PA mixed-effects model using exploratory dataset

PA_exp.lmer <- lmer(bc_pa ~ outcome + PE + ( 1 + outcome + PE | id), data = PA_exp, REML = TRUE)
summary(PA_exp.lmer)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: bc_pa ~ outcome + PE + (1 + outcome + PE | id)
##    Data: PA_exp
## 
## REML criterion at convergence: 7453.9
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -6.2665 -0.5029  0.0391  0.5287  3.3075 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev. Corr       
##  id       (Intercept) 438.869  20.949              
##           outcome       5.645   2.376   -0.88      
##           PE          170.171  13.045    0.39 -0.54
##  Residual             109.869  10.482              
## Number of obs: 951, groups:  id, 59
## 
## Fixed effects:
##             Estimate Std. Error      df t value Pr(>|t|)    
## (Intercept)  -0.3912     3.0891 48.9762  -0.127 0.899749    
## outcome      -0.1640     0.3873 53.7472  -0.423 0.673688    
## PE            9.2324     2.1722 42.0323   4.250 0.000116 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##         (Intr) outcom
## outcome -0.874       
## PE       0.353 -0.499

NA mixed-effects model using exploratory dataset

NA_exp.lmer <- lmer(bc_na ~ outcome + PE + ( 1 + outcome + PE | id), data = NA_exp, REML = TRUE)
summary(NA_exp.lmer)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: bc_na ~ outcome + PE + (1 + outcome + PE | id)
##    Data: NA_exp
## 
## REML criterion at convergence: 7806.6
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -4.8653 -0.5519 -0.0554  0.5301  4.8687 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev. Corr       
##  id       (Intercept) 670.901  25.902              
##           outcome       6.063   2.462   -0.92      
##           PE          209.490  14.474    0.12 -0.33
##  Residual             169.548  13.021              
## Number of obs: 951, groups:  id, 59
## 
## Fixed effects:
##             Estimate Std. Error      df t value Pr(>|t|)    
## (Intercept)  24.1706     5.7278 27.5777   4.220 0.000238 ***
## outcome      -2.3422     0.6297 26.4361  -3.719 0.000949 ***
## PE            0.0586     2.5096 31.5525   0.023 0.981517    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##         (Intr) outcom
## outcome -0.957       
## PE       0.345 -0.472

PA mixed-effects model using confirmatory dataset

PA_con.lmer <- lmer(bc_pa ~ outcome + PE + ( 1 + outcome + PE | id), data = PA_con, REML = TRUE)
summary(PA_con.lmer)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: bc_pa ~ outcome + PE + (1 + outcome + PE | id)
##    Data: PA_con
## 
## REML criterion at convergence: 10742.6
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -4.6023 -0.5259  0.0218  0.5549  4.5331 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev. Corr       
##  id       (Intercept) 123.301  11.104              
##           outcome       2.053   1.433   -0.50      
##           PE           33.292   5.770    0.12  0.22
##  Residual             154.990  12.449              
## Number of obs: 1325, groups:  id, 69
## 
## Fixed effects:
##             Estimate Std. Error      df t value Pr(>|t|)  
## (Intercept)   2.8074     1.9552 50.6470   1.436   0.1572  
## outcome      -0.1670     0.2676 44.3735  -0.624   0.5357  
## PE            1.8323     0.8695 38.4965   2.107   0.0417 *
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##         (Intr) outcom
## outcome -0.699       
## PE       0.216  0.018

NA mixed-effects model using confirmatory dataset

NA_con.lmer <- lmer(bc_na ~ outcome + PE + ( 1 + outcome + PE | id), data = NA_con, REML = TRUE)
summary(NA_con.lmer)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: bc_na ~ outcome + PE + (1 + outcome + PE | id)
##    Data: NA_con
## 
## REML criterion at convergence: 10965.9
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.8251 -0.5327 -0.0026  0.4347  4.3983 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev. Corr       
##  id       (Intercept) 4239.92  65.115              
##           outcome       39.53   6.287   -0.99      
##           PE            76.44   8.743    0.90 -0.89
##  Residual              187.13  13.679              
## Number of obs: 1325, groups:  id, 69
## 
## Fixed effects:
##             Estimate Std. Error     df t value Pr(>|t|)
## (Intercept)   -4.476     10.671 54.858  -0.419    0.677
## outcome        0.675      1.049 55.619   0.644    0.523
## PE            -1.566      1.366 50.065  -1.147    0.257
## 
## Correlation of Fixed Effects:
##         (Intr) outcom
## outcome -0.991       
## PE       0.894 -0.876

Summary:

Prediction Error is still a significant predictor of PA in both the exploratory and confirmatory datsets, as the best fitting gamma value for PA is still 0.94. The new best-fitting gamma for NA is 0.99. With this new gamma, outcome is still a significant predictor of NA in the exploratory dataset. Neither outcome nor prediction error are significant predictors of NA in the confirmatory dataset.