Main Analyses:
Updating Analyses
Updating across exam is not stable. Updating at exam two is greater
than at either of the other two
## Family: gaussian
## Links: mu = identity
## Formula: Prediction_0_delta ~ PE0 + exam_num.f + (1 + PE0 | ID)
## Data: predictions[abs(predictions$PE0) < 30, ] (Number of observations: 1394)
## Draws: 4 chains, each with iter = 2000; warmup = 1000; thin = 1;
## total post-warmup draws = 4000
##
## Multilevel Hyperparameters:
## ~ID (Number of levels: 686)
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sd(Intercept) 1.45 0.50 0.34 2.37 1.00 885 927
## sd(PE0) 0.21 0.04 0.11 0.29 1.00 856 1269
## cor(Intercept,PE0) 0.87 0.17 0.38 1.00 1.00 652 713
##
## Regression Coefficients:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## Intercept -3.03 0.40 -3.82 -2.24 1.00 5163 3180
## PE0 0.07 0.03 0.02 0.12 1.00 5554 3338
## exam_num.f2 1.24 0.63 0.01 2.44 1.00 5532 3134
## exam_num.f3 -0.87 0.63 -2.13 0.33 1.00 6137 2951
##
## Further Distributional Parameters:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sigma 9.47 0.21 9.06 9.90 1.00 1995 2261
##
## Draws were sampled using sampling(NUTS). For each parameter, Bulk_ESS
## and Tail_ESS are effective sample size measures, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).
## Ignoring unknown labels:
## • fill : "NA"
## • colour : "NA"
## Ignoring unknown labels:
## • fill : "NA"
## • colour : "NA"
## Family: gaussian
## Links: mu = identity
## Formula: Prediction_1_delta ~ PE1 + exam_num.f + (1 + PE1 | ID)
## Data: predictions[abs(predictions$PE1) < 30, ] (Number of observations: 1609)
## Draws: 4 chains, each with iter = 2000; warmup = 1000; thin = 1;
## total post-warmup draws = 4000
##
## Multilevel Hyperparameters:
## ~ID (Number of levels: 703)
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sd(Intercept) 0.57 0.42 0.03 1.55 1.00 1391 1718
## sd(PE1) 0.26 0.09 0.04 0.41 1.01 483 489
## cor(Intercept,PE1) 0.24 0.54 -0.89 0.97 1.01 303 742
##
## Regression Coefficients:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## Intercept -6.92 0.64 -8.17 -5.66 1.00 6213 2876
## PE1 0.27 0.04 0.19 0.34 1.00 6376 3165
## exam_num.f2 5.75 0.95 3.86 7.64 1.00 6404 3301
## exam_num.f3 0.02 0.97 -1.87 1.90 1.00 5912 3193
##
## Further Distributional Parameters:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sigma 15.40 0.32 14.79 16.05 1.00 1407 2476
##
## Draws were sampled using sampling(NUTS). 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).
## Ignoring unknown labels:
## • fill : "NA"
## • colour : "NA"
## Ignoring unknown labels:
## • fill : "NA"
## • colour : "NA"
## Warning: There were 2 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: gaussian
## Links: mu = identity
## Formula: Prediction_2_delta ~ PE2 + exam_num.f + (1 + PE2 | ID)
## Data: predictions[abs(predictions$PE2) < 30, ] (Number of observations: 2160)
## Draws: 4 chains, each with iter = 2000; warmup = 1000; thin = 1;
## total post-warmup draws = 4000
##
## Multilevel Hyperparameters:
## ~ID (Number of levels: 781)
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sd(Intercept) 0.37 0.28 0.01 1.03 1.00 2732 1986
## sd(PE2) 0.09 0.06 0.00 0.22 1.00 933 1461
## cor(Intercept,PE2) -0.05 0.58 -0.96 0.95 1.00 1882 2206
##
## Regression Coefficients:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## Intercept -8.12 0.55 -9.21 -7.05 1.00 5108 3047
## PE2 0.17 0.03 0.11 0.23 1.00 6617 3264
## exam_num.f2 7.75 0.79 6.22 9.30 1.00 4002 2409
## exam_num.f3 0.41 0.79 -1.13 1.97 1.00 4488 3619
##
## Further Distributional Parameters:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sigma 15.28 0.24 14.84 15.76 1.00 4180 2145
##
## Draws were sampled using sampling(NUTS). For each parameter, Bulk_ESS
## and Tail_ESS are effective sample size measures, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).
## Ignoring unknown labels:
## • fill : "NA"
## • colour : "NA"
## Ignoring unknown labels:
## • fill : "NA"
## • colour : "NA"
Asymmetric Updating
Larger (slope for) updating for positive PE than negative
## Family: gaussian
## Links: mu = identity
## Formula: Prediction_0_delta ~ abs_PE0 * sign_PE0 + exam_num.f + (1 | ID)
## Data: predictions[abs(predictions$PE0) < 30, ] (Number of observations: 1394)
## Draws: 4 chains, each with iter = 2000; warmup = 1000; thin = 1;
## total post-warmup draws = 4000
##
## Multilevel Hyperparameters:
## ~ID (Number of levels: 686)
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sd(Intercept) 0.46 0.33 0.02 1.21 1.00 1405 1739
##
## Regression Coefficients:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## Intercept -3.93 0.66 -5.22 -2.62 1.00 3136 2831
## abs_PE0 0.01 0.05 -0.08 0.10 1.00 3230 2988
## sign_PE01 -0.07 0.87 -1.74 1.61 1.00 2499 2946
## exam_num.f2 1.20 0.62 0.00 2.47 1.00 4298 2451
## exam_num.f3 -0.86 0.64 -2.12 0.33 1.00 5063 2903
## abs_PE0:sign_PE01 0.20 0.08 0.05 0.35 1.00 2315 2656
##
## Further Distributional Parameters:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sigma 9.78 0.19 9.43 10.17 1.00 5648 2700
##
## Draws were sampled using sampling(NUTS). 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).
## Family: gaussian
## Links: mu = identity
## Formula: Prediction_1_delta ~ abs_PE1 * sign_PE1 + exam_num.f + (1 | ID)
## Data: predictions[abs(predictions$PE1) < 30, ] (Number of observations: 1609)
## Draws: 4 chains, each with iter = 2000; warmup = 1000; thin = 1;
## total post-warmup draws = 4000
##
## Multilevel Hyperparameters:
## ~ID (Number of levels: 703)
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sd(Intercept) 0.51 0.39 0.02 1.43 1.00 1986 2035
##
## Regression Coefficients:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## Intercept -8.58 0.98 -10.58 -6.67 1.00 3514 3385
## abs_PE1 -0.03 0.08 -0.18 0.12 1.00 3128 3102
## sign_PE11 -0.72 1.22 -3.12 1.69 1.00 2792 3022
## exam_num.f2 5.35 0.97 3.47 7.32 1.00 5189 3410
## exam_num.f3 -0.49 1.02 -2.50 1.53 1.00 4818 3020
## abs_PE1:sign_PE11 0.61 0.11 0.39 0.82 1.00 2556 3109
##
## Further Distributional Parameters:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sigma 15.59 0.27 15.07 16.14 1.00 5888 3061
##
## Draws were sampled using sampling(NUTS). For each parameter, Bulk_ESS
## and Tail_ESS are effective sample size measures, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).
## Family: gaussian
## Links: mu = identity
## Formula: Prediction_2_delta ~ abs_PE2 * sign_PE2 + exam_num.f + (1 | ID)
## Data: predictions[abs(predictions$PE2) < 30, ] (Number of observations: 2160)
## Draws: 4 chains, each with iter = 2000; warmup = 1000; thin = 1;
## total post-warmup draws = 4000
##
## Multilevel Hyperparameters:
## ~ID (Number of levels: 781)
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sd(Intercept) 0.38 0.29 0.01 1.09 1.00 2258 1526
##
## Regression Coefficients:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## Intercept -7.88 0.92 -9.65 -6.05 1.00 3240 2953
## abs_PE2 -0.13 0.07 -0.28 0.02 1.00 2722 3040
## sign_PE21 -1.75 1.08 -3.83 0.34 1.00 2627 2723
## exam_num.f2 7.60 0.81 5.99 9.19 1.00 5170 3110
## exam_num.f3 0.26 0.82 -1.34 1.89 1.00 5117 2670
## abs_PE2:sign_PE21 0.44 0.10 0.24 0.64 1.00 2262 2692
##
## Further Distributional Parameters:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sigma 15.30 0.24 14.85 15.78 1.00 7645 2453
##
## Draws were sampled using sampling(NUTS). 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).
Accuracy Violins
Accuracy Update, all PE’s trend negative – especially PE0
## `geom_smooth()` using formula = 'y ~ x'
Accuracy models
Accuracy decreases over tie regardless of prediction type
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Accuracy_0 ~ exam_num + (1 | ID)
## Data: predictions
##
## REML criterion at convergence: 19299.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.5637 -0.4239 0.1767 0.6063 2.3779
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 42.6 6.527
## Residual 83.9 9.160
## Number of obs: 2549, groups: ID, 885
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 90.5184 0.4827 2519.8561 187.529 <2e-16 ***
## exam_num -1.5457 0.1726 1913.4484 -8.954 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## exam_num -0.799
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Accuracy_1 ~ exam_num + (1 | ID)
## Data: predictions
##
## REML criterion at convergence: 20669.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -6.4079 -0.4285 0.1878 0.6232 2.7914
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 26.45 5.143
## Residual 73.07 8.548
## Number of obs: 2806, groups: ID, 887
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 91.4603 0.4216 2794.1272 216.911 < 2e-16 ***
## exam_num -1.0416 0.1479 2109.6067 -7.044 2.53e-12 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## exam_num -0.823
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Accuracy_2 ~ exam_num + (1 | ID)
## Data: predictions
##
## REML criterion at convergence: 23308.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -9.8771 -0.3655 0.1708 0.5379 4.4105
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 30.97 5.565
## Residual 53.51 7.315
## Number of obs: 3271, groups: ID, 896
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 91.9415 0.3616 3016.1474 254.291 < 2e-16 ***
## exam_num -0.8792 0.1165 2441.7399 -7.549 6.15e-14 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## exam_num -0.778
Neuroticism Analyses
PE x NE
- High NE doesn’t update from PE 0
- NE predicts prediction 1 updating, no interaction
- NE predicts prediction 2 updating, no interaction
## Family: gaussian
## Links: mu = identity
## Formula: Prediction_0_delta ~ PE0 * negative_emotionality + exam_num.f + (1 | ID)
## Data: predictions[abs(predictions$PE0) < 30, ] (Number of observations: 1387)
## Draws: 4 chains, each with iter = 2000; warmup = 1000; thin = 1;
## total post-warmup draws = 4000
##
## Multilevel Hyperparameters:
## ~ID (Number of levels: 683)
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sd(Intercept) 0.43 0.32 0.01 1.17 1.00 2339 1929
##
## Regression Coefficients:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS
## Intercept -1.23 0.98 -3.18 0.68 1.00 9004
## PE0 0.15 0.08 -0.01 0.30 1.00 7468
## negative_emotionality -0.10 0.05 -0.20 0.00 1.00 8686
## exam_num.f2 1.15 0.63 -0.12 2.37 1.00 7856
## exam_num.f3 -0.88 0.64 -2.15 0.39 1.00 8235
## PE0:negative_emotionality -0.00 0.00 -0.01 0.00 1.00 7130
## Tail_ESS
## Intercept 2973
## PE0 3208
## negative_emotionality 2898
## exam_num.f2 3105
## exam_num.f3 3042
## PE0:negative_emotionality 2978
##
## Further Distributional Parameters:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sigma 9.81 0.19 9.45 10.17 1.00 10005 2779
##
## Draws were sampled using sampling(NUTS). For each parameter, Bulk_ESS
## and Tail_ESS are effective sample size measures, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).
## Family: gaussian
## Links: mu = identity
## Formula: Prediction_1_delta ~ PE1 * negative_emotionality + exam_num.f + (1 | ID)
## Data: predictions[abs(predictions$PE1) < 30, ] (Number of observations: 1597)
## Draws: 4 chains, each with iter = 2000; warmup = 1000; thin = 1;
## total post-warmup draws = 4000
##
## Multilevel Hyperparameters:
## ~ID (Number of levels: 698)
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sd(Intercept) 0.49 0.37 0.02 1.36 1.00 2566 1880
##
## Regression Coefficients:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS
## Intercept -1.59 1.40 -4.35 1.09 1.00 10348
## PE1 0.22 0.12 -0.02 0.46 1.00 8362
## negative_emotionality -0.31 0.07 -0.46 -0.17 1.00 10219
## exam_num.f2 5.68 0.92 3.82 7.44 1.00 7661
## exam_num.f3 -0.05 0.94 -1.88 1.81 1.00 7041
## PE1:negative_emotionality 0.00 0.01 -0.01 0.02 1.00 8136
## Tail_ESS
## Intercept 2713
## PE1 2656
## negative_emotionality 2819
## exam_num.f2 3323
## exam_num.f3 3307
## PE1:negative_emotionality 2901
##
## Further Distributional Parameters:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sigma 15.65 0.27 15.15 16.19 1.00 8669 2991
##
## Draws were sampled using sampling(NUTS). 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).
## Family: gaussian
## Links: mu = identity
## Formula: Prediction_2_delta ~ PE2 * negative_emotionality + exam_num.f + (1 | ID)
## Data: predictions[abs(predictions$PE2) < 30, ] (Number of observations: 2145)
## Draws: 4 chains, each with iter = 2000; warmup = 1000; thin = 1;
## total post-warmup draws = 4000
##
## Multilevel Hyperparameters:
## ~ID (Number of levels: 776)
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sd(Intercept) 0.38 0.29 0.02 1.07 1.00 2909 2483
##
## Regression Coefficients:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS
## Intercept -5.54 1.21 -7.93 -3.08 1.00 6629
## PE2 0.15 0.10 -0.05 0.35 1.00 5729
## negative_emotionality -0.15 0.07 -0.29 -0.03 1.00 6690
## exam_num.f2 7.73 0.79 6.18 9.27 1.00 6784
## exam_num.f3 0.32 0.79 -1.24 1.87 1.00 5995
## PE2:negative_emotionality 0.00 0.01 -0.01 0.01 1.00 5584
## Tail_ESS
## Intercept 2670
## PE2 2743
## negative_emotionality 2606
## exam_num.f2 3210
## exam_num.f3 2939
## PE2:negative_emotionality 3346
##
## Further Distributional Parameters:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sigma 15.33 0.24 14.87 15.81 1.00 7260 2800
##
## Draws were sampled using sampling(NUTS). For each parameter, Bulk_ESS
## and Tail_ESS are effective sample size measures, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).
NE x Updating Asymmetric
- PE0 and NE have no bearing on prediction 0 updating
- NE predicts prediction 1 updating (negatively), the interaction of
|PE1| and sign_PE1 predicts prediction 1 updating (+PE -> postive
updating)
- PE2 and NE have no bearing on prediction 2 updating
## Family: gaussian
## Links: mu = identity
## Formula: Prediction_0_delta ~ abs_PE0 * sign_PE0 * negative_emotionality + (1 | ID)
## Data: predictions[abs(predictions$PE0) < 30, ] (Number of observations: 1387)
## Draws: 4 chains, each with iter = 2000; warmup = 1000; thin = 1;
## total post-warmup draws = 4000
##
## Multilevel Hyperparameters:
## ~ID (Number of levels: 683)
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sd(Intercept) 0.43 0.33 0.02 1.21 1.00 2097 1996
##
## Regression Coefficients:
## Estimate Est.Error l-95% CI u-95% CI
## Intercept -1.90 1.96 -5.73 1.86
## abs_PE0 -0.14 0.15 -0.43 0.15
## sign_PE01 5.01 3.03 -0.81 11.03
## negative_emotionality -0.12 0.12 -0.34 0.10
## abs_PE0:sign_PE01 -0.10 0.29 -0.67 0.47
## abs_PE0:negative_emotionality 0.01 0.01 -0.01 0.03
## sign_PE01:negative_emotionality -0.29 0.17 -0.64 0.04
## abs_PE0:sign_PE01:negative_emotionality 0.02 0.02 -0.02 0.05
## Rhat Bulk_ESS Tail_ESS
## Intercept 1.00 2836 3258
## abs_PE0 1.00 2818 3111
## sign_PE01 1.00 2592 3011
## negative_emotionality 1.00 2481 2996
## abs_PE0:sign_PE01 1.00 2741 3031
## abs_PE0:negative_emotionality 1.00 2430 3128
## sign_PE01:negative_emotionality 1.00 2547 2964
## abs_PE0:sign_PE01:negative_emotionality 1.00 2605 2852
##
## Further Distributional Parameters:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sigma 9.78 0.19 9.42 10.17 1.00 10319 2329
##
## Draws were sampled using sampling(NUTS). 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).
## Family: gaussian
## Links: mu = identity
## Formula: Prediction_1_delta ~ abs_PE1 * sign_PE1 * negative_emotionality + (1 | ID)
## Data: predictions[abs(predictions$PE1) < 30, ] (Number of observations: 1597)
## Draws: 4 chains, each with iter = 2000; warmup = 1000; thin = 1;
## total post-warmup draws = 4000
##
## Multilevel Hyperparameters:
## ~ID (Number of levels: 698)
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sd(Intercept) 0.51 0.39 0.02 1.44 1.00 2686 2270
##
## Regression Coefficients:
## Estimate Est.Error l-95% CI u-95% CI
## Intercept 2.10 2.95 -3.71 7.99
## abs_PE1 -0.37 0.26 -0.88 0.13
## sign_PE11 -4.54 4.32 -12.94 3.97
## negative_emotionality -0.56 0.17 -0.91 -0.22
## abs_PE1:sign_PE11 0.96 0.42 0.16 1.78
## abs_PE1:negative_emotionality 0.02 0.02 -0.01 0.05
## sign_PE11:negative_emotionality 0.23 0.25 -0.26 0.71
## abs_PE1:sign_PE11:negative_emotionality -0.02 0.02 -0.07 0.02
## Rhat Bulk_ESS Tail_ESS
## Intercept 1.00 2548 2877
## abs_PE1 1.00 2555 2947
## sign_PE11 1.00 2398 2586
## negative_emotionality 1.00 2505 3018
## abs_PE1:sign_PE11 1.00 2380 2828
## abs_PE1:negative_emotionality 1.00 2542 2825
## sign_PE11:negative_emotionality 1.00 2349 2449
## abs_PE1:sign_PE11:negative_emotionality 1.00 2276 2649
##
## Further Distributional Parameters:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sigma 15.75 0.29 15.18 16.33 1.00 10660 2521
##
## Draws were sampled using sampling(NUTS). For each parameter, Bulk_ESS
## and Tail_ESS are effective sample size measures, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).
## Family: gaussian
## Links: mu = identity
## Formula: Prediction_2_delta ~ abs_PE2 * sign_PE2 * negative_emotionality + (1 | ID)
## Data: predictions[abs(predictions$PE2) < 30, ] (Number of observations: 2145)
## Draws: 4 chains, each with iter = 2000; warmup = 1000; thin = 1;
## total post-warmup draws = 4000
##
## Multilevel Hyperparameters:
## ~ID (Number of levels: 776)
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sd(Intercept) 0.38 0.30 0.01 1.09 1.00 2768 1824
##
## Regression Coefficients:
## Estimate Est.Error l-95% CI u-95% CI
## Intercept -0.97 2.67 -6.01 4.33
## abs_PE2 -0.27 0.24 -0.72 0.21
## sign_PE21 -3.90 3.62 -10.98 3.11
## negative_emotionality -0.26 0.16 -0.57 0.04
## abs_PE2:sign_PE21 0.57 0.33 -0.08 1.22
## abs_PE2:negative_emotionality 0.01 0.01 -0.02 0.04
## sign_PE21:negative_emotionality 0.12 0.21 -0.29 0.54
## abs_PE2:sign_PE21:negative_emotionality -0.01 0.02 -0.05 0.03
## Rhat Bulk_ESS Tail_ESS
## Intercept 1.00 2140 2794
## abs_PE2 1.00 2051 2508
## sign_PE21 1.00 1798 2423
## negative_emotionality 1.00 1935 2471
## abs_PE2:sign_PE21 1.00 1840 2314
## abs_PE2:negative_emotionality 1.00 1916 2747
## sign_PE21:negative_emotionality 1.00 1640 2263
## abs_PE2:sign_PE21:negative_emotionality 1.00 1719 2179
##
## Further Distributional Parameters:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sigma 15.72 0.24 15.25 16.18 1.00 10568 2583
##
## Draws were sampled using sampling(NUTS). 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).
Accruacy x NE
When looking at top and bottom quartile
- Accuracy 0 has no bearing on prediction updating
- Accuracy 1 interacts with high NE. High NE and greater accuracy
predicts prediction 1 updating (negatively)
- Accuracy 2 predicts prediction 2 updating (negatively)
## Family: gaussian
## Links: mu = identity
## Formula: Prediction_0_delta ~ Accuracy_0 * negative_emotionality + exam_num.f + (1 | ID)
## Data: predictions[abs(predictions$PE0) < 30, ] (Number of observations: 1387)
## Draws: 4 chains, each with iter = 2000; warmup = 1000; thin = 1;
## total post-warmup draws = 4000
##
## Multilevel Hyperparameters:
## ~ID (Number of levels: 683)
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sd(Intercept) 0.40 0.31 0.01 1.13 1.00 1501 1705
##
## Regression Coefficients:
## Estimate Est.Error l-95% CI u-95% CI Rhat
## Intercept -26.22 11.32 -48.42 -3.97 1.00
## Accuracy_0 0.27 0.12 0.02 0.52 1.00
## negative_emotionality 1.70 0.65 0.46 2.97 1.00
## exam_num.f2 0.89 0.63 -0.34 2.13 1.00
## exam_num.f3 -0.84 0.62 -2.05 0.35 1.00
## Accuracy_0:negative_emotionality -0.02 0.01 -0.03 -0.01 1.00
## Bulk_ESS Tail_ESS
## Intercept 1968 2540
## Accuracy_0 1950 2483
## negative_emotionality 1894 2553
## exam_num.f2 5175 3337
## exam_num.f3 4894 3334
## Accuracy_0:negative_emotionality 1873 2504
##
## Further Distributional Parameters:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sigma 9.81 0.19 9.45 10.19 1.00 5939 2746
##
## Draws were sampled using sampling(NUTS). For each parameter, Bulk_ESS
## and Tail_ESS are effective sample size measures, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).
## Family: gaussian
## Links: mu = identity
## Formula: Prediction_1_delta ~ Accuracy_1 * negative_emotionality + exam_num.f + (1 | ID)
## Data: predictions[abs(predictions$PE1) < 30, ] (Number of observations: 1597)
## Draws: 4 chains, each with iter = 2000; warmup = 1000; thin = 1;
## total post-warmup draws = 4000
##
## Multilevel Hyperparameters:
## ~ID (Number of levels: 698)
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sd(Intercept) 0.47 0.35 0.02 1.31 1.00 2494 1907
##
## Regression Coefficients:
## Estimate Est.Error l-95% CI u-95% CI Rhat
## Intercept -34.70 18.37 -70.72 2.07 1.00
## Accuracy_1 0.35 0.20 -0.05 0.74 1.00
## negative_emotionality 2.92 1.03 0.88 4.94 1.00
## exam_num.f2 5.22 0.96 3.37 7.16 1.00
## exam_num.f3 -0.17 0.96 -2.05 1.68 1.00
## Accuracy_1:negative_emotionality -0.03 0.01 -0.06 -0.01 1.00
## Bulk_ESS Tail_ESS
## Intercept 2247 2385
## Accuracy_1 2236 2253
## negative_emotionality 2217 2464
## exam_num.f2 5130 3168
## exam_num.f3 4580 3259
## Accuracy_1:negative_emotionality 2194 2463
##
## Further Distributional Parameters:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sigma 15.84 0.28 15.32 16.41 1.00 5871 3018
##
## Draws were sampled using sampling(NUTS). 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).
## Family: gaussian
## Links: mu = identity
## Formula: Prediction_2_delta ~ Accuracy_2 * negative_emotionality + exam_num.f + (1 | ID)
## Data: predictions[abs(predictions$PE2) < 30, ] (Number of observations: 2145)
## Draws: 4 chains, each with iter = 2000; warmup = 1000; thin = 1;
## total post-warmup draws = 4000
##
## Multilevel Hyperparameters:
## ~ID (Number of levels: 776)
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sd(Intercept) 0.37 0.28 0.01 1.04 1.00 1979 1660
##
## Regression Coefficients:
## Estimate Est.Error l-95% CI u-95% CI Rhat
## Intercept -13.07 15.14 -41.75 17.35 1.00
## Accuracy_2 0.08 0.16 -0.25 0.39 1.00
## negative_emotionality 1.09 0.86 -0.63 2.72 1.00
## exam_num.f2 7.50 0.80 5.91 9.08 1.00
## exam_num.f3 0.36 0.82 -1.23 1.94 1.00
## Accuracy_2:negative_emotionality -0.01 0.01 -0.03 0.01 1.00
## Bulk_ESS Tail_ESS
## Intercept 1451 2089
## Accuracy_2 1447 2087
## negative_emotionality 1441 2044
## exam_num.f2 4123 3041
## exam_num.f3 3760 2800
## Accuracy_2:negative_emotionality 1441 2117
##
## Further Distributional Parameters:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sigma 15.41 0.23 14.97 15.85 1.00 5006 3254
##
## Draws were sampled using sampling(NUTS). For each parameter, Bulk_ESS
## and Tail_ESS are effective sample size measures, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).
Splines PE x NE
## Family: gaussian
## Links: mu = identity
## Formula: Prediction_0_delta ~ t2(PE0, negative_emotionality) + exam_num.f + (1 | ID)
## Data: predictions[predictions$abs_PE0 < 30, ] (Number of observations: 1387)
## Draws: 4 chains, each with iter = 2000; warmup = 1000; thin = 1;
## total post-warmup draws = 4000
##
## Smoothing Spline Hyperparameters:
## Estimate Est.Error l-95% CI u-95% CI Rhat
## sds(t2PE0negative_emotionality_1) 3.60 3.14 0.17 11.54 1.00
## sds(t2PE0negative_emotionality_2) 4.26 3.80 0.18 13.95 1.00
## sds(t2PE0negative_emotionality_3) 14.05 7.52 2.46 32.77 1.00
## Bulk_ESS Tail_ESS
## sds(t2PE0negative_emotionality_1) 3423 3220
## sds(t2PE0negative_emotionality_2) 3487 2546
## sds(t2PE0negative_emotionality_3) 1761 1489
##
## Multilevel Hyperparameters:
## ~ID (Number of levels: 683)
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sd(Intercept) 0.44 0.32 0.02 1.17 1.00 1746 2195
##
## Regression Coefficients:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS
## Intercept -2.15 0.64 -3.44 -0.92 1.00 3279
## exam_num.f2 1.04 0.63 -0.20 2.27 1.00 6485
## exam_num.f3 -0.98 0.62 -2.17 0.24 1.00 6772
## t2PE0negative_emotionality_1 0.29 0.51 -0.74 1.22 1.00 3500
## t2PE0negative_emotionality_2 -1.41 0.48 -2.37 -0.53 1.00 4221
## t2PE0negative_emotionality_3 -0.07 0.69 -1.34 1.39 1.00 3240
## Tail_ESS
## Intercept 3227
## exam_num.f2 3286
## exam_num.f3 3283
## t2PE0negative_emotionality_1 3341
## t2PE0negative_emotionality_2 3138
## t2PE0negative_emotionality_3 3336
##
## Further Distributional Parameters:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sigma 9.75 0.19 9.40 10.12 1.00 7377 2839
##
## Draws were sampled using sampling(NUTS). 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).
## Family: gaussian
## Links: mu = identity
## Formula: Prediction_1_delta ~ t2(PE1, negative_emotionality) + exam_num.f + (1 | ID)
## Data: predictions[predictions$abs_PE1 < 30, ] (Number of observations: 1597)
## Draws: 4 chains, each with iter = 2000; warmup = 1000; thin = 1;
## total post-warmup draws = 4000
##
## Smoothing Spline Hyperparameters:
## Estimate Est.Error l-95% CI u-95% CI Rhat
## sds(t2PE1negative_emotionality_1) 6.72 5.74 0.26 21.14 1.00
## sds(t2PE1negative_emotionality_2) 7.56 6.72 0.25 24.37 1.00
## sds(t2PE1negative_emotionality_3) 22.78 10.33 8.22 47.78 1.00
## Bulk_ESS Tail_ESS
## sds(t2PE1negative_emotionality_1) 3199 2990
## sds(t2PE1negative_emotionality_2) 3150 2161
## sds(t2PE1negative_emotionality_3) 3143 2075
##
## Multilevel Hyperparameters:
## ~ID (Number of levels: 698)
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sd(Intercept) 0.51 0.39 0.02 1.45 1.00 2427 2231
##
## Regression Coefficients:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS
## Intercept -4.94 0.98 -6.90 -2.98 1.00 5171
## exam_num.f2 5.29 0.95 3.42 7.12 1.00 6601
## exam_num.f3 -0.60 0.96 -2.47 1.19 1.00 7309
## t2PE1negative_emotionality_1 1.63 0.80 0.03 3.15 1.00 4673
## t2PE1negative_emotionality_2 -3.43 0.73 -4.83 -1.93 1.00 5250
## t2PE1negative_emotionality_3 -0.51 1.01 -2.54 1.41 1.00 5095
## Tail_ESS
## Intercept 2912
## exam_num.f2 2962
## exam_num.f3 2951
## t2PE1negative_emotionality_1 3564
## t2PE1negative_emotionality_2 3453
## t2PE1negative_emotionality_3 3092
##
## Further Distributional Parameters:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sigma 15.54 0.28 15.01 16.10 1.00 9946 2661
##
## Draws were sampled using sampling(NUTS). For each parameter, Bulk_ESS
## and Tail_ESS are effective sample size measures, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).
## Family: gaussian
## Links: mu = identity
## Formula: Prediction_2_delta ~ t2(PE2, negative_emotionality) + exam_num.f + (1 | ID)
## Data: predictions[predictions$abs_PE2 < 30, ] (Number of observations: 2145)
## Draws: 4 chains, each with iter = 2000; warmup = 1000; thin = 1;
## total post-warmup draws = 4000
##
## Smoothing Spline Hyperparameters:
## Estimate Est.Error l-95% CI u-95% CI Rhat
## sds(t2PE2negative_emotionality_1) 7.00 6.16 0.25 22.45 1.00
## sds(t2PE2negative_emotionality_2) 6.92 6.45 0.20 23.39 1.00
## sds(t2PE2negative_emotionality_3) 12.42 8.73 0.65 33.45 1.00
## Bulk_ESS Tail_ESS
## sds(t2PE2negative_emotionality_1) 2733 2943
## sds(t2PE2negative_emotionality_2) 3122 2316
## sds(t2PE2negative_emotionality_3) 2241 1899
##
## Multilevel Hyperparameters:
## ~ID (Number of levels: 776)
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sd(Intercept) 0.39 0.28 0.02 1.05 1.00 2632 1764
##
## Regression Coefficients:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS
## Intercept -7.10 0.77 -8.57 -5.56 1.00 4427
## exam_num.f2 7.66 0.78 6.11 9.17 1.00 6430
## exam_num.f3 0.23 0.81 -1.39 1.80 1.00 7179
## t2PE2negative_emotionality_1 0.85 0.56 -0.21 1.94 1.00 4640
## t2PE2negative_emotionality_2 2.15 0.51 1.14 3.16 1.00 5287
## t2PE2negative_emotionality_3 0.21 0.74 -1.22 1.76 1.00 5142
## Tail_ESS
## Intercept 3109
## exam_num.f2 3440
## exam_num.f3 3275
## t2PE2negative_emotionality_1 2798
## t2PE2negative_emotionality_2 3196
## t2PE2negative_emotionality_3 3142
##
## Further Distributional Parameters:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sigma 15.32 0.23 14.86 15.79 1.00 9370 2992
##
## Draws were sampled using sampling(NUTS). 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).
EVPE
Distribution
## Warning: Removed 169 rows containing non-finite outside the scale range
## (`stat_density()`).
EVPE Predicts Updating?
- EVPE and PE0 predicts prediction 0 updating
- EVPE and PE1 predicts prediction 1 updating
- EVPE and PE2 predicts prediction 2 updating
## Warning: There were 9 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: gaussian
## Links: mu = identity
## Formula: Prediction_0_delta ~ EVPE + PE0 + exam_num.f + (1 | cohort) + (1 | ID)
## Data: predictions[abs(predictions$PE0) < 30, ] (Number of observations: 1394)
## Draws: 4 chains, each with iter = 2000; warmup = 1000; thin = 1;
## total post-warmup draws = 4000
##
## Multilevel Hyperparameters:
## ~cohort (Number of levels: 4)
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sd(Intercept) 1.48 1.03 0.28 4.35 1.00 1499 1802
##
## ~ID (Number of levels: 686)
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sd(Intercept) 0.67 0.48 0.02 1.74 1.00 1248 1794
##
## Regression Coefficients:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## Intercept -1.20 0.92 -3.12 0.70 1.00 2743 2469
## EVPE 0.21 0.03 0.16 0.26 1.00 4671 2358
## PE0 0.22 0.03 0.16 0.28 1.00 5719 2872
## exam_num.f2 2.36 0.67 1.04 3.67 1.00 6680 3269
## exam_num.f3 -0.64 0.66 -1.90 0.64 1.00 6922 3401
##
## Further Distributional Parameters:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sigma 9.55 0.18 9.21 9.92 1.00 6090 2254
##
## Draws were sampled using sampling(NUTS). For each parameter, Bulk_ESS
## and Tail_ESS are effective sample size measures, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).
## Ignoring unknown labels:
## • fill : "NA"
## • colour : "NA"
## Ignoring unknown labels:
## • fill : "NA"
## • colour : "NA"
## Ignoring unknown labels:
## • fill : "NA"
## • colour : "NA"
## Ignoring unknown labels:
## • fill : "NA"
## • colour : "NA"
## Warning: There were 7 divergent transitions after warmup. Increasing
## adapt_delta above 0.8 may help. See
## http://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup
## Family: gaussian
## Links: mu = identity
## Formula: Prediction_1_delta ~ EVPE + PE1 + exam_num.f + (1 | cohort) + (1 | ID)
## Data: predictions[abs(predictions$PE1) < 30, ] (Number of observations: 1609)
## Draws: 4 chains, each with iter = 2000; warmup = 1000; thin = 1;
## total post-warmup draws = 4000
##
## Multilevel Hyperparameters:
## ~cohort (Number of levels: 4)
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sd(Intercept) 2.18 1.52 0.38 6.38 1.01 1195 1234
##
## ~ID (Number of levels: 703)
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sd(Intercept) 0.73 0.55 0.03 2.00 1.00 1774 2216
##
## Regression Coefficients:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## Intercept -4.21 1.34 -7.00 -1.45 1.00 2778 2067
## EVPE 0.38 0.03 0.32 0.45 1.00 7193 3114
## PE1 0.48 0.04 0.40 0.55 1.00 6091 3125
## exam_num.f2 6.89 0.90 5.17 8.67 1.00 7553 2618
## exam_num.f3 0.14 0.93 -1.68 1.94 1.00 7295 3041
##
## Further Distributional Parameters:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sigma 15.00 0.26 14.49 15.52 1.00 7762 2432
##
## Draws were sampled using sampling(NUTS). 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).
## Ignoring unknown labels:
## • fill : "NA"
## • colour : "NA"
## Ignoring unknown labels:
## • fill : "NA"
## • colour : "NA"
## Ignoring unknown labels:
## • fill : "NA"
## • colour : "NA"
## Ignoring unknown labels:
## • fill : "NA"
## • colour : "NA"
## Warning: There were 7 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: gaussian
## Links: mu = identity
## Formula: Prediction_2_delta ~ EVPE + PE2 + exam_num.f + (1 | cohort) + (1 | ID)
## Data: predictions[abs(predictions$PE2) < 30, ] (Number of observations: 2160)
## Draws: 4 chains, each with iter = 2000; warmup = 1000; thin = 1;
## total post-warmup draws = 4000
##
## Multilevel Hyperparameters:
## ~cohort (Number of levels: 4)
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sd(Intercept) 4.51 2.58 1.65 11.51 1.01 1038 1036
##
## ~ID (Number of levels: 781)
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sd(Intercept) 0.42 0.31 0.02 1.18 1.00 1901 1585
##
## Regression Coefficients:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## Intercept -6.74 2.35 -11.60 -1.75 1.00 952 1052
## EVPE 0.28 0.03 0.23 0.33 1.00 4071 3408
## PE2 0.34 0.03 0.28 0.41 1.00 3677 2885
## exam_num.f2 8.33 0.77 6.83 9.81 1.00 4491 3121
## exam_num.f3 0.21 0.77 -1.28 1.75 1.00 4262 3188
##
## Further Distributional Parameters:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sigma 14.76 0.23 14.33 15.21 1.00 4805 2626
##
## Draws were sampled using sampling(NUTS). 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).
## Ignoring unknown labels:
## • fill : "NA"
## • colour : "NA"
## Ignoring unknown labels:
## • fill : "NA"
## • colour : "NA"
## Ignoring unknown labels:
## • fill : "NA"
## • colour : "NA"
## Ignoring unknown labels:
## • fill : "NA"
## • colour : "NA"
EVPE Predicts Updating with Asymmetry
Prediction 0 Updating: - |EVPE| predicts updating negatively - |PE0|
predicts updating negatively - Interaction of: - |EVPE| and sign_EVPE
predicts updating positively - |PE0| and sign_PE0 predicts updating
positively
Prediction 1 Updating: - |EVPE| predicts updating negatively - |PE1|
predicts updating negatively - Interaction of: - |EVPE| and sign_EVPE
predicts updating positively - |PE1| and sign_PE1 predicts updating
positively
Prediction21 Updating: - |EVPE| predicts updating negatively - |PE2|
predicts updating negatively - Interaction of: - |EVPE| and sign_EVPE
predicts updating positively - |PE2| and sign_PE2 predicts updating
positively
## Warning: There were 10 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: gaussian
## Links: mu = identity
## Formula: Prediction_0_delta ~ EVPE_abs * EVPE_sign + abs_PE0 * sign_PE0 + exam_num.f + (1 | cohort) + (1 | ID)
## Data: predictions[abs(predictions$PE0) < 30, ] (Number of observations: 1394)
## Draws: 4 chains, each with iter = 2000; warmup = 1000; thin = 1;
## total post-warmup draws = 4000
##
## Multilevel Hyperparameters:
## ~cohort (Number of levels: 4)
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sd(Intercept) 1.30 0.99 0.14 4.07 1.00 1283 1372
##
## ~ID (Number of levels: 686)
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sd(Intercept) 0.65 0.48 0.03 1.79 1.00 1139 1682
##
## Regression Coefficients:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## Intercept -2.75 1.11 -5.00 -0.40 1.00 2450 1638
## EVPE_abs -0.08 0.04 -0.16 0.01 1.00 3903 3001
## EVPE_sign1 0.30 1.00 -1.64 2.21 1.00 4359 2648
## abs_PE0 -0.21 0.05 -0.32 -0.11 1.00 3538 2447
## sign_PE01 -0.28 0.87 -1.96 1.43 1.00 4166 2916
## exam_num.f2 1.92 0.68 0.61 3.25 1.00 5087 2710
## exam_num.f3 -0.87 0.64 -2.12 0.37 1.00 6102 3161
## EVPE_abs:EVPE_sign1 0.46 0.08 0.30 0.61 1.00 3258 2838
## abs_PE0:sign_PE01 0.42 0.09 0.26 0.60 1.00 3202 2637
##
## Further Distributional Parameters:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sigma 9.51 0.19 9.15 9.87 1.00 4981 2472
##
## Draws were sampled using sampling(NUTS). 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).
## Warning: There were 14 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: gaussian
## Links: mu = identity
## Formula: Prediction_1_delta ~ EVPE_abs * EVPE_sign + abs_PE1 * sign_PE1 + exam_num.f + (1 | cohort) + (1 | ID)
## Data: predictions[abs(predictions$PE1) < 30, ] (Number of observations: 1609)
## Draws: 4 chains, each with iter = 2000; warmup = 1000; thin = 1;
## total post-warmup draws = 4000
##
## Multilevel Hyperparameters:
## ~cohort (Number of levels: 4)
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sd(Intercept) 1.98 1.58 0.23 6.52 1.00 1076 1057
##
## ~ID (Number of levels: 703)
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sd(Intercept) 0.70 0.52 0.03 1.91 1.00 1860 2078
##
## Regression Coefficients:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## Intercept -6.40 1.57 -9.50 -3.43 1.00 2439 1422
## EVPE_abs -0.22 0.06 -0.34 -0.10 1.00 3373 1100
## EVPE_sign1 1.91 1.47 -1.06 4.83 1.00 4476 2896
## abs_PE1 -0.45 0.08 -0.60 -0.29 1.00 4265 3025
## sign_PE11 -0.74 1.22 -3.11 1.62 1.00 4723 3103
## exam_num.f2 6.38 0.92 4.60 8.15 1.00 5073 3130
## exam_num.f3 -0.17 0.90 -1.97 1.59 1.00 5796 3193
## EVPE_abs:EVPE_sign1 0.69 0.11 0.48 0.90 1.00 4012 2774
## abs_PE1:sign_PE11 0.98 0.11 0.77 1.21 1.00 3851 2737
##
## Further Distributional Parameters:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sigma 14.97 0.27 14.46 15.51 1.00 6051 2317
##
## Draws were sampled using sampling(NUTS). For each parameter, Bulk_ESS
## and Tail_ESS are effective sample size measures, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).
## Warning: There were 40 divergent transitions after warmup. Increasing
## adapt_delta above 0.8 may help. See
## http://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup
## Family: gaussian
## Links: mu = identity
## Formula: Prediction_2_delta ~ EVPE_abs * EVPE_sign + abs_PE2 * sign_PE2 + exam_num.f + (1 | cohort) + (1 | ID)
## Data: predictions[abs(predictions$PE2) < 30, ] (Number of observations: 2160)
## Draws: 4 chains, each with iter = 2000; warmup = 1000; thin = 1;
## total post-warmup draws = 4000
##
## Multilevel Hyperparameters:
## ~cohort (Number of levels: 4)
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sd(Intercept) 4.19 2.37 1.52 10.66 1.01 820 367
##
## ~ID (Number of levels: 781)
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sd(Intercept) 0.41 0.31 0.01 1.17 1.00 1859 1625
##
## Regression Coefficients:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## Intercept -8.06 2.61 -13.89 -3.22 1.01 486 174
## EVPE_abs -0.10 0.05 -0.20 0.00 1.00 2418 2730
## EVPE_sign1 2.00 1.20 -0.29 4.31 1.00 1940 2725
## abs_PE2 -0.46 0.08 -0.62 -0.31 1.00 2200 2934
## sign_PE21 -1.82 1.05 -3.92 0.17 1.00 2093 2784
## exam_num.f2 7.88 0.79 6.25 9.41 1.00 3608 3014
## exam_num.f3 -0.05 0.79 -1.58 1.49 1.00 3273 3023
## EVPE_abs:EVPE_sign1 0.49 0.08 0.33 0.65 1.00 1692 2350
## abs_PE2:sign_PE21 0.82 0.10 0.63 1.02 1.00 1829 2423
##
## Further Distributional Parameters:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sigma 14.71 0.23 14.28 15.15 1.00 4985 3011
##
## Draws were sampled using sampling(NUTS). 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).
EVPE x NE
## 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: gaussian
## Links: mu = identity
## Formula: Prediction_0_delta ~ t2(EVPE, negative_emotionality) + exam_num.f + (1 | ID)
## Data: predictions (Number of observations: 1466)
## Draws: 4 chains, each with iter = 2000; warmup = 1000; thin = 1;
## total post-warmup draws = 4000
##
## Smoothing Spline Hyperparameters:
## Estimate Est.Error l-95% CI u-95% CI Rhat
## sds(t2EVPEnegative_emotionality_1) 6.05 5.26 0.24 19.78 1.00
## sds(t2EVPEnegative_emotionality_2) 4.93 4.65 0.17 17.17 1.00
## sds(t2EVPEnegative_emotionality_3) 12.85 9.36 0.59 34.40 1.01
## Bulk_ESS Tail_ESS
## sds(t2EVPEnegative_emotionality_1) 2308 2858
## sds(t2EVPEnegative_emotionality_2) 3184 2307
## sds(t2EVPEnegative_emotionality_3) 1466 1482
##
## Multilevel Hyperparameters:
## ~ID (Number of levels: 704)
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sd(Intercept) 0.39 0.29 0.02 1.09 1.00 2259 1801
##
## Regression Coefficients:
## Estimate Est.Error l-95% CI u-95% CI Rhat
## Intercept -2.80 0.66 -4.04 -1.41 1.00
## exam_num.f2 0.53 0.68 -0.78 1.88 1.00
## exam_num.f3 -0.59 0.70 -1.98 0.77 1.00
## t2EVPEnegative_emotionality_1 -0.27 0.45 -1.12 0.70 1.00
## t2EVPEnegative_emotionality_2 -0.95 0.72 -2.42 0.42 1.00
## t2EVPEnegative_emotionality_3 1.75 0.95 0.04 3.66 1.00
## Bulk_ESS Tail_ESS
## Intercept 2936 2729
## exam_num.f2 6339 3301
## exam_num.f3 6980 3151
## t2EVPEnegative_emotionality_1 3395 2987
## t2EVPEnegative_emotionality_2 3487 2649
## t2EVPEnegative_emotionality_3 3601 3126
##
## Further Distributional Parameters:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sigma 10.58 0.20 10.19 10.97 1.00 8265 2779
##
## Draws were sampled using sampling(NUTS). 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).
## Warning: There were 5 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: gaussian
## Links: mu = identity
## Formula: Prediction_1_delta ~ t2(EVPE, negative_emotionality) + exam_num.f + (1 | ID)
## Data: predictions (Number of observations: 1663)
## Draws: 4 chains, each with iter = 2000; warmup = 1000; thin = 1;
## total post-warmup draws = 4000
##
## Smoothing Spline Hyperparameters:
## Estimate Est.Error l-95% CI u-95% CI Rhat
## sds(t2EVPEnegative_emotionality_1) 9.69 8.08 0.46 29.99 1.00
## sds(t2EVPEnegative_emotionality_2) 8.41 7.27 0.35 26.76 1.00
## sds(t2EVPEnegative_emotionality_3) 14.64 13.10 0.56 44.70 1.00
## Bulk_ESS Tail_ESS
## sds(t2EVPEnegative_emotionality_1) 2768 2841
## sds(t2EVPEnegative_emotionality_2) 3162 2299
## sds(t2EVPEnegative_emotionality_3) 1658 1871
##
## Multilevel Hyperparameters:
## ~ID (Number of levels: 708)
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sd(Intercept) 0.45 0.35 0.02 1.31 1.00 2874 2651
##
## Regression Coefficients:
## Estimate Est.Error l-95% CI u-95% CI Rhat
## Intercept -6.31 0.91 -8.01 -4.41 1.00
## exam_num.f2 6.21 0.99 4.23 8.12 1.00
## exam_num.f3 0.60 0.98 -1.31 2.56 1.00
## t2EVPEnegative_emotionality_1 1.01 0.58 -0.12 2.16 1.00
## t2EVPEnegative_emotionality_2 3.86 0.96 1.95 5.74 1.00
## t2EVPEnegative_emotionality_3 -0.08 1.27 -2.53 2.52 1.00
## Bulk_ESS Tail_ESS
## Intercept 3419 1325
## exam_num.f2 6245 3226
## exam_num.f3 6849 3346
## t2EVPEnegative_emotionality_1 6151 3058
## t2EVPEnegative_emotionality_2 4263 2466
## t2EVPEnegative_emotionality_3 4708 2617
##
## Further Distributional Parameters:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sigma 16.41 0.29 15.84 16.99 1.00 9856 2732
##
## Draws were sampled using sampling(NUTS). 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).
## Warning: There were 4 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: gaussian
## Links: mu = identity
## Formula: Prediction_2_delta ~ t2(EVPE, negative_emotionality) + exam_num.f + (1 | ID)
## Data: predictions (Number of observations: 2229)
## Draws: 4 chains, each with iter = 2000; warmup = 1000; thin = 1;
## total post-warmup draws = 4000
##
## Smoothing Spline Hyperparameters:
## Estimate Est.Error l-95% CI u-95% CI Rhat
## sds(t2EVPEnegative_emotionality_1) 8.00 7.02 0.22 26.60 1.00
## sds(t2EVPEnegative_emotionality_2) 8.05 7.49 0.24 28.00 1.00
## sds(t2EVPEnegative_emotionality_3) 22.77 13.98 3.01 56.83 1.01
## Bulk_ESS Tail_ESS
## sds(t2EVPEnegative_emotionality_1) 2173 1697
## sds(t2EVPEnegative_emotionality_2) 2959 2420
## sds(t2EVPEnegative_emotionality_3) 1191 1103
##
## Multilevel Hyperparameters:
## ~ID (Number of levels: 790)
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sd(Intercept) 0.37 0.28 0.02 1.03 1.00 3030 2488
##
## Regression Coefficients:
## Estimate Est.Error l-95% CI u-95% CI Rhat
## Intercept -6.73 0.86 -8.32 -5.01 1.00
## exam_num.f2 7.98 0.81 6.35 9.51 1.00
## exam_num.f3 0.68 0.80 -0.89 2.23 1.00
## t2EVPEnegative_emotionality_1 0.67 0.50 -0.31 1.69 1.00
## t2EVPEnegative_emotionality_2 -3.54 0.98 -5.60 -1.73 1.00
## t2EVPEnegative_emotionality_3 -0.54 1.39 -3.48 1.96 1.00
## Bulk_ESS Tail_ESS
## Intercept 2069 1476
## exam_num.f2 5153 3455
## exam_num.f3 5371 3151
## t2EVPEnegative_emotionality_1 4034 3058
## t2EVPEnegative_emotionality_2 2362 1355
## t2EVPEnegative_emotionality_3 2842 1576
##
## Further Distributional Parameters:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sigma 15.71 0.23 15.26 16.19 1.00 7885 3139
##
## Draws were sampled using sampling(NUTS). 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).
