0.38
cor(ER6Acc_Prev_MF,ER10Rel_Prev_MF)                          -0.35     0.35
cor(ER6Acc_Prev_Str_Overall,ER10Rel_Prev_MF)                 -0.35     0.35
cor(ER7Pla_Intercept,ER10Rel_Prev_MF)                        -0.29     0.35
cor(ER7Pla_Prev_MF,ER10Rel_Prev_MF)                          -0.34     0.35
cor(ER7Pla_Prev_Str_Overall,ER10Rel_Prev_MF)                 -0.35     0.34
cor(ER8Rea_Intercept,ER10Rel_Prev_MF)                        -0.34     0.31
cor(ER8Rea_Prev_MF,ER10Rel_Prev_MF)                          -0.33     0.36
cor(ER8Rea_Prev_Str_Overall,ER10Rel_Prev_MF)                 -0.37     0.34
cor(ER9ESu_Intercept,ER10Rel_Prev_MF)                        -0.26     0.39
cor(ER9ESu_Prev_MF,ER10Rel_Prev_MF)                          -0.29     0.40
cor(ER9ESu_Prev_Str_Overall,ER10Rel_Prev_MF)                 -0.34     0.34
cor(ER10Rel_Intercept,ER10Rel_Prev_MF)                       -0.50     0.20
cor(ER1Dis_Intercept,ER10Rel_Prev_Str_Overall)               -0.44     0.17
cor(ER1Dis_Prev_MF,ER10Rel_Prev_Str_Overall)                 -0.39     0.28
cor(ER1Dis_Prev_Str_Overall,ER10Rel_Prev_Str_Overall)        -0.15     0.50
cor(ER2Rum_Intercept,ER10Rel_Prev_Str_Overall)               -0.25     0.35
cor(ER2Rum_Prev_MF,ER10Rel_Prev_Str_Overall)                 -0.34     0.35
cor(ER2Rum_Prev_Str_Overall,ER10Rel_Prev_Str_Overall)        -0.34     0.34
cor(ER3SBl_Intercept,ER10Rel_Prev_Str_Overall)               -0.27     0.34
cor(ER3SBl_Prev_MF,ER10Rel_Prev_Str_Overall)                 -0.33     0.36
cor(ER3SBl_Prev_Str_Overall,ER10Rel_Prev_Str_Overall)        -0.34     0.36
cor(ER4ExprS_Intercept,ER10Rel_Prev_Str_Overall)             -0.22     0.39
cor(ER4ExprS_Prev_MF,ER10Rel_Prev_Str_Overall)               -0.34     0.36
cor(ER4ExprS_Prev_Str_Overall,ER10Rel_Prev_Str_Overall)      -0.31     0.38
cor(ER5ExperS_Intercept,ER10Rel_Prev_Str_Overall)            -0.25     0.35
cor(ER5ExperS_Prev_MF,ER10Rel_Prev_Str_Overall)              -0.35     0.35
cor(ER5ExperS_Prev_Str_Overall,ER10Rel_Prev_Str_Overall)     -0.30     0.37
cor(ER6Acc_Intercept,ER10Rel_Prev_Str_Overall)               -0.22     0.35
cor(ER6Acc_Prev_MF,ER10Rel_Prev_Str_Overall)                 -0.37     0.32
cor(ER6Acc_Prev_Str_Overall,ER10Rel_Prev_Str_Overall)        -0.34     0.35
cor(ER7Pla_Intercept,ER10Rel_Prev_Str_Overall)               -0.31     0.29
cor(ER7Pla_Prev_MF,ER10Rel_Prev_Str_Overall)                 -0.33     0.34
cor(ER7Pla_Prev_Str_Overall,ER10Rel_Prev_Str_Overall)        -0.33     0.36
cor(ER8Rea_Intercept,ER10Rel_Prev_Str_Overall)               -0.33     0.28
cor(ER8Rea_Prev_MF,ER10Rel_Prev_Str_Overall)                 -0.32     0.33
cor(ER8Rea_Prev_Str_Overall,ER10Rel_Prev_Str_Overall)        -0.33     0.36
cor(ER9ESu_Intercept,ER10Rel_Prev_Str_Overall)               -0.27     0.34
cor(ER9ESu_Prev_MF,ER10Rel_Prev_Str_Overall)                 -0.29     0.39
cor(ER9ESu_Prev_Str_Overall,ER10Rel_Prev_Str_Overall)        -0.32     0.37
cor(ER10Rel_Intercept,ER10Rel_Prev_Str_Overall)              -0.48     0.12
cor(ER10Rel_Prev_MF,ER10Rel_Prev_Str_Overall)                -0.30     0.38
                                                          Rhat Bulk_ESS
sd(ER1Dis_Intercept)                                      1.00     7948
sd(ER1Dis_Prev_MF)                                        1.00     1466
sd(ER1Dis_Prev_Str_Overall)                               1.00     3822
sd(ER2Rum_Intercept)                                      1.00     5870
sd(ER2Rum_Prev_MF)                                        1.00     2486
sd(ER2Rum_Prev_Str_Overall)                               1.00     2691
sd(ER3SBl_Intercept)                                      1.00     5912
sd(ER3SBl_Prev_MF)                                        1.00     2114
sd(ER3SBl_Prev_Str_Overall)                               1.00     4177
sd(ER4ExprS_Intercept)                                    1.00    10015
sd(ER4ExprS_Prev_MF)                                      1.00     4374
sd(ER4ExprS_Prev_Str_Overall)                             1.00     2861
sd(ER5ExperS_Intercept)                                   1.00    10234
sd(ER5ExperS_Prev_MF)                                     1.00     5872
sd(ER5ExperS_Prev_Str_Overall)                            1.00     5160
sd(ER6Acc_Intercept)                                      1.00    10280
sd(ER6Acc_Prev_MF)                                        1.00     2956
sd(ER6Acc_Prev_Str_Overall)                               1.00     4443
sd(ER7Pla_Intercept)                                      1.00    11305
sd(ER7Pla_Prev_MF)                                        1.00     3048
sd(ER7Pla_Prev_Str_Overall)                               1.00     4558
sd(ER8Rea_Intercept)                                      1.00     6347
sd(ER8Rea_Prev_MF)                                        1.00     2523
sd(ER8Rea_Prev_Str_Overall)                               1.00     6835
sd(ER9ESu_Intercept)                                      1.00     9496
sd(ER9ESu_Prev_MF)                                        1.00     2753
sd(ER9ESu_Prev_Str_Overall)                               1.00     3534
sd(ER10Rel_Intercept)                                     1.00     9028
sd(ER10Rel_Prev_MF)                                       1.00     3101
sd(ER10Rel_Prev_Str_Overall)                              1.00     4420
cor(ER1Dis_Intercept,ER1Dis_Prev_MF)                      1.00    10821
cor(ER1Dis_Intercept,ER1Dis_Prev_Str_Overall)             1.00     9870
cor(ER1Dis_Prev_MF,ER1Dis_Prev_Str_Overall)               1.00     5254
cor(ER1Dis_Intercept,ER2Rum_Intercept)                    1.00     7266
cor(ER1Dis_Prev_MF,ER2Rum_Intercept)                      1.00     2807
cor(ER1Dis_Prev_Str_Overall,ER2Rum_Intercept)             1.00     6330
cor(ER1Dis_Intercept,ER2Rum_Prev_MF)                      1.00    25359
cor(ER1Dis_Prev_MF,ER2Rum_Prev_MF)                        1.00    17530
cor(ER1Dis_Prev_Str_Overall,ER2Rum_Prev_MF)               1.00    19398
cor(ER2Rum_Intercept,ER2Rum_Prev_MF)                      1.00    21235
cor(ER1Dis_Intercept,ER2Rum_Prev_Str_Overall)             1.00    14956
cor(ER1Dis_Prev_MF,ER2Rum_Prev_Str_Overall)               1.00     8536
cor(ER1Dis_Prev_Str_Overall,ER2Rum_Prev_Str_Overall)      1.00    13225
cor(ER2Rum_Intercept,ER2Rum_Prev_Str_Overall)             1.00    15757
cor(ER2Rum_Prev_MF,ER2Rum_Prev_Str_Overall)               1.00     7990
cor(ER1Dis_Intercept,ER3SBl_Intercept)                    1.00     9369
cor(ER1Dis_Prev_MF,ER3SBl_Intercept)                      1.00     3744
cor(ER1Dis_Prev_Str_Overall,ER3SBl_Intercept)             1.00     7210
cor(ER2Rum_Intercept,ER3SBl_Intercept)                    1.00     7402
cor(ER2Rum_Prev_MF,ER3SBl_Intercept)                      1.00     4515
cor(ER2Rum_Prev_Str_Overall,ER3SBl_Intercept)             1.00     6612
cor(ER1Dis_Intercept,ER3SBl_Prev_MF)                      1.00    21874
cor(ER1Dis_Prev_MF,ER3SBl_Prev_MF)                        1.00    16152
cor(ER1Dis_Prev_Str_Overall,ER3SBl_Prev_MF)               1.00    17591
cor(ER2Rum_Intercept,ER3SBl_Prev_MF)                      1.00     7893
cor(ER2Rum_Prev_MF,ER3SBl_Prev_MF)                        1.00    10986
cor(ER2Rum_Prev_Str_Overall,ER3SBl_Prev_MF)               1.00    17217
cor(ER3SBl_Intercept,ER3SBl_Prev_MF)                      1.00    18388
cor(ER1Dis_Intercept,ER3SBl_Prev_Str_Overall)             1.00    23992
cor(ER1Dis_Prev_MF,ER3SBl_Prev_Str_Overall)               1.00    22907
cor(ER1Dis_Prev_Str_Overall,ER3SBl_Prev_Str_Overall)      1.00    22632
cor(ER2Rum_Intercept,ER3SBl_Prev_Str_Overall)             1.00    17245
cor(ER2Rum_Prev_MF,ER3SBl_Prev_Str_Overall)               1.00    14783
cor(ER2Rum_Prev_Str_Overall,ER3SBl_Prev_Str_Overall)      1.00    16532
cor(ER3SBl_Intercept,ER3SBl_Prev_Str_Overall)             1.00    20448
cor(ER3SBl_Prev_MF,ER3SBl_Prev_Str_Overall)               1.00    13567
cor(ER1Dis_Intercept,ER4ExprS_Intercept)                  1.00     6920
cor(ER1Dis_Prev_MF,ER4ExprS_Intercept)                    1.00     3545
cor(ER1Dis_Prev_Str_Overall,ER4ExprS_Intercept)           1.00     5832
cor(ER2Rum_Intercept,ER4ExprS_Intercept)                  1.00     5129
cor(ER2Rum_Prev_MF,ER4ExprS_Intercept)                    1.00     2608
cor(ER2Rum_Prev_Str_Overall,ER4ExprS_Intercept)           1.00     4394
cor(ER3SBl_Intercept,ER4ExprS_Intercept)                  1.00     5955
cor(ER3SBl_Prev_MF,ER4ExprS_Intercept)                    1.00     3041
cor(ER3SBl_Prev_Str_Overall,ER4ExprS_Intercept)           1.00     3181
cor(ER1Dis_Intercept,ER4ExprS_Prev_MF)                    1.00    24700
cor(ER1Dis_Prev_MF,ER4ExprS_Prev_MF)                      1.00    22402
cor(ER1Dis_Prev_Str_Overall,ER4ExprS_Prev_MF)             1.00    24033
cor(ER2Rum_Intercept,ER4ExprS_Prev_MF)                    1.00    22389
cor(ER2Rum_Prev_MF,ER4ExprS_Prev_MF)                      1.00    15526
cor(ER2Rum_Prev_Str_Overall,ER4ExprS_Prev_MF)             1.00    18307
cor(ER3SBl_Intercept,ER4ExprS_Prev_MF)                    1.00    18790
cor(ER3SBl_Prev_MF,ER4ExprS_Prev_MF)                      1.00    13352
cor(ER3SBl_Prev_Str_Overall,ER4ExprS_Prev_MF)             1.00    14317
cor(ER4ExprS_Intercept,ER4ExprS_Prev_MF)                  1.00    19408
cor(ER1Dis_Intercept,ER4ExprS_Prev_Str_Overall)           1.00    20635
cor(ER1Dis_Prev_MF,ER4ExprS_Prev_Str_Overall)             1.00    13992
cor(ER1Dis_Prev_Str_Overall,ER4ExprS_Prev_Str_Overall)    1.00    11947
cor(ER2Rum_Intercept,ER4ExprS_Prev_Str_Overall)           1.00    16609
cor(ER2Rum_Prev_MF,ER4ExprS_Prev_Str_Overall)             1.00    12953
cor(ER2Rum_Prev_Str_Overall,ER4ExprS_Prev_Str_Overall)    1.00    13147
cor(ER3SBl_Intercept,ER4ExprS_Prev_Str_Overall)           1.00    16230
cor(ER3SBl_Prev_MF,ER4ExprS_Prev_Str_Overall)             1.00    12857
cor(ER3SBl_Prev_Str_Overall,ER4ExprS_Prev_Str_Overall)    1.00    12045
cor(ER4ExprS_Intercept,ER4ExprS_Prev_Str_Overall)         1.00    11306
cor(ER4ExprS_Prev_MF,ER4ExprS_Prev_Str_Overall)           1.00    10626
cor(ER1Dis_Intercept,ER5ExperS_Intercept)                 1.00     6281
cor(ER1Dis_Prev_MF,ER5ExperS_Intercept)                   1.00     2940
cor(ER1Dis_Prev_Str_Overall,ER5ExperS_Intercept)          1.00     5392
cor(ER2Rum_Intercept,ER5ExperS_Intercept)                 1.00     5603
cor(ER2Rum_Prev_MF,ER5ExperS_Intercept)                   1.00     2855
cor(ER2Rum_Prev_Str_Overall,ER5ExperS_Intercept)          1.00     4981
cor(ER3SBl_Intercept,ER5ExperS_Intercept)                 1.00     6383
cor(ER3SBl_Prev_MF,ER5ExperS_Intercept)                   1.00     3539
cor(ER3SBl_Prev_Str_Overall,ER5ExperS_Intercept)          1.00     3852
cor(ER4ExprS_Intercept,ER5ExperS_Intercept)               1.00     6956
cor(ER4ExprS_Prev_MF,ER5ExperS_Intercept)                 1.00     6449
cor(ER4ExprS_Prev_Str_Overall,ER5ExperS_Intercept)        1.00     8915
cor(ER1Dis_Intercept,ER5ExperS_Prev_MF)                   1.00    27185
cor(ER1Dis_Prev_MF,ER5ExperS_Prev_MF)                     1.00    25279
cor(ER1Dis_Prev_Str_Overall,ER5ExperS_Prev_MF)            1.00    23504
cor(ER2Rum_Intercept,ER5ExperS_Prev_MF)                   1.00    24337
cor(ER2Rum_Prev_MF,ER5ExperS_Prev_MF)                     1.00    16487
cor(ER2Rum_Prev_Str_Overall,ER5ExperS_Prev_MF)            1.00    19475
cor(ER3SBl_Intercept,ER5ExperS_Prev_MF)                   1.00    20875
cor(ER3SBl_Prev_MF,ER5ExperS_Prev_MF)                     1.00    13942
cor(ER3SBl_Prev_Str_Overall,ER5ExperS_Prev_MF)            1.00    12387
cor(ER4ExprS_Intercept,ER5ExperS_Prev_MF)                 1.00    18404
cor(ER4ExprS_Prev_MF,ER5ExperS_Prev_MF)                   1.00    10523
cor(ER4ExprS_Prev_Str_Overall,ER5ExperS_Prev_MF)          1.00    12145
cor(ER5ExperS_Intercept,ER5ExperS_Prev_MF)                1.00    18628
cor(ER1Dis_Intercept,ER5ExperS_Prev_Str_Overall)          1.00    14089
cor(ER1Dis_Prev_MF,ER5ExperS_Prev_Str_Overall)            1.00     8856
cor(ER1Dis_Prev_Str_Overall,ER5ExperS_Prev_Str_Overall)   1.00    13344
cor(ER2Rum_Intercept,ER5ExperS_Prev_Str_Overall)          1.00    12131
cor(ER2Rum_Prev_MF,ER5ExperS_Prev_Str_Overall)            1.00     8918
cor(ER2Rum_Prev_Str_Overall,ER5ExperS_Prev_Str_Overall)   1.00     9663
cor(ER3SBl_Intercept,ER5ExperS_Prev_Str_Overall)          1.00    12071
cor(ER3SBl_Prev_MF,ER5ExperS_Prev_Str_Overall)            1.00     8638
cor(ER3SBl_Prev_Str_Overall,ER5ExperS_Prev_Str_Overall)   1.00     9805
cor(ER4ExprS_Intercept,ER5ExperS_Prev_Str_Overall)        1.00    10367
cor(ER4ExprS_Prev_MF,ER5ExperS_Prev_Str_Overall)          1.00     9337
cor(ER4ExprS_Prev_Str_Overall,ER5ExperS_Prev_Str_Overall) 1.00     7208
cor(ER5ExperS_Intercept,ER5ExperS_Prev_Str_Overall)       1.00    14711
cor(ER5ExperS_Prev_MF,ER5ExperS_Prev_Str_Overall)         1.00     9000
cor(ER1Dis_Intercept,ER6Acc_Intercept)                    1.00     6074
cor(ER1Dis_Prev_MF,ER6Acc_Intercept)                      1.00     2832
cor(ER1Dis_Prev_Str_Overall,ER6Acc_Intercept)             1.00     4138
cor(ER2Rum_Intercept,ER6Acc_Intercept)                    1.00     4497
cor(ER2Rum_Prev_MF,ER6Acc_Intercept)                      1.00     2367
cor(ER2Rum_Prev_Str_Overall,ER6Acc_Intercept)             1.00     3964
cor(ER3SBl_Intercept,ER6Acc_Intercept)                    1.00     5080
cor(ER3SBl_Prev_MF,ER6Acc_Intercept)                      1.00     2599
cor(ER3SBl_Prev_Str_Overall,ER6Acc_Intercept)             1.00     2811
cor(ER4ExprS_Intercept,ER6Acc_Intercept)                  1.00     6795
cor(ER4ExprS_Prev_MF,ER6Acc_Intercept)                    1.00     4463
cor(ER4ExprS_Prev_Str_Overall,ER6Acc_Intercept)           1.00     4028
cor(ER5ExperS_Intercept,ER6Acc_Intercept)                 1.00     9039
cor(ER5ExperS_Prev_MF,ER6Acc_Intercept)                   1.00     4586
cor(ER5ExperS_Prev_Str_Overall,ER6Acc_Intercept)          1.00     6120
cor(ER1Dis_Intercept,ER6Acc_Prev_MF)                      1.00    24694
cor(ER1Dis_Prev_MF,ER6Acc_Prev_MF)                        1.00    19749
cor(ER1Dis_Prev_Str_Overall,ER6Acc_Prev_MF)               1.00    19916
cor(ER2Rum_Intercept,ER6Acc_Prev_MF)                      1.00    22158
cor(ER2Rum_Prev_MF,ER6Acc_Prev_MF)                        1.00    14883
cor(ER2Rum_Prev_Str_Overall,ER6Acc_Prev_MF)               1.00    16251
cor(ER3SBl_Intercept,ER6Acc_Prev_MF)                      1.00    17830
cor(ER3SBl_Prev_MF,ER6Acc_Prev_MF)                        1.00    11903
cor(ER3SBl_Prev_Str_Overall,ER6Acc_Prev_MF)               1.00    11900
cor(ER4ExprS_Intercept,ER6Acc_Prev_MF)                    1.00    17949
cor(ER4ExprS_Prev_MF,ER6Acc_Prev_MF)                      1.00    11097
cor(ER4ExprS_Prev_Str_Overall,ER6Acc_Prev_MF)             1.00    12367
cor(ER5ExperS_Intercept,ER6Acc_Prev_MF)                   1.00    18376
cor(ER5ExperS_Prev_MF,ER6Acc_Prev_MF)                     1.00     8278
cor(ER5ExperS_Prev_Str_Overall,ER6Acc_Prev_MF)            1.00    12385
cor(ER6Acc_Intercept,ER6Acc_Prev_MF)                      1.00    11249
cor(ER1Dis_Intercept,ER6Acc_Prev_Str_Overall)             1.00    24827
cor(ER1Dis_Prev_MF,ER6Acc_Prev_Str_Overall)               1.00    20071
cor(ER1Dis_Prev_Str_Overall,ER6Acc_Prev_Str_Overall)      1.00    16398
cor(ER2Rum_Intercept,ER6Acc_Prev_Str_Overall)             1.00    23842
cor(ER2Rum_Prev_MF,ER6Acc_Prev_Str_Overall)               1.00    15802
cor(ER2Rum_Prev_Str_Overall,ER6Acc_Prev_Str_Overall)      1.00    14749
cor(ER3SBl_Intercept,ER6Acc_Prev_Str_Overall)             1.00    21779
cor(ER3SBl_Prev_MF,ER6Acc_Prev_Str_Overall)               1.00    13931
cor(ER3SBl_Prev_Str_Overall,ER6Acc_Prev_Str_Overall)      1.00    12170
cor(ER4ExprS_Intercept,ER6Acc_Prev_Str_Overall)           1.00    19585
cor(ER4ExprS_Prev_MF,ER6Acc_Prev_Str_Overall)             1.00    10997
cor(ER4ExprS_Prev_Str_Overall,ER6Acc_Prev_Str_Overall)    1.00    10534
cor(ER5ExperS_Intercept,ER6Acc_Prev_Str_Overall)          1.00    20939
cor(ER5ExperS_Prev_MF,ER6Acc_Prev_Str_Overall)            1.00     9056
cor(ER5ExperS_Prev_Str_Overall,ER6Acc_Prev_Str_Overall)   1.00    13436
cor(ER6Acc_Intercept,ER6Acc_Prev_Str_Overall)             1.00    20426
cor(ER6Acc_Prev_MF,ER6Acc_Prev_Str_Overall)               1.00     8884
cor(ER1Dis_Intercept,ER7Pla_Intercept)                    1.00     9109
cor(ER1Dis_Prev_MF,ER7Pla_Intercept)                      1.00     3987
cor(ER1Dis_Prev_Str_Overall,ER7Pla_Intercept)             1.00     5969
cor(ER2Rum_Intercept,ER7Pla_Intercept)                    1.00     7006
cor(ER2Rum_Prev_MF,ER7Pla_Intercept)                      1.00     3767
cor(ER2Rum_Prev_Str_Overall,ER7Pla_Intercept)             1.00     4909
cor(ER3SBl_Intercept,ER7Pla_Intercept)                    1.00     7231
cor(ER3SBl_Prev_MF,ER7Pla_Intercept)                      1.00     4180
cor(ER3SBl_Prev_Str_Overall,ER7Pla_Intercept)             1.00     4139
cor(ER4ExprS_Intercept,ER7Pla_Intercept)                  1.00     8786
cor(ER4ExprS_Prev_MF,ER7Pla_Intercept)                    1.00     4833
cor(ER4ExprS_Prev_Str_Overall,ER7Pla_Intercept)           1.00     5249
cor(ER5ExperS_Intercept,ER7Pla_Intercept)                 1.00    10205
cor(ER5ExperS_Prev_MF,ER7Pla_Intercept)                   1.00     4661
cor(ER5ExperS_Prev_Str_Overall,ER7Pla_Intercept)          1.00     6719
cor(ER6Acc_Intercept,ER7Pla_Intercept)                    1.00    11586
cor(ER6Acc_Prev_MF,ER7Pla_Intercept)                      1.00     9916
cor(ER6Acc_Prev_Str_Overall,ER7Pla_Intercept)             1.00     8891
cor(ER1Dis_Intercept,ER7Pla_Prev_MF)                      1.00    22321
cor(ER1Dis_Prev_MF,ER7Pla_Prev_MF)                        1.00    14290
cor(ER1Dis_Prev_Str_Overall,ER7Pla_Prev_MF)               1.00    18775
cor(ER2Rum_Intercept,ER7Pla_Prev_MF)                      1.00    13307
cor(ER2Rum_Prev_MF,ER7Pla_Prev_MF)                        1.00     9714
cor(ER2Rum_Prev_Str_Overall,ER7Pla_Prev_MF)               1.00    14259
cor(ER3SBl_Intercept,ER7Pla_Prev_MF)                      1.00    13006
cor(ER3SBl_Prev_MF,ER7Pla_Prev_MF)                        1.00     9725
cor(ER3SBl_Prev_Str_Overall,ER7Pla_Prev_MF)               1.00     9205
cor(ER4ExprS_Intercept,ER7Pla_Prev_MF)                    1.00    18543
cor(ER4ExprS_Prev_MF,ER7Pla_Prev_MF)                      1.00     8595
cor(ER4ExprS_Prev_Str_Overall,ER7Pla_Prev_MF)             1.00    11506
cor(ER5ExperS_Intercept,ER7Pla_Prev_MF)                   1.00    17845
cor(ER5ExperS_Prev_MF,ER7Pla_Prev_MF)                     1.00     8379
cor(ER5ExperS_Prev_Str_Overall,ER7Pla_Prev_MF)            1.00    11358
cor(ER6Acc_Intercept,ER7Pla_Prev_MF)                      1.00    15577
cor(ER6Acc_Prev_MF,ER7Pla_Prev_MF)                        1.00     7846
cor(ER6Acc_Prev_Str_Overall,ER7Pla_Prev_MF)               1.00     8163
cor(ER7Pla_Intercept,ER7Pla_Prev_MF)                      1.00    10265
cor(ER1Dis_Intercept,ER7Pla_Prev_Str_Overall)             1.00    21645
cor(ER1Dis_Prev_MF,ER7Pla_Prev_Str_Overall)               1.00    18822
cor(ER1Dis_Prev_Str_Overall,ER7Pla_Prev_Str_Overall)      1.00    19912
cor(ER2Rum_Intercept,ER7Pla_Prev_Str_Overall)             1.00    20259
cor(ER2Rum_Prev_MF,ER7Pla_Prev_Str_Overall)               1.00    14953
cor(ER2Rum_Prev_Str_Overall,ER7Pla_Prev_Str_Overall)      1.00    16140
cor(ER3SBl_Intercept,ER7Pla_Prev_Str_Overall)             1.00    20308
cor(ER3SBl_Prev_MF,ER7Pla_Prev_Str_Overall)               1.00    12210
cor(ER3SBl_Prev_Str_Overall,ER7Pla_Prev_Str_Overall)      1.00    12919
cor(ER4ExprS_Intercept,ER7Pla_Prev_Str_Overall)           1.00    17530
cor(ER4ExprS_Prev_MF,ER7Pla_Prev_Str_Overall)             1.00    10370
cor(ER4ExprS_Prev_Str_Overall,ER7Pla_Prev_Str_Overall)    1.00    12242
cor(ER5ExperS_Intercept,ER7Pla_Prev_Str_Overall)          1.00    19355
cor(ER5ExperS_Prev_MF,ER7Pla_Prev_Str_Overall)            1.00     9291
cor(ER5ExperS_Prev_Str_Overall,ER7Pla_Prev_Str_Overall)   1.00    13390
cor(ER6Acc_Intercept,ER7Pla_Prev_Str_Overall)             1.00    21131
cor(ER6Acc_Prev_MF,ER7Pla_Prev_Str_Overall)               1.00     8890
cor(ER6Acc_Prev_Str_Overall,ER7Pla_Prev_Str_Overall)      1.00     8796
cor(ER7Pla_Intercept,ER7Pla_Prev_Str_Overall)             1.00    17479
cor(ER7Pla_Prev_MF,ER7Pla_Prev_Str_Overall)               1.00     9699
cor(ER1Dis_Intercept,ER8Rea_Intercept)                    1.00    11545
cor(ER1Dis_Prev_MF,ER8Rea_Intercept)                      1.00     4774
cor(ER1Dis_Prev_Str_Overall,ER8Rea_Intercept)             1.00     8077
cor(ER2Rum_Intercept,ER8Rea_Intercept)                    1.00     8191
cor(ER2Rum_Prev_MF,ER8Rea_Intercept)                      1.00     4351
cor(ER2Rum_Prev_Str_Overall,ER8Rea_Intercept)             1.00     5930
cor(ER3SBl_Intercept,ER8Rea_Intercept)                    1.00     9707
cor(ER3SBl_Prev_MF,ER8Rea_Intercept)                      1.00     5245
cor(ER3SBl_Prev_Str_Overall,ER8Rea_Intercept)             1.00     5422
cor(ER4ExprS_Intercept,ER8Rea_Intercept)                  1.00    10581
cor(ER4ExprS_Prev_MF,ER8Rea_Intercept)                    1.00     5487
cor(ER4ExprS_Prev_Str_Overall,ER8Rea_Intercept)           1.00     6791
cor(ER5ExperS_Intercept,ER8Rea_Intercept)                 1.00    13557
cor(ER5ExperS_Prev_MF,ER8Rea_Intercept)                   1.00     6520
cor(ER5ExperS_Prev_Str_Overall,ER8Rea_Intercept)          1.00     8514
cor(ER6Acc_Intercept,ER8Rea_Intercept)                    1.00     9894
cor(ER6Acc_Prev_MF,ER8Rea_Intercept)                      1.00     7272
cor(ER6Acc_Prev_Str_Overall,ER8Rea_Intercept)             1.00     8891
cor(ER7Pla_Intercept,ER8Rea_Intercept)                    1.00     9977
cor(ER7Pla_Prev_MF,ER8Rea_Intercept)                      1.00     7901
cor(ER7Pla_Prev_Str_Overall,ER8Rea_Intercept)             1.00     9559
cor(ER1Dis_Intercept,ER8Rea_Prev_MF)                      1.00    19129
cor(ER1Dis_Prev_MF,ER8Rea_Prev_MF)                        1.00    12035
cor(ER1Dis_Prev_Str_Overall,ER8Rea_Prev_MF)               1.00    16024
cor(ER2Rum_Intercept,ER8Rea_Prev_MF)                      1.00    13062
cor(ER2Rum_Prev_MF,ER8Rea_Prev_MF)                        1.00     9256
cor(ER2Rum_Prev_Str_Overall,ER8Rea_Prev_MF)               1.00    11061
cor(ER3SBl_Intercept,ER8Rea_Prev_MF)                      1.00    12976
cor(ER3SBl_Prev_MF,ER8Rea_Prev_MF)                        1.00     8369
cor(ER3SBl_Prev_Str_Overall,ER8Rea_Prev_MF)               1.00     7981
cor(ER4ExprS_Intercept,ER8Rea_Prev_MF)                    1.00    13227
cor(ER4ExprS_Prev_MF,ER8Rea_Prev_MF)                      1.00     8796
cor(ER4ExprS_Prev_Str_Overall,ER8Rea_Prev_MF)             1.00    10013
cor(ER5ExperS_Intercept,ER8Rea_Prev_MF)                   1.00    17338
cor(ER5ExperS_Prev_MF,ER8Rea_Prev_MF)                     1.00     8293
cor(ER5ExperS_Prev_Str_Overall,ER8Rea_Prev_MF)            1.00    11758
cor(ER6Acc_Intercept,ER8Rea_Prev_MF)                      1.00     9186
cor(ER6Acc_Prev_MF,ER8Rea_Prev_MF)                        1.00     7607
cor(ER6Acc_Prev_Str_Overall,ER8Rea_Prev_MF)               1.00     8538
cor(ER7Pla_Intercept,ER8Rea_Prev_MF)                      1.00    10693
cor(ER7Pla_Prev_MF,ER8Rea_Prev_MF)                        1.00     7369
cor(ER7Pla_Prev_Str_Overall,ER8Rea_Prev_MF)               1.00     8068
cor(ER8Rea_Intercept,ER8Rea_Prev_MF)                      1.00    12013
cor(ER1Dis_Intercept,ER8Rea_Prev_Str_Overall)             1.00    28649
cor(ER1Dis_Prev_MF,ER8Rea_Prev_Str_Overall)               1.00    23659
cor(ER1Dis_Prev_Str_Overall,ER8Rea_Prev_Str_Overall)      1.00    23017
cor(ER2Rum_Intercept,ER8Rea_Prev_Str_Overall)             1.00    23643
cor(ER2Rum_Prev_MF,ER8Rea_Prev_Str_Overall)               1.00    14886
cor(ER2Rum_Prev_Str_Overall,ER8Rea_Prev_Str_Overall)      1.00    16511
cor(ER3SBl_Intercept,ER8Rea_Prev_Str_Overall)             1.00    22587
cor(ER3SBl_Prev_MF,ER8Rea_Prev_Str_Overall)               1.00    14816
cor(ER3SBl_Prev_Str_Overall,ER8Rea_Prev_Str_Overall)      1.00    12578
cor(ER4ExprS_Intercept,ER8Rea_Prev_Str_Overall)           1.00    22393
cor(ER4ExprS_Prev_MF,ER8Rea_Prev_Str_Overall)             1.00    10781
cor(ER4ExprS_Prev_Str_Overall,ER8Rea_Prev_Str_Overall)    1.00    12054
cor(ER5ExperS_Intercept,ER8Rea_Prev_Str_Overall)          1.00    21697
cor(ER5ExperS_Prev_MF,ER8Rea_Prev_Str_Overall)            1.00     9632
cor(ER5ExperS_Prev_Str_Overall,ER8Rea_Prev_Str_Overall)   1.00    13962
cor(ER6Acc_Intercept,ER8Rea_Prev_Str_Overall)             1.00    20175
cor(ER6Acc_Prev_MF,ER8Rea_Prev_Str_Overall)               1.00     9229
cor(ER6Acc_Prev_Str_Overall,ER8Rea_Prev_Str_Overall)      1.00     8198
cor(ER7Pla_Intercept,ER8Rea_Prev_Str_Overall)             1.00    17291
cor(ER7Pla_Prev_MF,ER8Rea_Prev_Str_Overall)               1.00     9324
cor(ER7Pla_Prev_Str_Overall,ER8Rea_Prev_Str_Overall)      1.00     7639
cor(ER8Rea_Intercept,ER8Rea_Prev_Str_Overall)             1.00    12946
cor(ER8Rea_Prev_MF,ER8Rea_Prev_Str_Overall)               1.00     8230
cor(ER1Dis_Intercept,ER9ESu_Intercept)                    1.00     8705
cor(ER1Dis_Prev_MF,ER9ESu_Intercept)                      1.00     4343
cor(ER1Dis_Prev_Str_Overall,ER9ESu_Intercept)             1.00     7151
cor(ER2Rum_Intercept,ER9ESu_Intercept)                    1.00     7706
cor(ER2Rum_Prev_MF,ER9ESu_Intercept)                      1.00     4653
cor(ER2Rum_Prev_Str_Overall,ER9ESu_Intercept)             1.00     7117
cor(ER3SBl_Intercept,ER9ESu_Intercept)                    1.00     7974
cor(ER3SBl_Prev_MF,ER9ESu_Intercept)                      1.00     4650
cor(ER3SBl_Prev_Str_Overall,ER9ESu_Intercept)             1.00     5450
cor(ER4ExprS_Intercept,ER9ESu_Intercept)                  1.00    10394
cor(ER4ExprS_Prev_MF,ER9ESu_Intercept)                    1.00     6357
cor(ER4ExprS_Prev_Str_Overall,ER9ESu_Intercept)           1.00     7125
cor(ER5ExperS_Intercept,ER9ESu_Intercept)                 1.00    12682
cor(ER5ExperS_Prev_MF,ER9ESu_Intercept)                   1.00     6750
cor(ER5ExperS_Prev_Str_Overall,ER9ESu_Intercept)          1.00     9007
cor(ER6Acc_Intercept,ER9ESu_Intercept)                    1.00    12184
cor(ER6Acc_Prev_MF,ER9ESu_Intercept)                      1.00     9157
cor(ER6Acc_Prev_Str_Overall,ER9ESu_Intercept)             1.00     9617
cor(ER7Pla_Intercept,ER9ESu_Intercept)                    1.00    10003
cor(ER7Pla_Prev_MF,ER9ESu_Intercept)                      1.00     8260
cor(ER7Pla_Prev_Str_Overall,ER9ESu_Intercept)             1.00    10457
cor(ER8Rea_Intercept,ER9ESu_Intercept)                    1.00     9996
cor(ER8Rea_Prev_MF,ER9ESu_Intercept)                      1.00     7764
cor(ER8Rea_Prev_Str_Overall,ER9ESu_Intercept)             1.00    10808
cor(ER1Dis_Intercept,ER9ESu_Prev_MF)                      1.00    18443
cor(ER1Dis_Prev_MF,ER9ESu_Prev_MF)                        1.00    12297
cor(ER1Dis_Prev_Str_Overall,ER9ESu_Prev_MF)               1.00    12586
cor(ER2Rum_Intercept,ER9ESu_Prev_MF)                      1.00    15441
cor(ER2Rum_Prev_MF,ER9ESu_Prev_MF)                        1.00     9969
cor(ER2Rum_Prev_Str_Overall,ER9ESu_Prev_MF)               1.00    14118
cor(ER3SBl_Intercept,ER9ESu_Prev_MF)                      1.00    15672
cor(ER3SBl_Prev_MF,ER9ESu_Prev_MF)                        1.00    10184
cor(ER3SBl_Prev_Str_Overall,ER9ESu_Prev_MF)               1.00    10872
cor(ER4ExprS_Intercept,ER9ESu_Prev_MF)                    1.00    17104
cor(ER4ExprS_Prev_MF,ER9ESu_Prev_MF)                      1.00    10367
cor(ER4ExprS_Prev_Str_Overall,ER9ESu_Prev_MF)             1.00    10704
cor(ER5ExperS_Intercept,ER9ESu_Prev_MF)                   1.00    16821
cor(ER5ExperS_Prev_MF,ER9ESu_Prev_MF)                     1.00     9650
cor(ER5ExperS_Prev_Str_Overall,ER9ESu_Prev_MF)            1.00    12379
cor(ER6Acc_Intercept,ER9ESu_Prev_MF)                      1.00    16838
cor(ER6Acc_Prev_MF,ER9ESu_Prev_MF)                        1.00     9043
cor(ER6Acc_Prev_Str_Overall,ER9ESu_Prev_MF)               1.00     8850
cor(ER7Pla_Intercept,ER9ESu_Prev_MF)                      1.00    13204
cor(ER7Pla_Prev_MF,ER9ESu_Prev_MF)                        1.00     7615
cor(ER7Pla_Prev_Str_Overall,ER9ESu_Prev_MF)               1.00     8509
cor(ER8Rea_Intercept,ER9ESu_Prev_MF)                      1.00    10678
cor(ER8Rea_Prev_MF,ER9ESu_Prev_MF)                        1.00     7957
cor(ER8Rea_Prev_Str_Overall,ER9ESu_Prev_MF)               1.00     7971
cor(ER9ESu_Intercept,ER9ESu_Prev_MF)                      1.00     8886
cor(ER1Dis_Intercept,ER9ESu_Prev_Str_Overall)             1.00    16502
cor(ER1Dis_Prev_MF,ER9ESu_Prev_Str_Overall)               1.00    13503
cor(ER1Dis_Prev_Str_Overall,ER9ESu_Prev_Str_Overall)      1.00    16235
cor(ER2Rum_Intercept,ER9ESu_Prev_Str_Overall)             1.00    18416
cor(ER2Rum_Prev_MF,ER9ESu_Prev_Str_Overall)               1.00    13757
cor(ER2Rum_Prev_Str_Overall,ER9ESu_Prev_Str_Overall)      1.00    13932
cor(ER3SBl_Intercept,ER9ESu_Prev_Str_Overall)             1.00    18556
cor(ER3SBl_Prev_MF,ER9ESu_Prev_Str_Overall)               1.00    12506
cor(ER3SBl_Prev_Str_Overall,ER9ESu_Prev_Str_Overall)      1.00    10622
cor(ER4ExprS_Intercept,ER9ESu_Prev_Str_Overall)           1.00    16950
cor(ER4ExprS_Prev_MF,ER9ESu_Prev_Str_Overall)             1.00    10009
cor(ER4ExprS_Prev_Str_Overall,ER9ESu_Prev_Str_Overall)    1.00    12113
cor(ER5ExperS_Intercept,ER9ESu_Prev_Str_Overall)          1.00    15768
cor(ER5ExperS_Prev_MF,ER9ESu_Prev_Str_Overall)            1.00     9585
cor(ER5ExperS_Prev_Str_Overall,ER9ESu_Prev_Str_Overall)   1.00    12150
cor(ER6Acc_Intercept,ER9ESu_Prev_Str_Overall)             1.00    18203
cor(ER6Acc_Prev_MF,ER9ESu_Prev_Str_Overall)               1.00     9188
cor(ER6Acc_Prev_Str_Overall,ER9ESu_Prev_Str_Overall)      1.00     7478
cor(ER7Pla_Intercept,ER9ESu_Prev_Str_Overall)             1.00    16003
cor(ER7Pla_Prev_MF,ER9ESu_Prev_Str_Overall)               1.00     9987
cor(ER7Pla_Prev_Str_Overall,ER9ESu_Prev_Str_Overall)      1.00     8402
cor(ER8Rea_Intercept,ER9ESu_Prev_Str_Overall)             1.00    11202
cor(ER8Rea_Prev_MF,ER9ESu_Prev_Str_Overall)               1.00     8415
cor(ER8Rea_Prev_Str_Overall,ER9ESu_Prev_Str_Overall)      1.00     7744
cor(ER9ESu_Intercept,ER9ESu_Prev_Str_Overall)             1.00    10841
cor(ER9ESu_Prev_MF,ER9ESu_Prev_Str_Overall)               1.00     8819
cor(ER1Dis_Intercept,ER10Rel_Intercept)                   1.00     8058
cor(ER1Dis_Prev_MF,ER10Rel_Intercept)                     1.00     4395
cor(ER1Dis_Prev_Str_Overall,ER10Rel_Intercept)            1.00     6749
cor(ER2Rum_Intercept,ER10Rel_Intercept)                   1.00     7150
cor(ER2Rum_Prev_MF,ER10Rel_Intercept)                     1.00     3833
cor(ER2Rum_Prev_Str_Overall,ER10Rel_Intercept)            1.00     4651
cor(ER3SBl_Intercept,ER10Rel_Intercept)                   1.00     7229
cor(ER3SBl_Prev_MF,ER10Rel_Intercept)                     1.00     4425
cor(ER3SBl_Prev_Str_Overall,ER10Rel_Intercept)            1.00     4476
cor(ER4ExprS_Intercept,ER10Rel_Intercept)                 1.00     8482
cor(ER4ExprS_Prev_MF,ER10Rel_Intercept)                   1.00     4391
cor(ER4ExprS_Prev_Str_Overall,ER10Rel_Intercept)          1.00     5424
cor(ER5ExperS_Intercept,ER10Rel_Intercept)                1.00     9040
cor(ER5ExperS_Prev_MF,ER10Rel_Intercept)                  1.00     5057
cor(ER5ExperS_Prev_Str_Overall,ER10Rel_Intercept)         1.00     6776
cor(ER6Acc_Intercept,ER10Rel_Intercept)                   1.00     8871
cor(ER6Acc_Prev_MF,ER10Rel_Intercept)                     1.00     5173
cor(ER6Acc_Prev_Str_Overall,ER10Rel_Intercept)            1.00     4971
cor(ER7Pla_Intercept,ER10Rel_Intercept)                   1.00     8055
cor(ER7Pla_Prev_MF,ER10Rel_Intercept)                     1.00     5661
cor(ER7Pla_Prev_Str_Overall,ER10Rel_Intercept)            1.00     6137
cor(ER8Rea_Intercept,ER10Rel_Intercept)                   1.00    10453
cor(ER8Rea_Prev_MF,ER10Rel_Intercept)                     1.00     8014
cor(ER8Rea_Prev_Str_Overall,ER10Rel_Intercept)            1.00     8028
cor(ER9ESu_Intercept,ER10Rel_Intercept)                   1.00    10720
cor(ER9ESu_Prev_MF,ER10Rel_Intercept)                     1.00     9847
cor(ER9ESu_Prev_Str_Overall,ER10Rel_Intercept)            1.00     9423
cor(ER1Dis_Intercept,ER10Rel_Prev_MF)                     1.00    17165
cor(ER1Dis_Prev_MF,ER10Rel_Prev_MF)                       1.00    15143
cor(ER1Dis_Prev_Str_Overall,ER10Rel_Prev_MF)              1.00    14165
cor(ER2Rum_Intercept,ER10Rel_Prev_MF)                     1.00    17421
cor(ER2Rum_Prev_MF,ER10Rel_Prev_MF)                       1.00    12684
cor(ER2Rum_Prev_Str_Overall,ER10Rel_Prev_MF)              1.00    12485
cor(ER3SBl_Intercept,ER10Rel_Prev_MF)                     1.00    18099
cor(ER3SBl_Prev_MF,ER10Rel_Prev_MF)                       1.00    12612
cor(ER3SBl_Prev_Str_Overall,ER10Rel_Prev_MF)              1.00     9762
cor(ER4ExprS_Intercept,ER10Rel_Prev_MF)                   1.00    14121
cor(ER4ExprS_Prev_MF,ER10Rel_Prev_MF)                     1.00    10495
cor(ER4ExprS_Prev_Str_Overall,ER10Rel_Prev_MF)            1.00     9978
cor(ER5ExperS_Intercept,ER10Rel_Prev_MF)                  1.00    18617
cor(ER5ExperS_Prev_MF,ER10Rel_Prev_MF)                    1.00     9128
cor(ER5ExperS_Prev_Str_Overall,ER10Rel_Prev_MF)           1.00    12640
cor(ER6Acc_Intercept,ER10Rel_Prev_MF)                     1.00    14560
cor(ER6Acc_Prev_MF,ER10Rel_Prev_MF)                       1.00     9659
cor(ER6Acc_Prev_Str_Overall,ER10Rel_Prev_MF)              1.00     8688
cor(ER7Pla_Intercept,ER10Rel_Prev_MF)                     1.00    15613
cor(ER7Pla_Prev_MF,ER10Rel_Prev_MF)                       1.00     9518
cor(ER7Pla_Prev_Str_Overall,ER10Rel_Prev_MF)              1.00     8402
cor(ER8Rea_Intercept,ER10Rel_Prev_MF)                     1.00    12816
cor(ER8Rea_Prev_MF,ER10Rel_Prev_MF)                       1.00     8821
cor(ER8Rea_Prev_Str_Overall,ER10Rel_Prev_MF)              1.00     7799
cor(ER9ESu_Intercept,ER10Rel_Prev_MF)                     1.00    10013
cor(ER9ESu_Prev_MF,ER10Rel_Prev_MF)                       1.00     8261
cor(ER9ESu_Prev_Str_Overall,ER10Rel_Prev_MF)              1.00     8593
cor(ER10Rel_Intercept,ER10Rel_Prev_MF)                    1.00     8069
cor(ER1Dis_Intercept,ER10Rel_Prev_Str_Overall)            1.00    16821
cor(ER1Dis_Prev_MF,ER10Rel_Prev_Str_Overall)              1.00    11118
cor(ER1Dis_Prev_Str_Overall,ER10Rel_Prev_Str_Overall)     1.00    12608
cor(ER2Rum_Intercept,ER10Rel_Prev_Str_Overall)            1.00    15721
cor(ER2Rum_Prev_MF,ER10Rel_Prev_Str_Overall)              1.00    12556
cor(ER2Rum_Prev_Str_Overall,ER10Rel_Prev_Str_Overall)     1.00    14476
cor(ER3SBl_Intercept,ER10Rel_Prev_Str_Overall)            1.00    15458
cor(ER3SBl_Prev_MF,ER10Rel_Prev_Str_Overall)              1.00    11373
cor(ER3SBl_Prev_Str_Overall,ER10Rel_Prev_Str_Overall)     1.00    11629
cor(ER4ExprS_Intercept,ER10Rel_Prev_Str_Overall)          1.00    14995
cor(ER4ExprS_Prev_MF,ER10Rel_Prev_Str_Overall)            1.00    10702
cor(ER4ExprS_Prev_Str_Overall,ER10Rel_Prev_Str_Overall)   1.00    11820
cor(ER5ExperS_Intercept,ER10Rel_Prev_Str_Overall)         1.00    17001
cor(ER5ExperS_Prev_MF,ER10Rel_Prev_Str_Overall)           1.00    10923
cor(ER5ExperS_Prev_Str_Overall,ER10Rel_Prev_Str_Overall)  1.00    12090
cor(ER6Acc_Intercept,ER10Rel_Prev_Str_Overall)            1.00    15514
cor(ER6Acc_Prev_MF,ER10Rel_Prev_Str_Overall)              1.00    10169
cor(ER6Acc_Prev_Str_Overall,ER10Rel_Prev_Str_Overall)     1.00     9552
cor(ER7Pla_Intercept,ER10Rel_Prev_Str_Overall)            1.00    14435
cor(ER7Pla_Prev_MF,ER10Rel_Prev_Str_Overall)              1.00     9978
cor(ER7Pla_Prev_Str_Overall,ER10Rel_Prev_Str_Overall)     1.00     9674
cor(ER8Rea_Intercept,ER10Rel_Prev_Str_Overall)            1.00    11820
cor(ER8Rea_Prev_MF,ER10Rel_Prev_Str_Overall)              1.00     9592
cor(ER8Rea_Prev_Str_Overall,ER10Rel_Prev_Str_Overall)     1.00     9117
cor(ER9ESu_Intercept,ER10Rel_Prev_Str_Overall)            1.00    10752
cor(ER9ESu_Prev_MF,ER10Rel_Prev_Str_Overall)              1.00     8487
cor(ER9ESu_Prev_Str_Overall,ER10Rel_Prev_Str_Overall)     1.00     8827
cor(ER10Rel_Intercept,ER10Rel_Prev_Str_Overall)           1.00    12320
cor(ER10Rel_Prev_MF,ER10Rel_Prev_Str_Overall)             1.00     8521
                                                          Tail_ESS
sd(ER1Dis_Intercept)                                         10077
sd(ER1Dis_Prev_MF)                                            1722
sd(ER1Dis_Prev_Str_Overall)                                   5624
sd(ER2Rum_Intercept)                                          7195
sd(ER2Rum_Prev_MF)                                            3947
sd(ER2Rum_Prev_Str_Overall)                                   2176
sd(ER3SBl_Intercept)                                          5906
sd(ER3SBl_Prev_MF)                                            4438
sd(ER3SBl_Prev_Str_Overall)                                   7395
sd(ER4ExprS_Intercept)                                        9144
sd(ER4ExprS_Prev_MF)                                          6122
sd(ER4ExprS_Prev_Str_Overall)                                 4008
sd(ER5ExperS_Intercept)                                       9869
sd(ER5ExperS_Prev_MF)                                         7775
sd(ER5ExperS_Prev_Str_Overall)                                5601
sd(ER6Acc_Intercept)                                          9458
sd(ER6Acc_Prev_MF)                                            6464
sd(ER6Acc_Prev_Str_Overall)                                   6182
sd(ER7Pla_Intercept)                                         10041
sd(ER7Pla_Prev_MF)                                            3167
sd(ER7Pla_Prev_Str_Overall)                                   6678
sd(ER8Rea_Intercept)                                          7356
sd(ER8Rea_Prev_MF)                                            3388
sd(ER8Rea_Prev_Str_Overall)                                   7630
sd(ER9ESu_Intercept)                                          8531
sd(ER9ESu_Prev_MF)                                            4238
sd(ER9ESu_Prev_Str_Overall)                                   5197
sd(ER10Rel_Intercept)                                        10260
sd(ER10Rel_Prev_MF)                                           5860
sd(ER10Rel_Prev_Str_Overall)                                  3633
cor(ER1Dis_Intercept,ER1Dis_Prev_MF)                          8553
cor(ER1Dis_Intercept,ER1Dis_Prev_Str_Overall)                 9842
cor(ER1Dis_Prev_MF,ER1Dis_Prev_Str_Overall)                   7486
cor(ER1Dis_Intercept,ER2Rum_Intercept)                        8721
cor(ER1Dis_Prev_MF,ER2Rum_Intercept)                          4200
cor(ER1Dis_Prev_Str_Overall,ER2Rum_Intercept)                 8721
cor(ER1Dis_Intercept,ER2Rum_Prev_MF)                          8184
cor(ER1Dis_Prev_MF,ER2Rum_Prev_MF)                            9519
cor(ER1Dis_Prev_Str_Overall,ER2Rum_Prev_MF)                   9349
cor(ER2Rum_Intercept,ER2Rum_Prev_MF)                          9398
cor(ER1Dis_Intercept,ER2Rum_Prev_Str_Overall)                 9742
cor(ER1Dis_Prev_MF,ER2Rum_Prev_Str_Overall)                   9187
cor(ER1Dis_Prev_Str_Overall,ER2Rum_Prev_Str_Overall)          9967
cor(ER2Rum_Intercept,ER2Rum_Prev_Str_Overall)                10356
cor(ER2Rum_Prev_MF,ER2Rum_Prev_Str_Overall)                  10063
cor(ER1Dis_Intercept,ER3SBl_Intercept)                        9635
cor(ER1Dis_Prev_MF,ER3SBl_Intercept)                          5359
cor(ER1Dis_Prev_Str_Overall,ER3SBl_Intercept)                 9211
cor(ER2Rum_Intercept,ER3SBl_Intercept)                        9365
cor(ER2Rum_Prev_MF,ER3SBl_Intercept)                          8744
cor(ER2Rum_Prev_Str_Overall,ER3SBl_Intercept)                 8752
cor(ER1Dis_Intercept,ER3SBl_Prev_MF)                          8823
cor(ER1Dis_Prev_MF,ER3SBl_Prev_MF)                            9050
cor(ER1Dis_Prev_Str_Overall,ER3SBl_Prev_MF)                   9603
cor(ER2Rum_Intercept,ER3SBl_Prev_MF)                          8398
cor(ER2Rum_Prev_MF,ER3SBl_Prev_MF)                            9048
cor(ER2Rum_Prev_Str_Overall,ER3SBl_Prev_MF)                   9034
cor(ER3SBl_Intercept,ER3SBl_Prev_MF)                         10514
cor(ER1Dis_Intercept,ER3SBl_Prev_Str_Overall)                 8748
cor(ER1Dis_Prev_MF,ER3SBl_Prev_Str_Overall)                   9268
cor(ER1Dis_Prev_Str_Overall,ER3SBl_Prev_Str_Overall)          9531
cor(ER2Rum_Intercept,ER3SBl_Prev_Str_Overall)                 9852
cor(ER2Rum_Prev_MF,ER3SBl_Prev_Str_Overall)                   9068
cor(ER2Rum_Prev_Str_Overall,ER3SBl_Prev_Str_Overall)          9277
cor(ER3SBl_Intercept,ER3SBl_Prev_Str_Overall)                 9535
cor(ER3SBl_Prev_MF,ER3SBl_Prev_Str_Overall)                   9570
cor(ER1Dis_Intercept,ER4ExprS_Intercept)                      8633
cor(ER1Dis_Prev_MF,ER4ExprS_Intercept)                        5232
cor(ER1Dis_Prev_Str_Overall,ER4ExprS_Intercept)               8368
cor(ER2Rum_Intercept,ER4ExprS_Intercept)                      8141
cor(ER2Rum_Prev_MF,ER4ExprS_Intercept)                        5296
cor(ER2Rum_Prev_Str_Overall,ER4ExprS_Intercept)               7804
cor(ER3SBl_Intercept,ER4ExprS_Intercept)                      8572
cor(ER3SBl_Prev_MF,ER4ExprS_Intercept)                        6698
cor(ER3SBl_Prev_Str_Overall,ER4ExprS_Intercept)               5878
cor(ER1Dis_Intercept,ER4ExprS_Prev_MF)                        8540
cor(ER1Dis_Prev_MF,ER4ExprS_Prev_MF)                          9156
cor(ER1Dis_Prev_Str_Overall,ER4ExprS_Prev_MF)                 8543
cor(ER2Rum_Intercept,ER4ExprS_Prev_MF)                        9473
cor(ER2Rum_Prev_MF,ER4ExprS_Prev_MF)                          9745
cor(ER2Rum_Prev_Str_Overall,ER4ExprS_Prev_MF)                 9748
cor(ER3SBl_Intercept,ER4ExprS_Prev_MF)                        9081
cor(ER3SBl_Prev_MF,ER4ExprS_Prev_MF)                          9124
cor(ER3SBl_Prev_Str_Overall,ER4ExprS_Prev_MF)                10005
cor(ER4ExprS_Intercept,ER4ExprS_Prev_MF)                      9560
cor(ER1Dis_Intercept,ER4ExprS_Prev_Str_Overall)               8941
cor(ER1Dis_Prev_MF,ER4ExprS_Prev_Str_Overall)                10291
cor(ER1Dis_Prev_Str_Overall,ER4ExprS_Prev_Str_Overall)        8991
cor(ER2Rum_Intercept,ER4ExprS_Prev_Str_Overall)              10098
cor(ER2Rum_Prev_MF,ER4ExprS_Prev_Str_Overall)                 9753
cor(ER2Rum_Prev_Str_Overall,ER4ExprS_Prev_Str_Overall)        9391
cor(ER3SBl_Intercept,ER4ExprS_Prev_Str_Overall)              10623
cor(ER3SBl_Prev_MF,ER4ExprS_Prev_Str_Overall)                 9773
cor(ER3SBl_Prev_Str_Overall,ER4ExprS_Prev_Str_Overall)       10571
cor(ER4ExprS_Intercept,ER4ExprS_Prev_Str_Overall)             9679
cor(ER4ExprS_Prev_MF,ER4ExprS_Prev_Str_Overall)              10750
cor(ER1Dis_Intercept,ER5ExperS_Intercept)                     8832
cor(ER1Dis_Prev_MF,ER5ExperS_Intercept)                       4710
cor(ER1Dis_Prev_Str_Overall,ER5ExperS_Intercept)              7784
cor(ER2Rum_Intercept,ER5ExperS_Intercept)                     7283
cor(ER2Rum_Prev_MF,ER5ExperS_Intercept)                       5625
cor(ER2Rum_Prev_Str_Overall,ER5ExperS_Intercept)              8041
cor(ER3SBl_Intercept,ER5ExperS_Intercept)                     8227
cor(ER3SBl_Prev_MF,ER5ExperS_Intercept)                       6191
cor(ER3SBl_Prev_Str_Overall,ER5ExperS_Intercept)              7238
cor(ER4ExprS_Intercept,ER5ExperS_Intercept)                   9473
cor(ER4ExprS_Prev_MF,ER5ExperS_Intercept)                     8911
cor(ER4ExprS_Prev_Str_Overall,ER5ExperS_Intercept)            8931
cor(ER1Dis_Intercept,ER5ExperS_Prev_MF)                       8559
cor(ER1Dis_Prev_MF,ER5ExperS_Prev_MF)                         8718
cor(ER1Dis_Prev_Str_Overall,ER5ExperS_Prev_MF)                8341
cor(ER2Rum_Intercept,ER5ExperS_Prev_MF)                       8254
cor(ER2Rum_Prev_MF,ER5ExperS_Prev_MF)                         8443
cor(ER2Rum_Prev_Str_Overall,ER5ExperS_Prev_MF)                9182
cor(ER3SBl_Intercept,ER5ExperS_Prev_MF)                       8887
cor(ER3SBl_Prev_MF,ER5ExperS_Prev_MF)                         9749
cor(ER3SBl_Prev_Str_Overall,ER5ExperS_Prev_MF)                8698
cor(ER4ExprS_Intercept,ER5ExperS_Prev_MF)                     9912
cor(ER4ExprS_Prev_MF,ER5ExperS_Prev_MF)                       8177
cor(ER4ExprS_Prev_Str_Overall,ER5ExperS_Prev_MF)             10100
cor(ER5ExperS_Intercept,ER5ExperS_Prev_MF)                    9303
cor(ER1Dis_Intercept,ER5ExperS_Prev_Str_Overall)              9843
cor(ER1Dis_Prev_MF,ER5ExperS_Prev_Str_Overall)                8785
cor(ER1Dis_Prev_Str_Overall,ER5ExperS_Prev_Str_Overall)      10572
cor(ER2Rum_Intercept,ER5ExperS_Prev_Str_Overall)             10185
cor(ER2Rum_Prev_MF,ER5ExperS_Prev_Str_Overall)                9988
cor(ER2Rum_Prev_Str_Overall,ER5ExperS_Prev_Str_Overall)      10192
cor(ER3SBl_Intercept,ER5ExperS_Prev_Str_Overall)             10453
cor(ER3SBl_Prev_MF,ER5ExperS_Prev_Str_Overall)               10191
cor(ER3SBl_Prev_Str_Overall,ER5ExperS_Prev_Str_Overall)      11159
cor(ER4ExprS_Intercept,ER5ExperS_Prev_Str_Overall)           10184
cor(ER4ExprS_Prev_MF,ER5ExperS_Prev_Str_Overall)              9894
cor(ER4ExprS_Prev_Str_Overall,ER5ExperS_Prev_Str_Overall)     9241
cor(ER5ExperS_Intercept,ER5ExperS_Prev_Str_Overall)          10698
cor(ER5ExperS_Prev_MF,ER5ExperS_Prev_Str_Overall)            10290
cor(ER1Dis_Intercept,ER6Acc_Intercept)                        8382
cor(ER1Dis_Prev_MF,ER6Acc_Intercept)                          4680
cor(ER1Dis_Prev_Str_Overall,ER6Acc_Intercept)                 6174
cor(ER2Rum_Intercept,ER6Acc_Intercept)                        7419
cor(ER2Rum_Prev_MF,ER6Acc_Intercept)                          4410
cor(ER2Rum_Prev_Str_Overall,ER6Acc_Intercept)                 6286
cor(ER3SBl_Intercept,ER6Acc_Intercept)                        7521
cor(ER3SBl_Prev_MF,ER6Acc_Intercept)                          5450
cor(ER3SBl_Prev_Str_Overall,ER6Acc_Intercept)                 5136
cor(ER4ExprS_Intercept,ER6Acc_Intercept)                      9555
cor(ER4ExprS_Prev_MF,ER6Acc_Intercept)                        7636
cor(ER4ExprS_Prev_Str_Overall,ER6Acc_Intercept)               8088
cor(ER5ExperS_Intercept,ER6Acc_Intercept)                     9886
cor(ER5ExperS_Prev_MF,ER6Acc_Intercept)                       7604
cor(ER5ExperS_Prev_Str_Overall,ER6Acc_Intercept)              9663
cor(ER1Dis_Intercept,ER6Acc_Prev_MF)                          7709
cor(ER1Dis_Prev_MF,ER6Acc_Prev_MF)                            9323
cor(ER1Dis_Prev_Str_Overall,ER6Acc_Prev_MF)                   9393
cor(ER2Rum_Intercept,ER6Acc_Prev_MF)                          9291
cor(ER2Rum_Prev_MF,ER6Acc_Prev_MF)                            9001
cor(ER2Rum_Prev_Str_Overall,ER6Acc_Prev_MF)                   9397
cor(ER3SBl_Intercept,ER6Acc_Prev_MF)                          8816
cor(ER3SBl_Prev_MF,ER6Acc_Prev_MF)                            9286
cor(ER3SBl_Prev_Str_Overall,ER6Acc_Prev_MF)                   9798
cor(ER4ExprS_Intercept,ER6Acc_Prev_MF)                        9755
cor(ER4ExprS_Prev_MF,ER6Acc_Prev_MF)                          9710
cor(ER4ExprS_Prev_Str_Overall,ER6Acc_Prev_MF)                 9862
cor(ER5ExperS_Intercept,ER6Acc_Prev_MF)                       9685
cor(ER5ExperS_Prev_MF,ER6Acc_Prev_MF)                         8858
cor(ER5ExperS_Prev_Str_Overall,ER6Acc_Prev_MF)                9793
cor(ER6Acc_Intercept,ER6Acc_Prev_MF)                          8952
cor(ER1Dis_Intercept,ER6Acc_Prev_Str_Overall)                 8822
cor(ER1Dis_Prev_MF,ER6Acc_Prev_Str_Overall)                   9548
cor(ER1Dis_Prev_Str_Overall,ER6Acc_Prev_Str_Overall)          9619
cor(ER2Rum_Intercept,ER6Acc_Prev_Str_Overall)                 9391
cor(ER2Rum_Prev_MF,ER6Acc_Prev_Str_Overall)                   9308
cor(ER2Rum_Prev_Str_Overall,ER6Acc_Prev_Str_Overall)          9622
cor(ER3SBl_Intercept,ER6Acc_Prev_Str_Overall)                 9912
cor(ER3SBl_Prev_MF,ER6Acc_Prev_Str_Overall)                   9513
cor(ER3SBl_Prev_Str_Overall,ER6Acc_Prev_Str_Overall)          9296
cor(ER4ExprS_Intercept,ER6Acc_Prev_Str_Overall)               9969
cor(ER4ExprS_Prev_MF,ER6Acc_Prev_Str_Overall)                 9759
cor(ER4ExprS_Prev_Str_Overall,ER6Acc_Prev_Str_Overall)        9796
cor(ER5ExperS_Intercept,ER6Acc_Prev_Str_Overall)              9287
cor(ER5ExperS_Prev_MF,ER6Acc_Prev_Str_Overall)                9367
cor(ER5ExperS_Prev_Str_Overall,ER6Acc_Prev_Str_Overall)      10289
cor(ER6Acc_Intercept,ER6Acc_Prev_Str_Overall)                 8954
cor(ER6Acc_Prev_MF,ER6Acc_Prev_Str_Overall)                   9467
cor(ER1Dis_Intercept,ER7Pla_Intercept)                        9893
cor(ER1Dis_Prev_MF,ER7Pla_Intercept)                          6792
cor(ER1Dis_Prev_Str_Overall,ER7Pla_Intercept)                 8608
cor(ER2Rum_Intercept,ER7Pla_Intercept)                        9074
cor(ER2Rum_Prev_MF,ER7Pla_Intercept)                          6588
cor(ER2Rum_Prev_Str_Overall,ER7Pla_Intercept)                 7748
cor(ER3SBl_Intercept,ER7Pla_Intercept)                        8638
cor(ER3SBl_Prev_MF,ER7Pla_Intercept)                          7562
cor(ER3SBl_Prev_Str_Overall,ER7Pla_Intercept)                 7007
cor(ER4ExprS_Intercept,ER7Pla_Intercept)                     10868
cor(ER4ExprS_Prev_MF,ER7Pla_Intercept)                        8093
cor(ER4ExprS_Prev_Str_Overall,ER7Pla_Intercept)               8104
cor(ER5ExperS_Intercept,ER7Pla_Intercept)                    10469
cor(ER5ExperS_Prev_MF,ER7Pla_Intercept)                       7494
cor(ER5ExperS_Prev_Str_Overall,ER7Pla_Intercept)              9254
cor(ER6Acc_Intercept,ER7Pla_Intercept)                       10764
cor(ER6Acc_Prev_MF,ER7Pla_Intercept)                          9931
cor(ER6Acc_Prev_Str_Overall,ER7Pla_Intercept)                 8849
cor(ER1Dis_Intercept,ER7Pla_Prev_MF)                          8730
cor(ER1Dis_Prev_MF,ER7Pla_Prev_MF)                            9847
cor(ER1Dis_Prev_Str_Overall,ER7Pla_Prev_MF)                   9866
cor(ER2Rum_Intercept,ER7Pla_Prev_MF)                          8960
cor(ER2Rum_Prev_MF,ER7Pla_Prev_MF)                            8913
cor(ER2Rum_Prev_Str_Overall,ER7Pla_Prev_MF)                   9469
cor(ER3SBl_Intercept,ER7Pla_Prev_MF)                          9929
cor(ER3SBl_Prev_MF,ER7Pla_Prev_MF)                            9696
cor(ER3SBl_Prev_Str_Overall,ER7Pla_Prev_MF)                   9906
cor(ER4ExprS_Intercept,ER7Pla_Prev_MF)                       10029
cor(ER4ExprS_Prev_MF,ER7Pla_Prev_MF)                          9629
cor(ER4ExprS_Prev_Str_Overall,ER7Pla_Prev_MF)                10228
cor(ER5ExperS_Intercept,ER7Pla_Prev_MF)                      10272
cor(ER5ExperS_Prev_MF,ER7Pla_Prev_MF)                         9958
cor(ER5ExperS_Prev_Str_Overall,ER7Pla_Prev_MF)               10172
cor(ER6Acc_Intercept,ER7Pla_Prev_MF)                         10156
cor(ER6Acc_Prev_MF,ER7Pla_Prev_MF)                            9834
cor(ER6Acc_Prev_Str_Overall,ER7Pla_Prev_MF)                   9868
cor(ER7Pla_Intercept,ER7Pla_Prev_MF)                          8968
cor(ER1Dis_Intercept,ER7Pla_Prev_Str_Overall)                 9137
cor(ER1Dis_Prev_MF,ER7Pla_Prev_Str_Overall)                   9118
cor(ER1Dis_Prev_Str_Overall,ER7Pla_Prev_Str_Overall)          9920
cor(ER2Rum_Intercept,ER7Pla_Prev_Str_Overall)                 9970
cor(ER2Rum_Prev_MF,ER7Pla_Prev_Str_Overall)                   9619
cor(ER2Rum_Prev_Str_Overall,ER7Pla_Prev_Str_Overall)          9883
cor(ER3SBl_Intercept,ER7Pla_Prev_Str_Overall)                 9756
cor(ER3SBl_Prev_MF,ER7Pla_Prev_Str_Overall)                  10108
cor(ER3SBl_Prev_Str_Overall,ER7Pla_Prev_Str_Overall)          9821
cor(ER4ExprS_Intercept,ER7Pla_Prev_Str_Overall)              10329
cor(ER4ExprS_Prev_MF,ER7Pla_Prev_Str_Overall)                 9875
cor(ER4ExprS_Prev_Str_Overall,ER7Pla_Prev_Str_Overall)       10657
cor(ER5ExperS_Intercept,ER7Pla_Prev_Str_Overall)             10170
cor(ER5ExperS_Prev_MF,ER7Pla_Prev_Str_Overall)                9934
cor(ER5ExperS_Prev_Str_Overall,ER7Pla_Prev_Str_Overall)      10748
cor(ER6Acc_Intercept,ER7Pla_Prev_Str_Overall)                10411
cor(ER6Acc_Prev_MF,ER7Pla_Prev_Str_Overall)                   9337
cor(ER6Acc_Prev_Str_Overall,ER7Pla_Prev_Str_Overall)          9804
cor(ER7Pla_Intercept,ER7Pla_Prev_Str_Overall)                10695
cor(ER7Pla_Prev_MF,ER7Pla_Prev_Str_Overall)                  10002
cor(ER1Dis_Intercept,ER8Rea_Intercept)                        9125
cor(ER1Dis_Prev_MF,ER8Rea_Intercept)                          8286
cor(ER1Dis_Prev_Str_Overall,ER8Rea_Intercept)                 9239
cor(ER2Rum_Intercept,ER8Rea_Intercept)                        9212
cor(ER2Rum_Prev_MF,ER8Rea_Intercept)                          7501
cor(ER2Rum_Prev_Str_Overall,ER8Rea_Intercept)                 8432
cor(ER3SBl_Intercept,ER8Rea_Intercept)                       10230
cor(ER3SBl_Prev_MF,ER8Rea_Intercept)                          8252
cor(ER3SBl_Prev_Str_Overall,ER8Rea_Intercept)                 7636
cor(ER4ExprS_Intercept,ER8Rea_Intercept)                     10137
cor(ER4ExprS_Prev_MF,ER8Rea_Intercept)                        8048
cor(ER4ExprS_Prev_Str_Overall,ER8Rea_Intercept)               9875
cor(ER5ExperS_Intercept,ER8Rea_Intercept)                    10786
cor(ER5ExperS_Prev_MF,ER8Rea_Intercept)                       9440
cor(ER5ExperS_Prev_Str_Overall,ER8Rea_Intercept)             10216
cor(ER6Acc_Intercept,ER8Rea_Intercept)                       10381
cor(ER6Acc_Prev_MF,ER8Rea_Intercept)                          9791
cor(ER6Acc_Prev_Str_Overall,ER8Rea_Intercept)                10263
cor(ER7Pla_Intercept,ER8Rea_Intercept)                       10577
cor(ER7Pla_Prev_MF,ER8Rea_Intercept)                          8527
cor(ER7Pla_Prev_Str_Overall,ER8Rea_Intercept)                10383
cor(ER1Dis_Intercept,ER8Rea_Prev_MF)                          9248
cor(ER1Dis_Prev_MF,ER8Rea_Prev_MF)                            9845
cor(ER1Dis_Prev_Str_Overall,ER8Rea_Prev_MF)                   9813
cor(ER2Rum_Intercept,ER8Rea_Prev_MF)                          9463
cor(ER2Rum_Prev_MF,ER8Rea_Prev_MF)                            9508
cor(ER2Rum_Prev_Str_Overall,ER8Rea_Prev_MF)                   9561
cor(ER3SBl_Intercept,ER8Rea_Prev_MF)                          9007
cor(ER3SBl_Prev_MF,ER8Rea_Prev_MF)                            8829
cor(ER3SBl_Prev_Str_Overall,ER8Rea_Prev_MF)                   9370
cor(ER4ExprS_Intercept,ER8Rea_Prev_MF)                       10273
cor(ER4ExprS_Prev_MF,ER8Rea_Prev_MF)                         10020
cor(ER4ExprS_Prev_Str_Overall,ER8Rea_Prev_MF)                10326
cor(ER5ExperS_Intercept,ER8Rea_Prev_MF)                      10190
cor(ER5ExperS_Prev_MF,ER8Rea_Prev_MF)                        10088
cor(ER5ExperS_Prev_Str_Overall,ER8Rea_Prev_MF)               10377
cor(ER6Acc_Intercept,ER8Rea_Prev_MF)                          8441
cor(ER6Acc_Prev_MF,ER8Rea_Prev_MF)                            9516
cor(ER6Acc_Prev_Str_Overall,ER8Rea_Prev_MF)                  10458
cor(ER7Pla_Intercept,ER8Rea_Prev_MF)                         10197
cor(ER7Pla_Prev_MF,ER8Rea_Prev_MF)                            9715
cor(ER7Pla_Prev_Str_Overall,ER8Rea_Prev_MF)                  10018
cor(ER8Rea_Intercept,ER8Rea_Prev_MF)                         10123
cor(ER1Dis_Intercept,ER8Rea_Prev_Str_Overall)                 9012
cor(ER1Dis_Prev_MF,ER8Rea_Prev_Str_Overall)                   8783
cor(ER1Dis_Prev_Str_Overall,ER8Rea_Prev_Str_Overall)          9765
cor(ER2Rum_Intercept,ER8Rea_Prev_Str_Overall)                 8686
cor(ER2Rum_Prev_MF,ER8Rea_Prev_Str_Overall)                   8981
cor(ER2Rum_Prev_Str_Overall,ER8Rea_Prev_Str_Overall)          8876
cor(ER3SBl_Intercept,ER8Rea_Prev_Str_Overall)                 9412
cor(ER3SBl_Prev_MF,ER8Rea_Prev_Str_Overall)                   9304
cor(ER3SBl_Prev_Str_Overall,ER8Rea_Prev_Str_Overall)          9109
cor(ER4ExprS_Intercept,ER8Rea_Prev_Str_Overall)               9702
cor(ER4ExprS_Prev_MF,ER8Rea_Prev_Str_Overall)                 8747
cor(ER4ExprS_Prev_Str_Overall,ER8Rea_Prev_Str_Overall)       10129
cor(ER5ExperS_Intercept,ER8Rea_Prev_Str_Overall)              9818
cor(ER5ExperS_Prev_MF,ER8Rea_Prev_Str_Overall)               10005
cor(ER5ExperS_Prev_Str_Overall,ER8Rea_Prev_Str_Overall)       9715
cor(ER6Acc_Intercept,ER8Rea_Prev_Str_Overall)                10079
cor(ER6Acc_Prev_MF,ER8Rea_Prev_Str_Overall)                   9910
cor(ER6Acc_Prev_Str_Overall,ER8Rea_Prev_Str_Overall)          9871
cor(ER7Pla_Intercept,ER8Rea_Prev_Str_Overall)                10734
cor(ER7Pla_Prev_MF,ER8Rea_Prev_Str_Overall)                  10261
cor(ER7Pla_Prev_Str_Overall,ER8Rea_Prev_Str_Overall)          9325
cor(ER8Rea_Intercept,ER8Rea_Prev_Str_Overall)                10866
cor(ER8Rea_Prev_MF,ER8Rea_Prev_Str_Overall)                  10205
cor(ER1Dis_Intercept,ER9ESu_Intercept)                        8966
cor(ER1Dis_Prev_MF,ER9ESu_Intercept)                          6158
cor(ER1Dis_Prev_Str_Overall,ER9ESu_Intercept)                 9499
cor(ER2Rum_Intercept,ER9ESu_Intercept)                        8955
cor(ER2Rum_Prev_MF,ER9ESu_Intercept)                          7853
cor(ER2Rum_Prev_Str_Overall,ER9ESu_Intercept)                 8511
cor(ER3SBl_Intercept,ER9ESu_Intercept)                        9766
cor(ER3SBl_Prev_MF,ER9ESu_Intercept)                          7562
cor(ER3SBl_Prev_Str_Overall,ER9ESu_Intercept)                 8643
cor(ER4ExprS_Intercept,ER9ESu_Intercept)                      9527
cor(ER4ExprS_Prev_MF,ER9ESu_Intercept)                        9524
cor(ER4ExprS_Prev_Str_Overall,ER9ESu_Intercept)               9306
cor(ER5ExperS_Intercept,ER9ESu_Intercept)                    10056
cor(ER5ExperS_Prev_MF,ER9ESu_Intercept)                       9564
cor(ER5ExperS_Prev_Str_Overall,ER9ESu_Intercept)              9920
cor(ER6Acc_Intercept,ER9ESu_Intercept)                       10493
cor(ER6Acc_Prev_MF,ER9ESu_Intercept)                          9961
cor(ER6Acc_Prev_Str_Overall,ER9ESu_Intercept)                10028
cor(ER7Pla_Intercept,ER9ESu_Intercept)                       10711
cor(ER7Pla_Prev_MF,ER9ESu_Intercept)                          9267
cor(ER7Pla_Prev_Str_Overall,ER9ESu_Intercept)                10310
cor(ER8Rea_Intercept,ER9ESu_Intercept)                       10808
cor(ER8Rea_Prev_MF,ER9ESu_Intercept)                          9858
cor(ER8Rea_Prev_Str_Overall,ER9ESu_Intercept)                10323
cor(ER1Dis_Intercept,ER9ESu_Prev_MF)                          9862
cor(ER1Dis_Prev_MF,ER9ESu_Prev_MF)                            9863
cor(ER1Dis_Prev_Str_Overall,ER9ESu_Prev_MF)                   8244
cor(ER2Rum_Intercept,ER9ESu_Prev_MF)                          9722
cor(ER2Rum_Prev_MF,ER9ESu_Prev_MF)                            9478
cor(ER2Rum_Prev_Str_Overall,ER9ESu_Prev_MF)                   9387
cor(ER3SBl_Intercept,ER9ESu_Prev_MF)                         10393
cor(ER3SBl_Prev_MF,ER9ESu_Prev_MF)                            9803
cor(ER3SBl_Prev_Str_Overall,ER9ESu_Prev_MF)                   9950
cor(ER4ExprS_Intercept,ER9ESu_Prev_MF)                        9983
cor(ER4ExprS_Prev_MF,ER9ESu_Prev_MF)                          9873
cor(ER4ExprS_Prev_Str_Overall,ER9ESu_Prev_MF)                10158
cor(ER5ExperS_Intercept,ER9ESu_Prev_MF)                      10892
cor(ER5ExperS_Prev_MF,ER9ESu_Prev_MF)                         9662
cor(ER5ExperS_Prev_Str_Overall,ER9ESu_Prev_MF)               10781
cor(ER6Acc_Intercept,ER9ESu_Prev_MF)                         10016
cor(ER6Acc_Prev_MF,ER9ESu_Prev_MF)                           10416
cor(ER6Acc_Prev_Str_Overall,ER9ESu_Prev_MF)                  10901
cor(ER7Pla_Intercept,ER9ESu_Prev_MF)                         10004
cor(ER7Pla_Prev_MF,ER9ESu_Prev_MF)                            9547
cor(ER7Pla_Prev_Str_Overall,ER9ESu_Prev_MF)                   9215
cor(ER8Rea_Intercept,ER9ESu_Prev_MF)                         10087
cor(ER8Rea_Prev_MF,ER9ESu_Prev_MF)                            9775
cor(ER8Rea_Prev_Str_Overall,ER9ESu_Prev_MF)                   9172
cor(ER9ESu_Intercept,ER9ESu_Prev_MF)                          9654
cor(ER1Dis_Intercept,ER9ESu_Prev_Str_Overall)                 9316
cor(ER1Dis_Prev_MF,ER9ESu_Prev_Str_Overall)                   9416
cor(ER1Dis_Prev_Str_Overall,ER9ESu_Prev_Str_Overall)          8086
cor(ER2Rum_Intercept,ER9ESu_Prev_Str_Overall)                 9777
cor(ER2Rum_Prev_MF,ER9ESu_Prev_Str_Overall)                  10036
cor(ER2Rum_Prev_Str_Overall,ER9ESu_Prev_Str_Overall)         10450
cor(ER3SBl_Intercept,ER9ESu_Prev_Str_Overall)                10305
cor(ER3SBl_Prev_MF,ER9ESu_Prev_Str_Overall)                  10790
cor(ER3SBl_Prev_Str_Overall,ER9ESu_Prev_Str_Overall)         10762
cor(ER4ExprS_Intercept,ER9ESu_Prev_Str_Overall)              10495
cor(ER4ExprS_Prev_MF,ER9ESu_Prev_Str_Overall)                10067
cor(ER4ExprS_Prev_Str_Overall,ER9ESu_Prev_Str_Overall)       10888
cor(ER5ExperS_Intercept,ER9ESu_Prev_Str_Overall)              9503
cor(ER5ExperS_Prev_MF,ER9ESu_Prev_Str_Overall)               10213
cor(ER5ExperS_Prev_Str_Overall,ER9ESu_Prev_Str_Overall)      10032
cor(ER6Acc_Intercept,ER9ESu_Prev_Str_Overall)                10216
cor(ER6Acc_Prev_MF,ER9ESu_Prev_Str_Overall)                   9411
cor(ER6Acc_Prev_Str_Overall,ER9ESu_Prev_Str_Overall)         10224
cor(ER7Pla_Intercept,ER9ESu_Prev_Str_Overall)                10564
cor(ER7Pla_Prev_MF,ER9ESu_Prev_Str_Overall)                  10690
cor(ER7Pla_Prev_Str_Overall,ER9ESu_Prev_Str_Overall)          9771
cor(ER8Rea_Intercept,ER9ESu_Prev_Str_Overall)                10500
cor(ER8Rea_Prev_MF,ER9ESu_Prev_Str_Overall)                   9699
cor(ER8Rea_Prev_Str_Overall,ER9ESu_Prev_Str_Overall)          9489
cor(ER9ESu_Intercept,ER9ESu_Prev_Str_Overall)                10027
cor(ER9ESu_Prev_MF,ER9ESu_Prev_Str_Overall)                  10592
cor(ER1Dis_Intercept,ER10Rel_Intercept)                       9303
cor(ER1Dis_Prev_MF,ER10Rel_Intercept)                         6959
cor(ER1Dis_Prev_Str_Overall,ER10Rel_Intercept)                8579
cor(ER2Rum_Intercept,ER10Rel_Intercept)                       8822
cor(ER2Rum_Prev_MF,ER10Rel_Intercept)                         6346
cor(ER2Rum_Prev_Str_Overall,ER10Rel_Intercept)                7227
cor(ER3SBl_Intercept,ER10Rel_Intercept)                       9543
cor(ER3SBl_Prev_MF,ER10Rel_Intercept)                         7372
cor(ER3SBl_Prev_Str_Overall,ER10Rel_Intercept)                7587
cor(ER4ExprS_Intercept,ER10Rel_Intercept)                    10070
cor(ER4ExprS_Prev_MF,ER10Rel_Intercept)                       7194
cor(ER4ExprS_Prev_Str_Overall,ER10Rel_Intercept)              7971
cor(ER5ExperS_Intercept,ER10Rel_Intercept)                   10192
cor(ER5ExperS_Prev_MF,ER10Rel_Intercept)                      8742
cor(ER5ExperS_Prev_Str_Overall,ER10Rel_Intercept)             8404
cor(ER6Acc_Intercept,ER10Rel_Intercept)                      10485
cor(ER6Acc_Prev_MF,ER10Rel_Intercept)                         7156
cor(ER6Acc_Prev_Str_Overall,ER10Rel_Intercept)                8314
cor(ER7Pla_Intercept,ER10Rel_Intercept)                       9778
cor(ER7Pla_Prev_MF,ER10Rel_Intercept)                         7092
cor(ER7Pla_Prev_Str_Overall,ER10Rel_Intercept)                9309
cor(ER8Rea_Intercept,ER10Rel_Intercept)                      10924
cor(ER8Rea_Prev_MF,ER10Rel_Intercept)                         8412
cor(ER8Rea_Prev_Str_Overall,ER10Rel_Intercept)                9436
cor(ER9ESu_Intercept,ER10Rel_Intercept)                      11168
cor(ER9ESu_Prev_MF,ER10Rel_Intercept)                        10777
cor(ER9ESu_Prev_Str_Overall,ER10Rel_Intercept)                9844
cor(ER1Dis_Intercept,ER10Rel_Prev_MF)                         9611
cor(ER1Dis_Prev_MF,ER10Rel_Prev_MF)                           9625
cor(ER1Dis_Prev_Str_Overall,ER10Rel_Prev_MF)                  9127
cor(ER2Rum_Intercept,ER10Rel_Prev_MF)                        10518
cor(ER2Rum_Prev_MF,ER10Rel_Prev_MF)                           9982
cor(ER2Rum_Prev_Str_Overall,ER10Rel_Prev_MF)                  9818
cor(ER3SBl_Intercept,ER10Rel_Prev_MF)                        10335
cor(ER3SBl_Prev_MF,ER10Rel_Prev_MF)                          10721
cor(ER3SBl_Prev_Str_Overall,ER10Rel_Prev_MF)                 10207
cor(ER4ExprS_Intercept,ER10Rel_Prev_MF)                      10107
cor(ER4ExprS_Prev_MF,ER10Rel_Prev_MF)                         9958
cor(ER4ExprS_Prev_Str_Overall,ER10Rel_Prev_MF)                9905
cor(ER5ExperS_Intercept,ER10Rel_Prev_MF)                     10233
cor(ER5ExperS_Prev_MF,ER10Rel_Prev_MF)                       10327
cor(ER5ExperS_Prev_Str_Overall,ER10Rel_Prev_MF)              10560
cor(ER6Acc_Intercept,ER10Rel_Prev_MF)                        10093
cor(ER6Acc_Prev_MF,ER10Rel_Prev_MF)                          10399
cor(ER6Acc_Prev_Str_Overall,ER10Rel_Prev_MF)                 10035
cor(ER7Pla_Intercept,ER10Rel_Prev_MF)                        11211
cor(ER7Pla_Prev_MF,ER10Rel_Prev_MF)                          10472
cor(ER7Pla_Prev_Str_Overall,ER10Rel_Prev_MF)                  9588
cor(ER8Rea_Intercept,ER10Rel_Prev_MF)                        10564
cor(ER8Rea_Prev_MF,ER10Rel_Prev_MF)                          10287
cor(ER8Rea_Prev_Str_Overall,ER10Rel_Prev_MF)                  9236
cor(ER9ESu_Intercept,ER10Rel_Prev_MF)                        10719
cor(ER9ESu_Prev_MF,ER10Rel_Prev_MF)                          10005
cor(ER9ESu_Prev_Str_Overall,ER10Rel_Prev_MF)                 10454
cor(ER10Rel_Intercept,ER10Rel_Prev_MF)                       10218
cor(ER1Dis_Intercept,ER10Rel_Prev_Str_Overall)                9077
cor(ER1Dis_Prev_MF,ER10Rel_Prev_Str_Overall)                  9981
cor(ER1Dis_Prev_Str_Overall,ER10Rel_Prev_Str_Overall)         9991
cor(ER2Rum_Intercept,ER10Rel_Prev_Str_Overall)               10053
cor(ER2Rum_Prev_MF,ER10Rel_Prev_Str_Overall)                 10576
cor(ER2Rum_Prev_Str_Overall,ER10Rel_Prev_Str_Overall)        10468
cor(ER3SBl_Intercept,ER10Rel_Prev_Str_Overall)               10230
cor(ER3SBl_Prev_MF,ER10Rel_Prev_Str_Overall)                 10241
cor(ER3SBl_Prev_Str_Overall,ER10Rel_Prev_Str_Overall)        10686
cor(ER4ExprS_Intercept,ER10Rel_Prev_Str_Overall)             10504
cor(ER4ExprS_Prev_MF,ER10Rel_Prev_Str_Overall)               10258
cor(ER4ExprS_Prev_Str_Overall,ER10Rel_Prev_Str_Overall)      10163
cor(ER5ExperS_Intercept,ER10Rel_Prev_Str_Overall)            10839
cor(ER5ExperS_Prev_MF,ER10Rel_Prev_Str_Overall)              10787
cor(ER5ExperS_Prev_Str_Overall,ER10Rel_Prev_Str_Overall)     10692
cor(ER6Acc_Intercept,ER10Rel_Prev_Str_Overall)                9833
cor(ER6Acc_Prev_MF,ER10Rel_Prev_Str_Overall)                 10558
cor(ER6Acc_Prev_Str_Overall,ER10Rel_Prev_Str_Overall)        10585
cor(ER7Pla_Intercept,ER10Rel_Prev_Str_Overall)                9698
cor(ER7Pla_Prev_MF,ER10Rel_Prev_Str_Overall)                 10714
cor(ER7Pla_Prev_Str_Overall,ER10Rel_Prev_Str_Overall)        10144
cor(ER8Rea_Intercept,ER10Rel_Prev_Str_Overall)               10457
cor(ER8Rea_Prev_MF,ER10Rel_Prev_Str_Overall)                 10745
cor(ER8Rea_Prev_Str_Overall,ER10Rel_Prev_Str_Overall)        10880
cor(ER9ESu_Intercept,ER10Rel_Prev_Str_Overall)               10672
cor(ER9ESu_Prev_MF,ER10Rel_Prev_Str_Overall)                 10072
cor(ER9ESu_Prev_Str_Overall,ER10Rel_Prev_Str_Overall)        10142
cor(ER10Rel_Intercept,ER10Rel_Prev_Str_Overall)              10369
cor(ER10Rel_Prev_MF,ER10Rel_Prev_Str_Overall)                10419

Regression Coefficients:
                           Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS
ER1Dis_Intercept              -5.19      0.96    -7.11    -3.34 1.00     7201
ER2Rum_Intercept              -5.28      0.95    -7.16    -3.47 1.00    10051
ER3SBl_Intercept              -5.25      1.09    -7.43    -3.17 1.00    12011
ER4ExprS_Intercept            -5.39      0.89    -7.16    -3.69 1.00    12814
ER5ExperS_Intercept           -5.17      1.06    -7.34    -3.13 1.00    11872
ER6Acc_Intercept              -4.85      0.71    -6.27    -3.46 1.00    10442
ER7Pla_Intercept              -3.84      0.76    -5.34    -2.38 1.00    10335
ER8Rea_Intercept              -6.93      1.10    -9.15    -4.82 1.00    11971
ER9ESu_Intercept              -9.42      1.17   -11.85    -7.24 1.00    10588
ER10Rel_Intercept             -3.94      0.89    -5.71    -2.20 1.00    12080
ER1Dis_Prev_MF                 0.03      0.09    -0.15     0.19 1.00     4099
ER1Dis_T1_Gender               0.43      0.39    -0.32     1.21 1.00     8430
ER1Dis_Age_2024                0.06      0.03    -0.00     0.12 1.00     7472
ER1Dis_Prev_Str_Overall        0.13      0.04     0.04     0.22 1.00    11238
ER2Rum_Prev_MF                 0.01      0.10    -0.20     0.20 1.00     8913
ER2Rum_T1_Gender              -0.08      0.38    -0.84     0.68 1.00    10968
ER2Rum_Age_2024                0.03      0.03    -0.04     0.09 1.00    10495
ER2Rum_Prev_Str_Overall        0.09      0.07    -0.06     0.21 1.00     5368
ER3SBl_Prev_MF                 0.02      0.15    -0.32     0.27 1.00     4390
ER3SBl_T1_Gender               0.02      0.44    -0.82     0.92 1.00    10933
ER3SBl_Age_2024               -0.00      0.04    -0.07     0.07 1.00    11344
ER3SBl_Prev_Str_Overall        0.12      0.05     0.01     0.22 1.00    14788
ER4ExprS_Prev_MF              -0.03      0.10    -0.23     0.16 1.00    17863
ER4ExprS_T1_Gender             0.01      0.35    -0.67     0.70 1.00    10858
ER4ExprS_Age_2024              0.03      0.03    -0.02     0.09 1.00    11755
ER4ExprS_Prev_Str_Overall      0.13      0.05     0.04     0.22 1.00    16283
ER5ExperS_Prev_MF             -0.04      0.09    -0.21     0.13 1.00    19440
ER5ExperS_T1_Gender            0.34      0.44    -0.51     1.22 1.00    11692
ER5ExperS_Age_2024             0.02      0.03    -0.05     0.09 1.00    11501
ER5ExperS_Prev_Str_Overall     0.08      0.06    -0.04     0.19 1.00    13204
ER6Acc_Prev_MF                 0.06      0.06    -0.07     0.18 1.00    15123
ER6Acc_T1_Gender               0.49      0.29    -0.07     1.06 1.00    11429
ER6Acc_Age_2024                0.05      0.02     0.00     0.09 1.00    10528
ER6Acc_Prev_Str_Overall        0.09      0.03     0.04     0.15 1.00    20560
ER7Pla_Prev_MF                -0.14      0.09    -0.33     0.02 1.00     8360
ER7Pla_T1_Gender               0.88      0.32     0.26     1.52 1.00    10491
ER7Pla_Age_2024               -0.00      0.02    -0.05     0.05 1.00    10453
ER7Pla_Prev_Str_Overall        0.10      0.03     0.04     0.17 1.00    17137
ER8Rea_Prev_MF                -0.01      0.17    -0.38     0.27 1.00     4788
ER8Rea_T1_Gender               0.70      0.44    -0.13     1.60 1.00    12827
ER8Rea_Age_2024                0.05      0.03    -0.01     0.12 1.00    13054
ER8Rea_Prev_Str_Overall        0.12      0.05     0.02     0.20 1.00    15807
ER9ESu_Prev_MF                 0.07      0.12    -0.18     0.28 1.00    10606
ER9ESu_T1_Gender               2.56      0.59     1.47     3.79 1.00    14049
ER9ESu_Age_2024                0.09      0.03     0.02     0.15 1.00    10322
ER9ESu_Prev_Str_Overall        0.12      0.05     0.02     0.21 1.00    12642
ER10Rel_Prev_MF                0.01      0.08    -0.16     0.16 1.00    14552
ER10Rel_T1_Gender              0.86      0.39     0.12     1.62 1.00    12517
ER10Rel_Age_2024              -0.00      0.03    -0.06     0.06 1.00    11284
ER10Rel_Prev_Str_Overall       0.01      0.04    -0.08     0.09 1.00    15068
                           Tail_ESS
ER1Dis_Intercept               8459
ER2Rum_Intercept               9744
ER3SBl_Intercept               9981
ER4ExprS_Intercept             9782
ER5ExperS_Intercept            9697
ER6Acc_Intercept               9566
ER7Pla_Intercept              10168
ER8Rea_Intercept              10387
ER9ESu_Intercept               9494
ER10Rel_Intercept             10269
ER1Dis_Prev_MF                 7451
ER1Dis_T1_Gender               9117
ER1Dis_Age_2024                8469
ER1Dis_Prev_Str_Overall        9526
ER2Rum_Prev_MF                 7224
ER2Rum_T1_Gender               9850
ER2Rum_Age_2024                9994
ER2Rum_Prev_Str_Overall        7922
ER3SBl_Prev_MF                 5392
ER3SBl_T1_Gender               8972
ER3SBl_Age_2024               10054
ER3SBl_Prev_Str_Overall        7250
ER4ExprS_Prev_MF               8721
ER4ExprS_T1_Gender            10146
ER4ExprS_Age_2024              9982
ER4ExprS_Prev_Str_Overall      9555
ER5ExperS_Prev_MF              9327
ER5ExperS_T1_Gender            9779
ER5ExperS_Age_2024            10188
ER5ExperS_Prev_Str_Overall     9801
ER6Acc_Prev_MF                 9534
ER6Acc_T1_Gender              10356
ER6Acc_Age_2024               10211
ER6Acc_Prev_Str_Overall        9387
ER7Pla_Prev_MF                 9700
ER7Pla_T1_Gender               9667
ER7Pla_Age_2024                9908
ER7Pla_Prev_Str_Overall       10085
ER8Rea_Prev_MF                 8038
ER8Rea_T1_Gender              10227
ER8Rea_Age_2024                9918
ER8Rea_Prev_Str_Overall        9216
ER9ESu_Prev_MF                 8308
ER9ESu_T1_Gender               9257
ER9ESu_Age_2024               10284
ER9ESu_Prev_Str_Overall        9713
ER10Rel_Prev_MF                8553
ER10Rel_T1_Gender              9865
ER10Rel_Age_2024              10060
ER10Rel_Prev_Str_Overall       9573

Draws were sampled using sample(hmc). For each parameter, Bulk_ESS
and Tail_ESS are effective sample size measures, and Rhat is the potential
scale reduction factor on split chains (at convergence, Rhat = 1).

exp(fixef(m_ERall_MF_final_randomslopes1))

                               Estimate Est.Error         Q2.5        Q97.5
ER1Dis_Intercept           5.555585e-03  2.600930 8.192156e-04 3.549738e-02
ER2Rum_Intercept           5.098226e-03  2.588835 7.741058e-04 3.105894e-02
ER3SBl_Intercept           5.247940e-03  2.981629 5.920720e-04 4.192225e-02
ER4ExprS_Intercept         4.575168e-03  2.435270 7.741195e-04 2.505844e-02
ER5ExperS_Intercept        5.662422e-03  2.895911 6.497447e-04 4.367809e-02
ER6Acc_Intercept           7.802926e-03  2.042425 1.893050e-03 3.153032e-02
ER7Pla_Intercept           2.149719e-02  2.130583 4.772677e-03 9.264510e-02
ER8Rea_Intercept           9.789138e-04  3.002846 1.057629e-04 8.062444e-03
ER9ESu_Intercept           8.141531e-05  3.213515 7.104959e-06 7.176319e-04
ER10Rel_Intercept          1.935258e-02  2.434292 3.329223e-03 1.109829e-01
ER1Dis_Prev_MF             1.030476e+00  1.092321 8.574573e-01 1.212695e+00
ER1Dis_T1_Gender           1.541598e+00  1.474398 7.261383e-01 3.337652e+00
ER1Dis_Age_2024            1.060380e+00  1.032031 9.972202e-01 1.128991e+00
ER1Dis_Prev_Str_Overall    1.138844e+00  1.045933 1.041437e+00 1.241909e+00
ER2Rum_Prev_MF             1.012374e+00  1.105591 8.158854e-01 1.216086e+00
ER2Rum_T1_Gender           9.253106e-01  1.468878 4.328930e-01 1.977401e+00
ER2Rum_Age_2024            1.025514e+00  1.031716 9.642797e-01 1.090178e+00
ER2Rum_Prev_Str_Overall    1.091609e+00  1.073423 9.393133e-01 1.235431e+00
ER3SBl_Prev_MF             1.021099e+00  1.161738 7.250346e-01 1.305807e+00
ER3SBl_T1_Gender           1.019167e+00  1.555509 4.387607e-01 2.514357e+00
ER3SBl_Age_2024            9.999644e-01  1.036743 9.311148e-01 1.073361e+00
ER3SBl_Prev_Str_Overall    1.131639e+00  1.053016 1.013824e+00 1.243907e+00
ER4ExprS_Prev_MF           9.720641e-01  1.102130 7.984554e-01 1.170891e+00
ER4ExprS_T1_Gender         1.006355e+00  1.416826 5.111576e-01 2.017379e+00
ER4ExprS_Age_2024          1.034354e+00  1.028979 9.790861e-01 1.095244e+00
ER4ExprS_Prev_Str_Overall  1.142897e+00  1.048523 1.036970e+00 1.251321e+00
ER5ExperS_Prev_MF          9.650406e-01  1.092509 8.076332e-01 1.142757e+00
ER5ExperS_T1_Gender        1.405080e+00  1.548924 6.034615e-01 3.388004e+00
ER5ExperS_Age_2024         1.019945e+00  1.035222 9.534558e-01 1.091824e+00
ER5ExperS_Prev_Str_Overall 1.088004e+00  1.062217 9.602498e-01 1.215259e+00
ER6Acc_Prev_MF             1.057123e+00  1.065258 9.300925e-01 1.194791e+00
ER6Acc_T1_Gender           1.629852e+00  1.341338 9.287972e-01 2.900379e+00
ER6Acc_Age_2024            1.047062e+00  1.023800 1.000783e+00 1.096981e+00
ER6Acc_Prev_Str_Overall    1.099507e+00  1.028576 1.039746e+00 1.161904e+00
ER7Pla_Prev_MF             8.650410e-01  1.092204 7.219617e-01 1.018386e+00
ER7Pla_T1_Gender           2.418152e+00  1.382534 1.291185e+00 4.563431e+00
ER7Pla_Age_2024            9.970726e-01  1.025006 9.501396e-01 1.047047e+00
ER7Pla_Prev_Str_Overall    1.106644e+00  1.032933 1.037002e+00 1.179503e+00
ER8Rea_Prev_MF             9.874150e-01  1.179553 6.837584e-01 1.303938e+00
ER8Rea_T1_Gender           2.022849e+00  1.553073 8.757081e-01 4.935124e+00
ER8Rea_Age_2024            1.055198e+00  1.035130 9.867948e-01 1.130037e+00
ER8Rea_Prev_Str_Overall    1.124590e+00  1.046938 1.021906e+00 1.224240e+00
ER9ESu_Prev_MF             1.076544e+00  1.124673 8.359569e-01 1.328424e+00
ER9ESu_T1_Gender           1.288367e+01  1.803081 4.369500e+00 4.440374e+01
ER9ESu_Age_2024            1.090641e+00  1.033917 1.023711e+00 1.167323e+00
ER9ESu_Prev_Str_Overall    1.129995e+00  1.049521 1.021480e+00 1.236321e+00
ER10Rel_Prev_MF            1.009637e+00  1.083447 8.545652e-01 1.173199e+00
ER10Rel_T1_Gender          2.359771e+00  1.471881 1.123982e+00 5.072396e+00
ER10Rel_Age_2024           9.997030e-01  1.030597 9.423562e-01 1.060158e+00
ER10Rel_Prev_Str_Overall   1.006451e+00  1.042408 9.257399e-01 1.088761e+00

p_values_m_ERall_MF_final_randomslopes1 <- describe_posterior(m_ERall_MF_final_randomslopes1, test = "p_direction")

Warning: Multivariate response models are not yet supported for tests `rope` and
  `p_rope`.

p_values_m_ERall_MF_final_randomslopes1

Summary of Posterior Distribution () (ER1Dis)

Parameter        | Response | Median |          95% CI |     pd |  Rhat |      ESS
----------------------------------------------------------------------------------
(Intercept)      |   ER1Dis |  -5.20 | [ -7.11, -3.34] |   100% | 1.000 |  7232.00
Prev_MF          |   ER1Dis |   0.03 | [ -0.15,  0.19] | 65.01% | 1.000 |  4067.00
T1_Gender        |   ER1Dis |   0.43 | [ -0.32,  1.21] | 87.22% | 1.000 |  8438.00
Age_2024         |   ER1Dis |   0.06 | [ -0.00,  0.12] | 96.92% | 1.000 |  7458.00
Prev_Str_Overall |   ER1Dis |   0.13 | [  0.04,  0.22] | 99.69% | 1.000 | 11158.00

# Fixed effects () (ER2Rum)

Parameter        | Response | Median |          95% CI |     pd |  Rhat |      ESS
----------------------------------------------------------------------------------
(Intercept)      |   ER2Rum |  -5.26 | [ -7.16, -3.47] |   100% | 1.000 | 10051.00
Prev_MF          |   ER2Rum |   0.02 | [ -0.20,  0.20] | 56.92% | 1.000 |  8358.00
T1_Gender        |   ER2Rum |  -0.08 | [ -0.84,  0.68] | 58.19% | 1.000 | 10835.00
Age_2024         |   ER2Rum |   0.02 | [ -0.04,  0.09] | 79.10% | 1.000 | 10470.00
Prev_Str_Overall |   ER2Rum |   0.09 | [ -0.06,  0.21] | 88.83% | 1.000 |  5305.00

# Fixed effects () (ER3SBl)

Parameter        | Response |   Median |          95% CI |     pd |  Rhat |      ESS
------------------------------------------------------------------------------------
(Intercept)      |   ER3SBl |    -5.22 | [ -7.43, -3.17] |   100% | 1.000 | 11975.00
Prev_MF          |   ER3SBl |     0.04 | [ -0.32,  0.27] | 60.29% | 1.002 |  3970.00
T1_Gender        |   ER3SBl |     0.01 | [ -0.82,  0.92] | 51.02% | 1.000 | 10884.00
Age_2024         |   ER3SBl | 2.20e-04 | [ -0.07,  0.07] | 50.28% | 1.000 | 11276.00
Prev_Str_Overall |   ER3SBl |     0.13 | [  0.01,  0.22] | 98.43% | 1.000 | 13818.00

# Fixed effects () (ER4ExprS)

Parameter        | Response |   Median |          95% CI |     pd |  Rhat |      ESS
------------------------------------------------------------------------------------
(Intercept)      | ER4ExprS |    -5.37 | [ -7.16, -3.69] |   100% | 1.000 | 12744.00
Prev_MF          | ER4ExprS |    -0.03 | [ -0.23,  0.16] | 61.22% | 1.000 | 17725.00
T1_Gender        | ER4ExprS | 1.96e-03 | [ -0.67,  0.70] | 50.18% | 1.000 | 10550.00
Age_2024         | ER4ExprS |     0.03 | [ -0.02,  0.09] | 88.33% | 1.000 | 11703.00
Prev_Str_Overall | ER4ExprS |     0.13 | [  0.04,  0.22] | 99.52% | 1.000 | 16060.00

# Fixed effects () (ER5ExperS)

Parameter        |  Response | Median |          95% CI |     pd |  Rhat |      ESS
-----------------------------------------------------------------------------------
(Intercept)      | ER5ExperS |  -5.15 | [ -7.34, -3.13] |   100% | 1.000 | 11838.00
Prev_MF          | ER5ExperS |  -0.03 | [ -0.21,  0.13] | 65.47% | 1.000 | 19109.00
T1_Gender        | ER5ExperS |   0.33 | [ -0.51,  1.22] | 78.41% | 1.000 | 11733.00
Age_2024         | ER5ExperS |   0.02 | [ -0.05,  0.09] | 71.51% | 1.000 | 11488.00
Prev_Str_Overall | ER5ExperS |   0.09 | [ -0.04,  0.19] | 91.27% | 1.000 | 12953.00

# Fixed effects () (ER6Acc)

Parameter        | Response | Median |          95% CI |     pd |  Rhat |      ESS
----------------------------------------------------------------------------------
(Intercept)      |   ER6Acc |  -4.85 | [ -6.27, -3.46] |   100% | 1.000 | 10401.00
Prev_MF          |   ER6Acc |   0.06 | [ -0.07,  0.18] | 81.75% | 1.000 | 14886.00
T1_Gender        |   ER6Acc |   0.49 | [ -0.07,  1.06] | 95.28% | 1.000 | 11354.00
Age_2024         |   ER6Acc |   0.05 | [  0.00,  0.09] | 97.67% | 1.000 | 10500.00
Prev_Str_Overall |   ER6Acc |   0.09 | [  0.04,  0.15] | 99.95% | 1.000 | 20358.00

# Fixed effects () (ER7Pla)

Parameter        | Response |    Median |          95% CI |     pd |  Rhat |      ESS
-------------------------------------------------------------------------------------
(Intercept)      |   ER7Pla |     -3.82 | [ -5.34, -2.38] |   100% | 1.000 | 10403.00
Prev_MF          |   ER7Pla |     -0.14 | [ -0.33,  0.02] | 95.70% | 1.000 |  8253.00
T1_Gender        |   ER7Pla |      0.88 | [  0.26,  1.52] | 99.74% | 1.000 | 10374.00
Age_2024         |   ER7Pla | -2.97e-03 | [ -0.05,  0.05] | 55.08% | 1.000 | 10419.00
Prev_Str_Overall |   ER7Pla |      0.10 | [  0.04,  0.17] | 99.78% | 1.000 | 17042.00

# Fixed effects () (ER8Rea)

Parameter        | Response |   Median |          95% CI |     pd |  Rhat |      ESS
------------------------------------------------------------------------------------
(Intercept)      |   ER8Rea |    -6.89 | [ -9.15, -4.82] |   100% | 1.000 | 12060.00
Prev_MF          |   ER8Rea | 2.26e-03 | [ -0.38,  0.27] | 50.57% | 1.000 |  4772.00
T1_Gender        |   ER8Rea |     0.70 | [ -0.13,  1.60] | 94.76% | 1.000 | 12752.00
Age_2024         |   ER8Rea |     0.05 | [ -0.01,  0.12] | 94.24% | 1.000 | 13062.00
Prev_Str_Overall |   ER8Rea |     0.12 | [  0.02,  0.20] | 98.78% | 1.000 | 15089.00

# Fixed effects () (ER9ESu)

Parameter        | Response | Median |          95% CI |     pd |  Rhat |      ESS
----------------------------------------------------------------------------------
(Intercept)      |   ER9ESu |  -9.38 | [-11.85, -7.24] |   100% | 1.000 | 10391.00
Prev_MF          |   ER9ESu |   0.08 | [ -0.18,  0.28] | 75.31% | 1.000 | 10013.00
T1_Gender        |   ER9ESu |   2.53 | [  1.47,  3.79] |   100% | 1.000 | 13806.00
Age_2024         |   ER9ESu |   0.09 | [  0.02,  0.15] | 99.61% | 1.000 | 10260.00
Prev_Str_Overall |   ER9ESu |   0.12 | [  0.02,  0.21] | 98.83% | 1.000 | 12400.00

# Fixed effects () (ER10Rel)

Parameter        | Response |    Median |          95% CI |     pd |  Rhat |      ESS
-------------------------------------------------------------------------------------
(Intercept)      |  ER10Rel |     -3.94 | [ -5.71, -2.20] |   100% | 1.000 | 12093.00
Prev_MF          |  ER10Rel |      0.01 | [ -0.16,  0.16] | 56.30% | 1.000 | 13972.00
T1_Gender        |  ER10Rel |      0.85 | [  0.12,  1.62] | 98.77% | 1.000 | 12535.00
Age_2024         |  ER10Rel | -2.33e-05 | [ -0.06,  0.06] | 50.05% | 1.000 | 11308.00
Prev_Str_Overall |  ER10Rel |  6.80e-03 | [ -0.08,  0.09] | 56.99% | 1.000 | 14847.00

#MF
2* (1 - .6501)

[1] 0.6998

2* (1 -.5692)

[1] 0.8616

2* (1 -.6029)

[1] 0.7942

2* (1 -.6122)

[1] 0.7756

2* (1 -.6547)

[1] 0.6906

2* (1 -.8175)

[1] 0.365

2* (1 -.9570)

[1] 0.086

2* (1 -.5057)

[1] 0.9886

2* (1 -.7531)

[1] 0.4938

2* (1 -.5630)

[1] 0.874

#Str
2* (1 - .9969)

[1] 0.0062

2* (1 -.8883)

[1] 0.2234

2* (1 - .9843)

[1] 0.0314

2* (1 -.9952)

[1] 0.0096

2* (1 -.9127)

[1] 0.1746

2* (1 -.9995)

[1] 0.001

2* (1 -.9978)

[1] 0.0044

2* (1 -.9878)

[1] 0.0244

2* (1 -.9883)

[1] 0.0234

2* (1 -.5699)

[1] 0.8602

# Robustness analysis: restricting the sample to participants with ≥10 valid pairs
# rerun <- TRUE
# 
# if (rerun) {
#   Sys.time()
#   m_ERall_MF_final_10pairs <- brm(mvbind(ER_1_Dis, ER_2_Rum, ER_3_SBl, ER_4_ExprS, 
#                                  ER_5_ExperS, ER_6_Acc, ER_7_Pla, ER_8_Rea,
#                                  ER_9_ESu, ER_10_Rel) ~ Prev_MF + T1_Gender + Age_2024 + 
#                             Prev_Str_Overall + (1 | p | PID),
#                           family = bernoulli(link = "logit"), data = d_10validpairs,
#                           backend = "cmdstanr", chains = 4, iter = 4000, warmup = 1000, cores = 4)
#   saveRDS(m_ERall_MF_final_10pairs, file = "m_ERall_MF_final_10pairs.RDS")
#   Sys.time()
# }
m_ERall_MF_final_10pairs <- readRDS("m_ERall_MF_final_10pairs.RDS")
summary(m_ERall_MF_final_10pairs)

 Family: MV(bernoulli, bernoulli, bernoulli, bernoulli, bernoulli, bernoulli, bernoulli, bernoulli, bernoulli, bernoulli) 
  Links: mu = logit
         mu = logit
         mu = logit
         mu = logit
         mu = logit
         mu = logit
         mu = logit
         mu = logit
         mu = logit
         mu = logit 
Formula: ER_1_Dis ~ Prev_MF + T1_Gender + Age_2024 + Prev_Str_Overall + (1 | p | PID) 
         ER_2_Rum ~ Prev_MF + T1_Gender + Age_2024 + Prev_Str_Overall + (1 | p | PID) 
         ER_3_SBl ~ Prev_MF + T1_Gender + Age_2024 + Prev_Str_Overall + (1 | p | PID) 
         ER_4_ExprS ~ Prev_MF + T1_Gender + Age_2024 + Prev_Str_Overall + (1 | p | PID) 
         ER_5_ExperS ~ Prev_MF + T1_Gender + Age_2024 + Prev_Str_Overall + (1 | p | PID) 
         ER_6_Acc ~ Prev_MF + T1_Gender + Age_2024 + Prev_Str_Overall + (1 | p | PID) 
         ER_7_Pla ~ Prev_MF + T1_Gender + Age_2024 + Prev_Str_Overall + (1 | p | PID) 
         ER_8_Rea ~ Prev_MF + T1_Gender + Age_2024 + Prev_Str_Overall + (1 | p | PID) 
         ER_9_ESu ~ Prev_MF + T1_Gender + Age_2024 + Prev_Str_Overall + (1 | p | PID) 
         ER_10_Rel ~ Prev_MF + T1_Gender + Age_2024 + Prev_Str_Overall + (1 | p | PID) 
   Data: d_10validpairs (Number of observations: 6348) 
  Draws: 4 chains, each with iter = 4000; warmup = 1000; thin = 1;
         total post-warmup draws = 12000

Multilevel Hyperparameters:
~PID (Number of levels: 140) 
                                            Estimate Est.Error l-95% CI
sd(ER1Dis_Intercept)                            1.74      0.15     1.47
sd(ER2Rum_Intercept)                            1.46      0.18     1.14
sd(ER3SBl_Intercept)                            1.69      0.22     1.31
sd(ER4ExprS_Intercept)                          1.29      0.15     1.03
sd(ER5ExperS_Intercept)                         1.83      0.19     1.49
sd(ER6Acc_Intercept)                            1.36      0.12     1.15
sd(ER7Pla_Intercept)                            1.34      0.13     1.12
sd(ER8Rea_Intercept)                            1.57      0.20     1.23
sd(ER9ESu_Intercept)                            1.45      0.18     1.13
sd(ER10Rel_Intercept)                           1.73      0.16     1.45
cor(ER1Dis_Intercept,ER2Rum_Intercept)          0.34      0.11     0.11
cor(ER1Dis_Intercept,ER3SBl_Intercept)          0.29      0.12     0.06
cor(ER2Rum_Intercept,ER3SBl_Intercept)          0.73      0.09     0.54
cor(ER1Dis_Intercept,ER4ExprS_Intercept)        0.29      0.11     0.06
cor(ER2Rum_Intercept,ER4ExprS_Intercept)        0.31      0.12     0.06
cor(ER3SBl_Intercept,ER4ExprS_Intercept)        0.41      0.12     0.16
cor(ER1Dis_Intercept,ER5ExperS_Intercept)       0.42      0.09     0.22
cor(ER2Rum_Intercept,ER5ExperS_Intercept)       0.36      0.11     0.14
cor(ER3SBl_Intercept,ER5ExperS_Intercept)       0.43      0.11     0.21
cor(ER4ExprS_Intercept,ER5ExperS_Intercept)     0.86      0.06     0.73
cor(ER1Dis_Intercept,ER6Acc_Intercept)          0.31      0.09     0.12
cor(ER2Rum_Intercept,ER6Acc_Intercept)          0.36      0.11     0.14
cor(ER3SBl_Intercept,ER6Acc_Intercept)          0.33      0.11     0.11
cor(ER4ExprS_Intercept,ER6Acc_Intercept)        0.63      0.09     0.44
cor(ER5ExperS_Intercept,ER6Acc_Intercept)       0.50      0.09     0.31
cor(ER1Dis_Intercept,ER7Pla_Intercept)          0.41      0.09     0.22
cor(ER2Rum_Intercept,ER7Pla_Intercept)          0.47      0.10     0.25
cor(ER3SBl_Intercept,ER7Pla_Intercept)          0.37      0.11     0.13
cor(ER4ExprS_Intercept,ER7Pla_Intercept)        0.33      0.11     0.11
cor(ER5ExperS_Intercept,ER7Pla_Intercept)       0.30      0.11     0.08
cor(ER6Acc_Intercept,ER7Pla_Intercept)          0.73      0.07     0.58
cor(ER1Dis_Intercept,ER8Rea_Intercept)          0.11      0.12    -0.13
cor(ER2Rum_Intercept,ER8Rea_Intercept)          0.50      0.11     0.27
cor(ER3SBl_Intercept,ER8Rea_Intercept)          0.36      0.12     0.11
cor(ER4ExprS_Intercept,ER8Rea_Intercept)        0.27      0.12     0.02
cor(ER5ExperS_Intercept,ER8Rea_Intercept)       0.12      0.12    -0.12
cor(ER6Acc_Intercept,ER8Rea_Intercept)          0.55      0.10     0.34
cor(ER7Pla_Intercept,ER8Rea_Intercept)          0.63      0.09     0.43
cor(ER1Dis_Intercept,ER9ESu_Intercept)          0.27      0.11     0.04
cor(ER2Rum_Intercept,ER9ESu_Intercept)          0.54      0.11     0.31
cor(ER3SBl_Intercept,ER9ESu_Intercept)          0.49      0.12     0.22
cor(ER4ExprS_Intercept,ER9ESu_Intercept)        0.35      0.12     0.10
cor(ER5ExperS_Intercept,ER9ESu_Intercept)       0.27      0.12     0.03
cor(ER6Acc_Intercept,ER9ESu_Intercept)          0.63      0.09     0.44
cor(ER7Pla_Intercept,ER9ESu_Intercept)          0.54      0.10     0.33
cor(ER8Rea_Intercept,ER9ESu_Intercept)          0.61      0.11     0.38
cor(ER1Dis_Intercept,ER10Rel_Intercept)         0.53      0.08     0.36
cor(ER2Rum_Intercept,ER10Rel_Intercept)         0.26      0.11     0.03
cor(ER3SBl_Intercept,ER10Rel_Intercept)         0.22      0.12    -0.01
cor(ER4ExprS_Intercept,ER10Rel_Intercept)       0.12      0.12    -0.11
cor(ER5ExperS_Intercept,ER10Rel_Intercept)      0.24      0.10     0.04
cor(ER6Acc_Intercept,ER10Rel_Intercept)         0.33      0.09     0.13
cor(ER7Pla_Intercept,ER10Rel_Intercept)         0.42      0.09     0.23
cor(ER8Rea_Intercept,ER10Rel_Intercept)         0.23      0.12    -0.00
cor(ER9ESu_Intercept,ER10Rel_Intercept)         0.40      0.11     0.18
                                            u-95% CI Rhat Bulk_ESS Tail_ESS
sd(ER1Dis_Intercept)                            2.05 1.00     2859     5550
sd(ER2Rum_Intercept)                            1.84 1.00     4292     6529
sd(ER3SBl_Intercept)                            2.16 1.00     5797     7472
sd(ER4ExprS_Intercept)                          1.61 1.00     6240     7858
sd(ER5ExperS_Intercept)                         2.25 1.00     4833     7009
sd(ER6Acc_Intercept)                            1.62 1.00     6207     8063
sd(ER7Pla_Intercept)                            1.61 1.00     5991     7686
sd(ER8Rea_Intercept)                            2.01 1.00     6480     7916
sd(ER9ESu_Intercept)                            1.83 1.00     5659     8413
sd(ER10Rel_Intercept)                           2.06 1.00     6417     8318
cor(ER1Dis_Intercept,ER2Rum_Intercept)          0.54 1.00     4245     7019
cor(ER1Dis_Intercept,ER3SBl_Intercept)          0.50 1.00     5242     8305
cor(ER2Rum_Intercept,ER3SBl_Intercept)          0.88 1.00     3812     6684
cor(ER1Dis_Intercept,ER4ExprS_Intercept)        0.49 1.00     5275     8081
cor(ER2Rum_Intercept,ER4ExprS_Intercept)        0.54 1.00     2563     5812
cor(ER3SBl_Intercept,ER4ExprS_Intercept)        0.63 1.00     3181     5314
cor(ER1Dis_Intercept,ER5ExperS_Intercept)       0.60 1.00     5264     8003
cor(ER2Rum_Intercept,ER5ExperS_Intercept)       0.56 1.00     2992     6453
cor(ER3SBl_Intercept,ER5ExperS_Intercept)       0.63 1.00     3041     5800
cor(ER4ExprS_Intercept,ER5ExperS_Intercept)     0.95 1.00     3897     6992
cor(ER1Dis_Intercept,ER6Acc_Intercept)          0.49 1.00     4784     7508
cor(ER2Rum_Intercept,ER6Acc_Intercept)          0.56 1.00     2643     5522
cor(ER3SBl_Intercept,ER6Acc_Intercept)          0.53 1.00     2994     6194
cor(ER4ExprS_Intercept,ER6Acc_Intercept)        0.78 1.00     2465     4683
cor(ER5ExperS_Intercept,ER6Acc_Intercept)       0.67 1.00     3388     6437
cor(ER1Dis_Intercept,ER7Pla_Intercept)          0.58 1.00     5418     7461
cor(ER2Rum_Intercept,ER7Pla_Intercept)          0.65 1.00     3112     6361
cor(ER3SBl_Intercept,ER7Pla_Intercept)          0.58 1.00     3649     6569
cor(ER4ExprS_Intercept,ER7Pla_Intercept)        0.54 1.00     2571     5745
cor(ER5ExperS_Intercept,ER7Pla_Intercept)       0.49 1.00     3523     6385
cor(ER6Acc_Intercept,ER7Pla_Intercept)          0.84 1.00     6477     8721
cor(ER1Dis_Intercept,ER8Rea_Intercept)          0.33 1.00     5898     7660
cor(ER2Rum_Intercept,ER8Rea_Intercept)          0.70 1.00     4071     6869
cor(ER3SBl_Intercept,ER8Rea_Intercept)          0.59 1.00     4165     7102
cor(ER4ExprS_Intercept,ER8Rea_Intercept)        0.50 1.00     3781     7525
cor(ER5ExperS_Intercept,ER8Rea_Intercept)       0.35 1.00     5317     8155
cor(ER6Acc_Intercept,ER8Rea_Intercept)          0.72 1.00     8044     9677
cor(ER7Pla_Intercept,ER8Rea_Intercept)          0.80 1.00     7224     9852
cor(ER1Dis_Intercept,ER9ESu_Intercept)          0.49 1.00     6159     8100
cor(ER2Rum_Intercept,ER9ESu_Intercept)          0.74 1.00     3408     6376
cor(ER3SBl_Intercept,ER9ESu_Intercept)          0.71 1.00     3221     6115
cor(ER4ExprS_Intercept,ER9ESu_Intercept)        0.57 1.00     3395     7127
cor(ER5ExperS_Intercept,ER9ESu_Intercept)       0.49 1.00     4983     8537
cor(ER6Acc_Intercept,ER9ESu_Intercept)          0.79 1.00     6226     8218
cor(ER7Pla_Intercept,ER9ESu_Intercept)          0.72 1.00     7039     9733
cor(ER8Rea_Intercept,ER9ESu_Intercept)          0.79 1.00     5282     7975
cor(ER1Dis_Intercept,ER10Rel_Intercept)         0.68 1.00     5690     8127
cor(ER2Rum_Intercept,ER10Rel_Intercept)         0.47 1.00     2842     6864
cor(ER3SBl_Intercept,ER10Rel_Intercept)         0.45 1.00     3444     6637
cor(ER4ExprS_Intercept,ER10Rel_Intercept)       0.34 1.00     2184     5678
cor(ER5ExperS_Intercept,ER10Rel_Intercept)      0.44 1.00     5037     7306
cor(ER6Acc_Intercept,ER10Rel_Intercept)         0.51 1.00     5884     8839
cor(ER7Pla_Intercept,ER10Rel_Intercept)         0.59 1.00     5806     8664
cor(ER8Rea_Intercept,ER10Rel_Intercept)         0.44 1.00     4365     7791
cor(ER9ESu_Intercept,ER10Rel_Intercept)         0.60 1.00     4111     7004

Regression Coefficients:
                           Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS
ER1Dis_Intercept              -5.46      0.94    -7.29    -3.63 1.00     2108
ER2Rum_Intercept              -5.73      0.99    -7.69    -3.78 1.00     3887
ER3SBl_Intercept              -5.21      1.15    -7.56    -3.01 1.00     4896
ER4ExprS_Intercept            -5.47      0.88    -7.21    -3.77 1.00     5247
ER5ExperS_Intercept           -5.31      1.13    -7.59    -3.14 1.00     4614
ER6Acc_Intercept              -5.08      0.78    -6.64    -3.57 1.00     4711
ER7Pla_Intercept              -3.98      0.78    -5.51    -2.44 1.00     4440
ER8Rea_Intercept              -6.90      1.10    -9.14    -4.79 1.00     6418
ER9ESu_Intercept              -8.91      1.09   -11.07    -6.83 1.00     5648
ER10Rel_Intercept             -3.99      0.94    -5.84    -2.17 1.00     4445
ER1Dis_Prev_MF                 0.11      0.06     0.00     0.23 1.00    14146
ER1Dis_T1_Gender               0.68      0.40    -0.10     1.47 1.00     2556
ER1Dis_Age_2024                0.06      0.03    -0.00     0.12 1.00     1853
ER1Dis_Prev_Str_Overall        0.11      0.03     0.06     0.16 1.00    13992
ER2Rum_Prev_MF                 0.04      0.08    -0.12     0.20 1.00    15114
ER2Rum_T1_Gender               0.10      0.40    -0.68     0.89 1.00     4416
ER2Rum_Age_2024                0.03      0.03    -0.03     0.10 1.00     3604
ER2Rum_Prev_Str_Overall        0.16      0.04     0.09     0.24 1.00    14043
ER3SBl_Prev_MF                 0.10      0.09    -0.08     0.28 1.00    16796
ER3SBl_T1_Gender              -0.03      0.47    -0.96     0.90 1.00     4784
ER3SBl_Age_2024               -0.01      0.04    -0.08     0.07 1.00     4726
ER3SBl_Prev_Str_Overall        0.16      0.04     0.07     0.24 1.00    15667
ER4ExprS_Prev_MF              -0.04      0.09    -0.21     0.13 1.00    10963
ER4ExprS_T1_Gender             0.17      0.35    -0.51     0.89 1.00     4643
ER4ExprS_Age_2024              0.04      0.03    -0.02     0.09 1.00     4706
ER4ExprS_Prev_Str_Overall      0.13      0.04     0.05     0.20 1.00    13565
ER5ExperS_Prev_MF             -0.07      0.08    -0.23     0.09 1.00    16593
ER5ExperS_T1_Gender            0.14      0.46    -0.74     1.06 1.00     4564
ER5ExperS_Age_2024             0.03      0.04    -0.04     0.11 1.00     4049
ER5ExperS_Prev_Str_Overall     0.13      0.03     0.06     0.20 1.00    15882
ER6Acc_Prev_MF                 0.06      0.06    -0.05     0.18 1.00    16190
ER6Acc_T1_Gender               0.44      0.33    -0.20     1.08 1.00     4362
ER6Acc_Age_2024                0.05      0.03     0.00     0.10 1.00     4384
ER6Acc_Prev_Str_Overall        0.09      0.02     0.04     0.14 1.00    15432
ER7Pla_Prev_MF                -0.07      0.06    -0.19     0.06 1.00    19270
ER7Pla_T1_Gender               0.90      0.34     0.24     1.58 1.00     4557
ER7Pla_Age_2024               -0.00      0.03    -0.05     0.05 1.00     3974
ER7Pla_Prev_Str_Overall        0.10      0.03     0.05     0.16 1.00    19098
ER8Rea_Prev_MF                 0.14      0.09    -0.04     0.31 1.00    16673
ER8Rea_T1_Gender               0.60      0.45    -0.25     1.49 1.00     6385
ER8Rea_Age_2024                0.04      0.04    -0.03     0.12 1.00     5917
ER8Rea_Prev_Str_Overall        0.14      0.04     0.07     0.22 1.00    17266
ER9ESu_Prev_MF                 0.08      0.08    -0.08     0.24 1.00    18502
ER9ESu_T1_Gender               2.61      0.57     1.55     3.79 1.00     7913
ER9ESu_Age_2024                0.07      0.03     0.01     0.13 1.00     4830
ER9ESu_Prev_Str_Overall        0.14      0.03     0.08     0.21 1.00    17306
ER10Rel_Prev_MF                0.04      0.06    -0.09     0.16 1.00    20922
ER10Rel_T1_Gender              0.83      0.41     0.03     1.66 1.00     4990
ER10Rel_Age_2024               0.00      0.03    -0.06     0.07 1.00     3948
ER10Rel_Prev_Str_Overall       0.00      0.03    -0.06     0.06 1.00    20356
                           Tail_ESS
ER1Dis_Intercept               4415
ER2Rum_Intercept               6215
ER3SBl_Intercept               7437
ER4ExprS_Intercept             7246
ER5ExperS_Intercept            6875
ER6Acc_Intercept               7105
ER7Pla_Intercept               7416
ER8Rea_Intercept               8376
ER9ESu_Intercept               8166
ER10Rel_Intercept              6798
ER1Dis_Prev_MF                 9301
ER1Dis_T1_Gender               4410
ER1Dis_Age_2024                3666
ER1Dis_Prev_Str_Overall        9941
ER2Rum_Prev_MF                 9655
ER2Rum_T1_Gender               6546
ER2Rum_Age_2024                6459
ER2Rum_Prev_Str_Overall       10370
ER3SBl_Prev_MF                 9620
ER3SBl_T1_Gender               7293
ER3SBl_Age_2024                7123
ER3SBl_Prev_Str_Overall        9665
ER4ExprS_Prev_MF               8912
ER4ExprS_T1_Gender             6230
ER4ExprS_Age_2024              7098
ER4ExprS_Prev_Str_Overall      9275
ER5ExperS_Prev_MF              9327
ER5ExperS_T1_Gender            6465
ER5ExperS_Age_2024             6496
ER5ExperS_Prev_Str_Overall     9883
ER6Acc_Prev_MF                 9172
ER6Acc_T1_Gender               6103
ER6Acc_Age_2024                6397
ER6Acc_Prev_Str_Overall       10130
ER7Pla_Prev_MF                 9127
ER7Pla_T1_Gender               6012
ER7Pla_Age_2024                6974
ER7Pla_Prev_Str_Overall        9482
ER8Rea_Prev_MF                 9581
ER8Rea_T1_Gender               8197
ER8Rea_Age_2024                8283
ER8Rea_Prev_Str_Overall        9315
ER9ESu_Prev_MF                 9382
ER9ESu_T1_Gender               7950
ER9ESu_Age_2024                8167
ER9ESu_Prev_Str_Overall        9199
ER10Rel_Prev_MF                9018
ER10Rel_T1_Gender              6529
ER10Rel_Age_2024               6451
ER10Rel_Prev_Str_Overall       9255

Draws were sampled using sample(hmc). For each parameter, Bulk_ESS
and Tail_ESS are effective sample size measures, and Rhat is the potential
scale reduction factor on split chains (at convergence, Rhat = 1).

#### AIM 3 - To investigate whether the number of emotion regulation strategies implemented predicts mental fatigue####

# Exploratory categorical specification of ER count (superseded by monotonic model)
# if (rerun) {
#   start_time <- Sys.time()
#   print(start_time)
#   m_MF_ER_count_cap5_final <- brm(MF ~ Prev_ER_count_cap5 + T1_Gender + Age_2024 + Prev_Str_Overall 
#                                  + Prev_MF_Within + (1 | PID),
#                                  family = cumulative(link = "logit", threshold = "flexible"), data = d,
#                                  backend = "cmdstanr", chains = 4, iter = 4000, warmup = 1000, cores = 4)
#   saveRDS(m_MF_ER_count_cap5_final, file = "m_MF_ER_count_cap5_final.RDS")
#   end_time <- Sys.time()
#   print(end_time)
# }

m_MF_ER_count_cap5_final <- readRDS("m_MF_ER_count_cap5_final.RDS")
summary(m_MF_ER_count_cap5_final)

 Family: cumulative 
  Links: mu = logit; disc = identity 
Formula: MF ~ Prev_ER_count_cap5 + T1_Gender + Age_2024 + Prev_Str_Overall + Prev_MF_Within + (1 | PID) 
   Data: d (Number of observations: 6492) 
  Draws: 4 chains, each with iter = 4000; warmup = 1000; thin = 1;
         total post-warmup draws = 12000

Multilevel Hyperparameters:
~PID (Number of levels: 179) 
              Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
sd(Intercept)     1.81      0.11     1.61     2.04 1.00     1890     4196

Regression Coefficients:
                     Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS
Intercept[1]            -2.22      0.78    -3.76    -0.70 1.00     1098
Intercept[2]             0.57      0.78    -0.97     2.09 1.00     1095
Intercept[3]             2.80      0.78     1.27     4.32 1.00     1099
Intercept[4]             4.87      0.79     3.34     6.41 1.00     1113
Prev_ER_count_cap51     -0.03      0.07    -0.18     0.10 1.00    17021
Prev_ER_count_cap52      0.05      0.10    -0.15     0.25 1.00    14482
Prev_ER_count_cap53      0.05      0.15    -0.25     0.34 1.00    17044
Prev_ER_count_cap54     -0.08      0.23    -0.53     0.36 1.00    17232
Prev_ER_count_cap55P    -0.06      0.32    -0.69     0.57 1.00    22574
T1_Gender               -0.35      0.33    -1.01     0.31 1.00     1190
Age_2024                -0.01      0.03    -0.07     0.04 1.00      972
Prev_Str_Overall         0.11      0.02     0.07     0.15 1.00    11582
Prev_MF_Within           0.94      0.04     0.87     1.02 1.00    20227
                     Tail_ESS
Intercept[1]             2080
Intercept[2]             2116
Intercept[3]             2096
Intercept[4]             2107
Prev_ER_count_cap51      9459
Prev_ER_count_cap52     10203
Prev_ER_count_cap53     10072
Prev_ER_count_cap54      9928
Prev_ER_count_cap55P     9430
T1_Gender                2319
Age_2024                 1902
Prev_Str_Overall         9786
Prev_MF_Within           9451

Further Distributional Parameters:
     Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
disc     1.00      0.00     1.00     1.00   NA       NA       NA

Draws were sampled using sample(hmc). For each parameter, Bulk_ESS
and Tail_ESS are effective sample size measures, and Rhat is the potential
scale reduction factor on split chains (at convergence, Rhat = 1).

exp(fixef(m_MF_ER_count_cap5_final))

                        Estimate Est.Error        Q2.5       Q97.5
Intercept[1]           0.1083852  2.180327  0.02331261   0.4952295
Intercept[2]           1.7625613  2.179514  0.38012339   8.1079515
Intercept[3]          16.5191892  2.184145  3.55344214  75.0816359
Intercept[4]         130.9363679  2.194457 28.12582659 605.6059336
Prev_ER_count_cap51    0.9658696  1.074623  0.83731007   1.1102343
Prev_ER_count_cap52    1.0494630  1.107575  0.85881347   1.2821151
Prev_ER_count_cap53    1.0475699  1.161600  0.77941643   1.4066485
Prev_ER_count_cap54    0.9189917  1.258180  0.58754642   1.4387666
Prev_ER_count_cap55P   0.9385207  1.378661  0.50000997   1.7613499
T1_Gender              0.7059710  1.395172  0.36390416   1.3606153
Age_2024               0.9880163  1.027674  0.93636928   1.0412852
Prev_Str_Overall       1.1162055  1.018119  1.07750031   1.1561363
Prev_MF_Within         2.5696045  1.037421  2.38988094   2.7602548

pd_m_MF_ER_count_cap5_final <- pd(m_MF_ER_count_cap5_final)
pd_m_MF_ER_count_cap5_final

Probability of Direction

Parameter            |     pd
-----------------------------
Intercept[1]         | 99.70%
Intercept[2]         | 76.88%
Intercept[3]         | 99.97%
Intercept[4]         |   100%
Prev_ER_count_cap51  | 68.62%
Prev_ER_count_cap52  | 68.41%
Prev_ER_count_cap53  | 62.27%
Prev_ER_count_cap54  | 64.60%
Prev_ER_count_cap55P | 56.97%
T1_Gender            | 85.42%
Age_2024             | 67.48%
Prev_Str_Overall     |   100%
Prev_MF_Within       |   100%

p_values <- describe_posterior(m_MF_ER_count_cap5_final, test = "p_direction")

# Display the p-values
p_values

Summary of Posterior Distribution

Parameter            | Median |         95% CI |     pd |  Rhat |      ESS
--------------------------------------------------------------------------
Intercept[1]         |  -2.23 | [-3.76, -0.70] | 99.70% | 1.002 |  1093.00
Intercept[2]         |   0.55 | [-0.97,  2.09] | 76.88% | 1.002 |  1090.00
Intercept[3]         |   2.79 | [ 1.27,  4.32] | 99.97% | 1.002 |  1094.00
Intercept[4]         |   4.87 | [ 3.34,  6.41] |   100% | 1.002 |  1109.00
Prev_ER_count_cap51  |  -0.03 | [-0.18,  0.10] | 68.62% | 1.000 | 16919.00
Prev_ER_count_cap52  |   0.05 | [-0.15,  0.25] | 68.41% | 1.000 | 14543.00
Prev_ER_count_cap53  |   0.05 | [-0.25,  0.34] | 62.27% | 1.000 | 17305.00
Prev_ER_count_cap54  |  -0.08 | [-0.53,  0.36] | 64.60% | 1.000 | 17097.00
Prev_ER_count_cap55P |  -0.06 | [-0.69,  0.57] | 56.97% | 1.000 | 22371.00
T1_Gender            |  -0.35 | [-1.01,  0.31] | 85.42% | 1.004 |  1172.00
Age_2024             |  -0.01 | [-0.07,  0.04] | 67.48% | 1.003 |   964.00
Prev_Str_Overall     |   0.11 | [ 0.07,  0.15] |   100% | 1.000 | 11542.00
Prev_MF_Within       |   0.94 | [ 0.87,  1.02] |   100% | 1.000 | 20125.00

2 * (1 - .6862)

[1] 0.6276

2 * (1 - .6841)

[1] 0.6318

2 * (1 - .6227)

[1] 0.7546

2 * (1 - .6460)

[1] 0.708

2 * (1 - .5697)

[1] 0.8606

2 * (1 - .8542)

[1] 0.2916

2 * (1 - .6748)

[1] 0.6504

2 * (1 - 1)

[1] 0

2 * (1 - 1)

[1] 0

# Adding random slopes to check model convergence and robustness of results
# rerun <- TRUE
# if (rerun) {
# m_MF_ER_count_cap5_final_randomslopes1 <- brm(
#   formula = MF ~ Prev_ER_count_cap5 + T1_Gender + Age_2024 + Prev_Str_Overall +
#     Prev_MF_Within +(1 + Prev_ER_count_cap5 + Prev_Str_Overall + Prev_MF_Within | PID),  data = d,  
#   backend = "cmdstanr", chains = 4, iter = 4000, warmup = 1000, cores = 4)
# }
# saveRDS(m_MF_ER_count_cap5_final_randomslopes1, file = "m_MF_ER_count_cap5_final_randomslopes1.RDS")

m_MF_ER_count_cap5_final_randomslopes1 <- readRDS("m_MF_ER_count_cap5_final_randomslopes1.RDS")
summary(m_MF_ER_count_cap5_final_randomslopes1)

 Family: cumulative 
  Links: mu = logit; disc = identity 
Formula: MF ~ Prev_ER_count_cap5 + T1_Gender + Age_2024 + Prev_Str_Overall + Prev_MF_Within + (1 + Prev_ER_count_cap5 + Prev_Str_Overall + Prev_MF_Within | PID) 
   Data: d (Number of observations: 6492) 
  Draws: 4 chains, each with iter = 4000; warmup = 1000; thin = 1;
         total post-warmup draws = 12000

Multilevel Hyperparameters:
~PID (Number of levels: 179) 
                                              Estimate Est.Error l-95% CI
sd(Intercept)                                     1.94      0.12     1.71
sd(Prev_ER_count_cap51)                           0.11      0.09     0.00
sd(Prev_ER_count_cap52)                           0.22      0.16     0.01
sd(Prev_ER_count_cap53)                           0.28      0.20     0.01
sd(Prev_ER_count_cap54)                           0.37      0.29     0.01
sd(Prev_ER_count_cap55P)                          0.92      0.57     0.05
sd(Prev_Str_Overall)                              0.12      0.03     0.07
sd(Prev_MF_Within)                                0.51      0.05     0.41
cor(Intercept,Prev_ER_count_cap51)               -0.05      0.31    -0.63
cor(Intercept,Prev_ER_count_cap52)               -0.02      0.28    -0.56
cor(Prev_ER_count_cap51,Prev_ER_count_cap52)     -0.00      0.33    -0.63
cor(Intercept,Prev_ER_count_cap53)               -0.02      0.31    -0.60
cor(Prev_ER_count_cap51,Prev_ER_count_cap53)      0.05      0.33    -0.59
cor(Prev_ER_count_cap52,Prev_ER_count_cap53)      0.03      0.33    -0.61
cor(Intercept,Prev_ER_count_cap54)                0.16      0.32    -0.49
cor(Prev_ER_count_cap51,Prev_ER_count_cap54)      0.05      0.33    -0.61
cor(Prev_ER_count_cap52,Prev_ER_count_cap54)     -0.02      0.34    -0.65
cor(Prev_ER_count_cap53,Prev_ER_count_cap54)      0.01      0.33    -0.62
cor(Intercept,Prev_ER_count_cap55P)              -0.15      0.30    -0.68
cor(Prev_ER_count_cap51,Prev_ER_count_cap55P)     0.03      0.33    -0.61
cor(Prev_ER_count_cap52,Prev_ER_count_cap55P)    -0.02      0.33    -0.64
cor(Prev_ER_count_cap53,Prev_ER_count_cap55P)     0.02      0.33    -0.61
cor(Prev_ER_count_cap54,Prev_ER_count_cap55P)    -0.02      0.33    -0.64
cor(Intercept,Prev_Str_Overall)                  -0.50      0.14    -0.75
cor(Prev_ER_count_cap51,Prev_Str_Overall)         0.06      0.32    -0.57
cor(Prev_ER_count_cap52,Prev_Str_Overall)        -0.10      0.32    -0.67
cor(Prev_ER_count_cap53,Prev_Str_Overall)         0.08      0.32    -0.55
cor(Prev_ER_count_cap54,Prev_Str_Overall)        -0.04      0.32    -0.64
cor(Prev_ER_count_cap55P,Prev_Str_Overall)        0.16      0.31    -0.48
cor(Intercept,Prev_MF_Within)                    -0.10      0.12    -0.34
cor(Prev_ER_count_cap51,Prev_MF_Within)          -0.10      0.31    -0.67
cor(Prev_ER_count_cap52,Prev_MF_Within)           0.19      0.31    -0.47
cor(Prev_ER_count_cap53,Prev_MF_Within)          -0.17      0.31    -0.71
cor(Prev_ER_count_cap54,Prev_MF_Within)          -0.11      0.32    -0.68
cor(Prev_ER_count_cap55P,Prev_MF_Within)         -0.06      0.30    -0.62
cor(Prev_Str_Overall,Prev_MF_Within)             -0.37      0.17    -0.67
                                              u-95% CI Rhat Bulk_ESS Tail_ESS
sd(Intercept)                                     2.19 1.00     3197     5839
sd(Prev_ER_count_cap51)                           0.32 1.00     5758     7525
sd(Prev_ER_count_cap52)                           0.57 1.00     3340     5547
sd(Prev_ER_count_cap53)                           0.74 1.00     4933     6853
sd(Prev_ER_count_cap54)                           1.07 1.00     5929     6603
sd(Prev_ER_count_cap55P)                          2.22 1.00     5137     7034
sd(Prev_Str_Overall)                              0.18 1.00     4441     6957
sd(Prev_MF_Within)                                0.62 1.00     7467     9579
cor(Intercept,Prev_ER_count_cap51)                0.56 1.00    22953     8733
cor(Intercept,Prev_ER_count_cap52)                0.54 1.00    22315     8976
cor(Prev_ER_count_cap51,Prev_ER_count_cap52)      0.63 1.00     9611     8420
cor(Intercept,Prev_ER_count_cap53)                0.58 1.00    21462     8759
cor(Prev_ER_count_cap51,Prev_ER_count_cap53)      0.65 1.00    11404     9813
cor(Prev_ER_count_cap52,Prev_ER_count_cap53)      0.66 1.00    10346     9985
cor(Intercept,Prev_ER_count_cap54)                0.72 1.00    18733     9225
cor(Prev_ER_count_cap51,Prev_ER_count_cap54)      0.65 1.00    13508     9611
cor(Prev_ER_count_cap52,Prev_ER_count_cap54)      0.62 1.00    15211    10417
cor(Prev_ER_count_cap53,Prev_ER_count_cap54)      0.64 1.00    12343    10523
cor(Intercept,Prev_ER_count_cap55P)               0.46 1.00    17503     9394
cor(Prev_ER_count_cap51,Prev_ER_count_cap55P)     0.65 1.00    10871    10042
cor(Prev_ER_count_cap52,Prev_ER_count_cap55P)     0.61 1.00    11876    10436
cor(Prev_ER_count_cap53,Prev_ER_count_cap55P)     0.64 1.00    10631    10027
cor(Prev_ER_count_cap54,Prev_ER_count_cap55P)     0.62 1.00     8921     9752
cor(Intercept,Prev_Str_Overall)                  -0.20 1.00     9565     9277
cor(Prev_ER_count_cap51,Prev_Str_Overall)         0.66 1.00     2403     5198
cor(Prev_ER_count_cap52,Prev_Str_Overall)         0.54 1.00     2674     5383
cor(Prev_ER_count_cap53,Prev_Str_Overall)         0.66 1.00     3118     7204
cor(Prev_ER_count_cap54,Prev_Str_Overall)         0.57 1.00     3753     6586
cor(Prev_ER_count_cap55P,Prev_Str_Overall)        0.72 1.00     4512     7491
cor(Intercept,Prev_MF_Within)                     0.14 1.00    14549     9820
cor(Prev_ER_count_cap51,Prev_MF_Within)           0.52 1.00     1058     2184
cor(Prev_ER_count_cap52,Prev_MF_Within)           0.71 1.00     1185     2746
cor(Prev_ER_count_cap53,Prev_MF_Within)           0.47 1.00     1583     3868
cor(Prev_ER_count_cap54,Prev_MF_Within)           0.55 1.00     1545     3535
cor(Prev_ER_count_cap55P,Prev_MF_Within)          0.54 1.00     2217     5480
cor(Prev_Str_Overall,Prev_MF_Within)             -0.01 1.00     5845     8091

Regression Coefficients:
                     Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS
Intercept[1]            -2.65      0.80    -4.24    -1.08 1.00     2197
Intercept[2]             0.22      0.80    -1.37     1.79 1.00     2198
Intercept[3]             2.52      0.80     0.92     4.09 1.00     2201
Intercept[4]             4.71      0.81     3.11     6.29 1.00     2217
Prev_ER_count_cap51     -0.02      0.08    -0.17     0.13 1.00    20543
Prev_ER_count_cap52      0.04      0.11    -0.17     0.26 1.00    17794
Prev_ER_count_cap53      0.04      0.17    -0.28     0.37 1.00    18273
Prev_ER_count_cap54     -0.17      0.27    -0.70     0.35 1.00    17504
Prev_ER_count_cap55P    -0.16      0.48    -1.17     0.73 1.00     9863
T1_Gender               -0.26      0.32    -0.89     0.37 1.00     2678
Age_2024                -0.03      0.03    -0.08     0.03 1.00     1964
Prev_Str_Overall         0.14      0.02     0.09     0.18 1.00     8864
Prev_MF_Within           0.90      0.06     0.78     1.02 1.00    18315
                     Tail_ESS
Intercept[1]             3923
Intercept[2]             3912
Intercept[3]             3751
Intercept[4]             3947
Prev_ER_count_cap51      9795
Prev_ER_count_cap52     10821
Prev_ER_count_cap53      9739
Prev_ER_count_cap54      9821
Prev_ER_count_cap55P     9250
T1_Gender                4512
Age_2024                 3655
Prev_Str_Overall         9614
Prev_MF_Within           8742

Further Distributional Parameters:
     Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
disc     1.00      0.00     1.00     1.00   NA       NA       NA

Draws were sampled using sample(hmc). For each parameter, Bulk_ESS
and Tail_ESS are effective sample size measures, and Rhat is the potential
scale reduction factor on split chains (at convergence, Rhat = 1).

exp(fixef(m_MF_ER_count_cap5_final_randomslopes1))

                         Estimate Est.Error        Q2.5       Q97.5
Intercept[1]           0.07034055  2.233387  0.01442507   0.3405447
Intercept[2]           1.24286643  2.231351  0.25516102   6.0027578
Intercept[3]          12.38159778  2.234983  2.50909379  59.8517098
Intercept[4]         111.02402729  2.247388 22.46655480 540.5730335
Prev_ER_count_cap51    0.97987455  1.078637  0.84428337   1.1354496
Prev_ER_count_cap52    1.04272474  1.115553  0.84082725   1.2912680
Prev_ER_count_cap53    1.04498912  1.179913  0.75526283   1.4438872
Prev_ER_count_cap54    0.84690239  1.309797  0.49467134   1.4175815
Prev_ER_count_cap55P   0.85093245  1.610258  0.30887643   2.0711439
T1_Gender              0.76723270  1.378679  0.40947786   1.4460299
Age_2024               0.97158773  1.028428  0.92006579   1.0262404
Prev_Str_Overall       1.14595445  1.023479  1.09579493   1.1998856
Prev_MF_Within         2.45803633  1.062589  2.18036910   2.7688270

# Robustness analysis: restricting the sample to participants with ≥10 valid pairs

# if (rerun) {
#   start_time <- Sys.time()
#   print(start_time)
#   m_MF_ER_count_cap5_final_10pairs <- brm(MF ~ Prev_ER_count_cap5 + T1_Gender + Age_2024 + Prev_Str_Overall 
#                                   + Prev_MF_Within + (1 | PID),
#                                   family = cumulative(link = "logit", threshold = "flexible"), data = d_10validpairs,
#                                   backend = "cmdstanr", chains = 4, iter = 4000, warmup = 1000, cores = 4)
#   saveRDS(m_MF_ER_count_cap5_final_10pairs, file = "m_MF_ER_count_cap5_final_10pairs.RDS")
#   end_time <- Sys.time()
#   print(end_time)
# }
m_MF_ER_count_cap5_final_10pairs <- readRDS("m_MF_ER_count_cap5_final_10pairs.RDS")
summary(m_MF_ER_count_cap5_final_10pairs)

 Family: cumulative 
  Links: mu = logit; disc = identity 
Formula: MF ~ Prev_ER_count_cap5 + T1_Gender + Age_2024 + Prev_Str_Overall + Prev_MF_Within + (1 | PID) 
   Data: d_10validpairs (Number of observations: 6344) 
  Draws: 4 chains, each with iter = 4000; warmup = 1000; thin = 1;
         total post-warmup draws = 12000

Multilevel Hyperparameters:
~PID (Number of levels: 140) 
              Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
sd(Intercept)     1.82      0.12     1.60     2.06 1.00     1391     2922

Regression Coefficients:
                     Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS
Intercept[1]            -2.40      0.86    -4.06    -0.69 1.00      883
Intercept[2]             0.40      0.86    -1.26     2.11 1.00      881
Intercept[3]             2.65      0.86     0.98     4.35 1.00      881
Intercept[4]             4.75      0.87     3.08     6.47 1.00      893
Prev_ER_count_cap51     -0.01      0.07    -0.16     0.13 1.00    12821
Prev_ER_count_cap52      0.06      0.10    -0.13     0.27 1.00    11609
Prev_ER_count_cap53      0.07      0.15    -0.22     0.37 1.00    13724
Prev_ER_count_cap54     -0.09      0.24    -0.56     0.37 1.00    14282
Prev_ER_count_cap55P     0.02      0.32    -0.61     0.65 1.00    18444
T1_Gender               -0.11      0.36    -0.80     0.62 1.00      911
Age_2024                -0.03      0.03    -0.09     0.03 1.00      818
Prev_Str_Overall         0.10      0.02     0.07     0.14 1.00     9314
Prev_MF_Within           0.96      0.04     0.89     1.03 1.00    16875
                     Tail_ESS
Intercept[1]             2070
Intercept[2]             2061
Intercept[3]             2034
Intercept[4]             2119
Prev_ER_count_cap51      9106
Prev_ER_count_cap52      9848
Prev_ER_count_cap53      9851
Prev_ER_count_cap54      9585
Prev_ER_count_cap55P     9259
T1_Gender                1968
Age_2024                 1864
Prev_Str_Overall         9538
Prev_MF_Within           9957

Further Distributional Parameters:
     Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
disc     1.00      0.00     1.00     1.00   NA       NA       NA

Draws were sampled using sample(hmc). For each parameter, Bulk_ESS
and Tail_ESS are effective sample size measures, and Rhat is the potential
scale reduction factor on split chains (at convergence, Rhat = 1).

exp(fixef(m_MF_ER_count_cap5_final_10pairs))

                         Estimate Est.Error        Q2.5       Q97.5
Intercept[1]           0.09063759  2.373130  0.01719791   0.5029263
Intercept[2]           1.49422641  2.370573  0.28267421   8.2113841
Intercept[3]          14.14516275  2.373149  2.66759449  77.2296731
Intercept[4]         115.46222902  2.383848 21.74627003 646.1826799
Prev_ER_count_cap51    0.98608656  1.075804  0.85274903   1.1371802
Prev_ER_count_cap52    1.06712962  1.108675  0.87410251   1.3105435
Prev_ER_count_cap53    1.07767412  1.160242  0.80481950   1.4427869
Prev_ER_count_cap54    0.91081302  1.266894  0.57211455   1.4549665
Prev_ER_count_cap55P   1.01996482  1.381430  0.54290624   1.9137751
T1_Gender              0.89873844  1.436186  0.44756338   1.8645092
Age_2024               0.97386541  1.030398  0.91820555   1.0330541
Prev_Str_Overall       1.10999627  1.018061  1.07148780   1.1493395
Prev_MF_Within         2.60751773  1.037873  2.42640024   2.8039903

p_values <- describe_posterior(m_MF_ER_count_cap5_final_10pairs, test = "p_direction")

##Posterior predictive checks on all core models

####Posterior predictive checks on all core models####
#Aim 1 - To explore whether emotion regulation strategies predict subsequent mental fatigue and stress
m_MF_ERall_final <- readRDS("m_MF_ERall_final.RDS")
pp_check(m_MF_ERall_final, ndraws = 100)

m_Stress_ERall_Cov4 <- readRDS("m_Stress_ERall_Cov4.RDS")
pp_check(m_Stress_ERall_Cov4, ndraws = 100)

#Aim 2 - To explore whether mental fatigue and stress predicts subsequent emotion regulation strategies
outcomesOCT <- c("ER1Dis", "ER2Rum", "ER3SBl", "ER4ExprS",
                 "ER5ExperS", "ER6Acc", "ER7Pla", "ER8Rea",
                 "ER9ESu", "ER10Rel")

pp_plots <- lapply(outcomesOCT, function(var) {
  pp_check(m_ERall_MF_final, resp = var, ndraws = 100) + 
    ggtitle(var)
})

# Combine all plots into a grid (2 rows of 5 plots each)
library(patchwork) 

Warning: package 'patchwork' was built under R version 4.4.3

Attaching package: 'patchwork'

The following object is masked from 'package:MASS':

    area

wrap_plots(pp_plots, ncol = 5)

#Aim 3 - To investigate whether the number of emotion regulation strategies implemented predicts mental fatigue
m_MF_ER_count_cap5_final <- readRDS("m_MF_ER_count_cap5_final.RDS")
pp_check(m_MF_ER_count_cap5_final, ndraws = 100)

8 Seed numbers and R version on all models

#R version 4.4.1

#Seed numbers
#Aim 1 - To explore whether emotion regulation strategies predict subsequent mental fatigue and stress
m_MF_ERall_final$fit@stan_args[[1]]$seed

[1] "4148485"

m_MF_ERall_time_diff_sensanalysis$fit@stan_args[[1]]$seed

[1] "1276220123"

m_MF_ERall_randomslopes2$fit@stan_args[[1]]$seed

[1] "1102371920"

m_MF_ERall_final_10pairs$fit@stan_args[[1]]$seed

[1] "1819845886"

m_Stress_ERall_Cov4$fit@stan_args[[1]]$seed

[1] "570379173"

m_Stress_ERall_Cov4_randomslopes1$fit@stan_args[[1]]$seed

[1] "421665739"

m_Stress_ERall_Cov4_10pairs$fit@stan_args[[1]]$seed

[1] "570359575"

#Aim 2 - To explore whether mental fatigue and stress predicts subsequent emotion regulation strategies.
m_ERall_MF_final$fit@stan_args[[1]]$seed

[1] "1839251176"

m_ERall_MF_Cov2_r2_time_diff_sensanalysis$fit@stan_args[[1]]$seed

[1] "1265947505"

m_ERall_MF_final_randomslopes1$fit@stan_args[[1]]$seed

[1] "2057777069"

m_ERall_MF_final_10pairs$fit@stan_args[[1]]$seed

[1] "235080770"

m_ERall_MF_Adj_final$fit@stan_args[[1]]$seed

[1] "2114610812"

#Aim 3 - To investigate whether the number of emotion regulation strategies implemented predicts mental fatigue
m_MF_ER_count_cap5_final$fit@stan_args[[1]]$seed

[1] "392502332"

m_MF_ER_count_cap5_final_randomslopes1$fit@stan_args[[1]]$seed

[1] "1686442558"

m_MF_ER_count_cap5_final_10pairs$fit@stan_args[[1]]$seed

[1] "2107907993"

m_MF_ER_count_cap5_mo$fit@stan_args[[1]]$seed

[1] "1018725013"