RE_postgrade_analyses
Emily Moraes
1/6/2021
Table of Contents
Density Plots
Post Reveal Entropy per hour
Post Reveal RE/Hour ~ Actual Grade
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: postRE_perhour ~ actual_grade + (1 | subject)
## Data: fm
##
## REML criterion at convergence: -1345.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.3392 -0.6494 0.0008 0.6765 4.2544
##
## Random effects:
## Groups Name Variance Std.Dev.
## subject (Intercept) 0.0001546 0.01243
## Residual 0.0020372 0.04514
## Number of obs: 416, groups: subject, 125
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.467e-01 1.035e-02 2.848e+02 14.169 <2e-16 ***
## actual_grade 8.148e-05 1.389e-04 2.797e+02 0.587 0.558
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## actual_grad -0.970Post Reveal RE/Hour ~ Prediction Error
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: postRE_perhour ~ PE + (1 | subject)
## Data: fm
##
## REML criterion at convergence: -1345.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.3122 -0.6507 -0.0114 0.6554 4.2433
##
## Random effects:
## Groups Name Variance Std.Dev.
## subject (Intercept) 0.000146 0.01208
## Residual 0.002046 0.04523
## Number of obs: 416, groups: subject, 125
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.526e-01 2.488e-03 1.120e+02 61.322 <2e-16 ***
## PE -3.025e-05 1.801e-04 4.034e+02 -0.168 0.867
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## PE 0.028Post Reveal RE/Hour ~ Actual Grade + Prediction Error
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: postRE_perhour ~ actual_grade + PE + (1 | subject)
## Data: fm
##
## REML criterion at convergence: -1330.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.3442 -0.6413 0.0067 0.6757 4.2761
##
## Random effects:
## Groups Name Variance Std.Dev.
## subject (Intercept) 0.0001532 0.01238
## Residual 0.0020420 0.04519
## Number of obs: 416, groups: subject, 125
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.433e-01 1.210e-02 2.648e+02 11.842 <2e-16 ***
## actual_grade 1.281e-04 1.630e-04 2.608e+02 0.786 0.433
## PE -1.162e-04 2.115e-04 3.925e+02 -0.549 0.583
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) actl_g
## actual_grad -0.978
## PE 0.517 -0.523
Post Reveal RE/Hour ~ PHQ
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: postRE_perhour ~ PHQ + (1 | subject)
## Data: fm
##
## REML criterion at convergence: -1356.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.3869 -0.6187 -0.0102 0.6681 4.1939
##
## Random effects:
## Groups Name Variance Std.Dev.
## subject (Intercept) 0.0001644 0.01282
## Residual 0.0020160 0.04490
## Number of obs: 418, groups: subject, 125
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.554e-01 3.481e-03 1.505e+02 44.641 <2e-16 ***
## PHQ -5.613e-04 4.736e-04 2.479e+02 -1.185 0.237
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## PHQ -0.696
Post Reveal RE/Hour ~ Exam number
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: postRE_perhour ~ exam_num + (1 | subject)
## Data: fm3
##
## REML criterion at convergence: -1370.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.6614 -0.5489 0.0663 0.6804 4.0863
##
## Random effects:
## Groups Name Variance Std.Dev.
## subject (Intercept) 0.0001662 0.01289
## Residual 0.0018936 0.04352
## Number of obs: 418, groups: subject, 125
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.383e-01 4.214e-03 4.077e+02 32.816 < 2e-16 ***
## exam_num2 2.479e-02 5.859e-03 3.169e+02 4.232 3.04e-05 ***
## exam_num3 7.792e-03 5.984e-03 3.192e+02 1.302 0.194
## exam_num4 2.660e-02 6.046e-03 3.270e+02 4.400 1.47e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) exm_n2 exm_n3
## exam_num2 -0.666
## exam_num3 -0.650 0.470
## exam_num4 -0.644 0.466 0.459Post Reveal RE/Hour ~ Cognitive attention average
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: postRE_perhour ~ Cog_attn_avg + (1 | subject)
## Data: fm3
##
## REML criterion at convergence: -886.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.5848 -0.6179 0.0126 0.6493 4.0374
##
## Random effects:
## Groups Name Variance Std.Dev.
## subject (Intercept) 0.0002323 0.01524
## Residual 0.0020309 0.04507
## Number of obs: 279, groups: subject, 113
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.416e-01 8.125e-03 2.202e+02 17.432 <2e-16 ***
## Cog_attn_avg 3.298e-04 1.550e-04 2.441e+02 2.128 0.0343 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## Cog_attn_vg -0.925Post Reveal RE/Hour ~ Sleep hours
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: postRE_perhour ~ Sleep_hours + (1 | subject)
## Data: fm3
##
## REML criterion at convergence: -892.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.4369 -0.6099 0.0050 0.6603 3.8615
##
## Random effects:
## Groups Name Variance Std.Dev.
## subject (Intercept) 0.0001911 0.01382
## Residual 0.0020560 0.04534
## Number of obs: 279, groups: subject, 113
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.184372 0.011443 235.737219 16.113 <2e-16 ***
## Sleep_hours -0.003822 0.001578 245.530884 -2.421 0.0162 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## Sleep_hours -0.964Post Reveal RE/Hour ~ Gender
##
## Welch Two Sample t-test
##
## data: fm3$postRE_perhour by fm3$gender
## t = 0.2946, df = 190.71, p-value = 0.7686
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.008753659 0.011827588
## sample estimates:
## mean in group Female mean in group Male
## 0.1529557 0.1514187Post Reveal RE/Hour ~ School Year
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: postRE_perhour ~ schoolyear + (1 | subject)
## Data: fm3
##
## REML criterion at convergence: -1324.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.2505 -0.6307 -0.0036 0.6576 4.2563
##
## Random effects:
## Groups Name Variance Std.Dev.
## subject (Intercept) 0.0001696 0.01302
## Residual 0.0020301 0.04506
## Number of obs: 411, groups: subject, 123
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.150797 0.002844 108.643097 53.026 <2e-16
## schoolyearSecond (Sophomore) 0.008498 0.007065 112.334972 1.203 0.232
## schoolyearThird (Junior) 0.010997 0.014006 110.541700 0.785 0.434
## schoolyearFourth (Senior) -0.002533 0.019887 95.941090 -0.127 0.899
##
## (Intercept) ***
## schoolyearSecond (Sophomore)
## schoolyearThird (Junior)
## schoolyearFourth (Senior)
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) scS(S) scT(J)
## schlyrS(Sp) -0.403
## schlyrT(Jn) -0.203 0.082
## schlyrF(Sn) -0.143 0.058 0.029
Post Reveal RE/Hour ~ Actual Grade ~ School Year
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: postRE_perhour ~ schoolyear + (1 | subject)
## Data: fm3
##
## REML criterion at convergence: -1324.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.2505 -0.6307 -0.0036 0.6576 4.2563
##
## Random effects:
## Groups Name Variance Std.Dev.
## subject (Intercept) 0.0001696 0.01302
## Residual 0.0020301 0.04506
## Number of obs: 411, groups: subject, 123
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.150797 0.002844 108.643097 53.026 <2e-16
## schoolyearSecond (Sophomore) 0.008498 0.007065 112.334972 1.203 0.232
## schoolyearThird (Junior) 0.010997 0.014006 110.541700 0.785 0.434
## schoolyearFourth (Senior) -0.002533 0.019887 95.941090 -0.127 0.899
##
## (Intercept) ***
## schoolyearSecond (Sophomore)
## schoolyearThird (Junior)
## schoolyearFourth (Senior)
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) scS(S) scT(J)
## schlyrS(Sp) -0.403
## schlyrT(Jn) -0.203 0.082
## schlyrF(Sn) -0.143 0.058 0.029
Extraversion
low -> 8-18
med-low -> 19-22
med-high -> 23-25
high -> 26-30
Post Reveal RE/Hour ~ Actual Grade * Extraversion
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: postRE_perhour ~ actual_grade * extraversion + (1 | subject)
## Data: fm
##
## REML criterion at convergence: -1290.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.37417 -0.66084 0.00092 0.66037 2.89117
##
## Random effects:
## Groups Name Variance Std.Dev.
## subject (Intercept) 0.0001813 0.01346
## Residual 0.0019534 0.04420
## Number of obs: 405, groups: subject, 122
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.535e-01 5.144e-02 3.398e+02 2.985 0.00304 **
## actual_grade 5.135e-05 6.892e-04 3.435e+02 0.075 0.94065
## extraversion -4.076e-04 2.298e-03 3.179e+02 -0.177 0.85931
## actual_grade:extraversion 2.190e-06 3.098e-05 3.209e+02 0.071 0.94370
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) actl_g extrvr
## actual_grad -0.976
## extraversin -0.979 0.957
## actl_grd:xt 0.951 -0.979 -0.974Post Reveal RE/Hour ~ Prediction Error + Extraversion
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: postRE_perhour ~ PE + extraversion + (1 | subject)
## Data: fm
##
## REML criterion at convergence: -1309.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.34350 -0.64770 -0.01275 0.68140 2.88795
##
## Random effects:
## Groups Name Variance Std.Dev.
## subject (Intercept) 0.000172 0.01312
## Residual 0.001957 0.04424
## Number of obs: 405, groups: subject, 122
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.580e-01 1.117e-02 9.972e+01 14.137 <2e-16 ***
## PE -6.051e-05 1.784e-04 3.926e+02 -0.339 0.735
## extraversion -2.831e-04 5.150e-04 1.004e+02 -0.550 0.584
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) PE
## PE -0.030
## extraversin -0.974 0.039
Post Reveal RE/Hour ~ Actual Grade * Exam number
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: postRE_perhour ~ actual_grade * exam_num + (1 | subject)
## Data: fm
##
## REML criterion at convergence: -1307.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.5665 -0.5445 0.0493 0.6762 3.8327
##
## Random effects:
## Groups Name Variance Std.Dev.
## subject (Intercept) 0.0001642 0.01282
## Residual 0.0019062 0.04366
## Number of obs: 416, groups: subject, 125
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.372e-01 1.632e-02 4.080e+02 8.406 7.16e-16 ***
## actual_grade 1.658e-05 2.458e-04 4.080e+02 0.067 0.9463
## exam_num2 1.151e-02 2.449e-02 3.635e+02 0.470 0.6385
## exam_num3 2.754e-02 2.707e-02 3.724e+02 1.017 0.3097
## exam_num4 6.562e-02 3.480e-02 3.768e+02 1.885 0.0601 .
## actual_grade:exam_num2 1.941e-04 3.531e-04 3.612e+02 0.550 0.5828
## actual_grade:exam_num3 -2.516e-04 3.608e-04 3.654e+02 -0.698 0.4859
## actual_grade:exam_num4 -4.960e-04 4.546e-04 3.715e+02 -1.091 0.2760
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) actl_g exm_n2 exm_n3 exm_n4 ac_:_2 ac_:_3
## actual_grad -0.966
## exam_num2 -0.637 0.616
## exam_num3 -0.570 0.551 0.385
## exam_num4 -0.438 0.423 0.304 0.276
## actl_grd:_2 0.645 -0.667 -0.970 -0.388 -0.306
## actl_grd:_3 0.628 -0.650 -0.420 -0.970 -0.299 0.453
## actl_grd:_4 0.493 -0.510 -0.338 -0.306 -0.981 0.364 0.355
Post Reveal RE/Hour (day after) ~ Grade Reveal Time
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: postRE_perhour ~ grade_reveal_time + (1 | subject)
## Data: fm
##
## REML criterion at convergence: -1349.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.6146 -0.6238 -0.0124 0.6809 4.2230
##
## Random effects:
## Groups Name Variance Std.Dev.
## subject (Intercept) 0.0001568 0.01252
## Residual 0.0020259 0.04501
## Number of obs: 418, groups: subject, 125
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.666e-01 1.711e-02 4.131e+02 9.738 <2e-16 ***
## grade_reveal_time -1.598e-05 1.923e-05 4.078e+02 -0.831 0.407
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## grad_rvl_tm -0.989
PHQ
low -> 0
med-low -> 1-4
med-high -> 5-8
high -> 9-25
Post Reveal RE/Hour ~ Actual Grade * PHQ
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: postRE_perhour ~ actual_grade * PHQ + (1 | subject)
## Data: fm
##
## REML criterion at convergence: -1313.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.4038 -0.6386 -0.0068 0.6774 4.2033
##
## Random effects:
## Groups Name Variance Std.Dev.
## subject (Intercept) 0.0001712 0.01308
## Residual 0.0020275 0.04503
## Number of obs: 416, groups: subject, 125
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.477e-01 1.579e-02 2.843e+02 9.356 <2e-16 ***
## actual_grade 1.026e-04 2.048e-04 2.850e+02 0.501 0.617
## PHQ 1.214e-04 2.154e-03 3.654e+02 0.056 0.955
## actual_grade:PHQ -9.040e-06 2.915e-05 3.584e+02 -0.310 0.757
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) actl_g PHQ
## actual_grad -0.975
## PHQ -0.745 0.730
## actl_gr:PHQ 0.703 -0.722 -0.974
Unique Locations per Hour
Day After Grade Reveal U
Unique Locations/Hour (day after) ~ Actual Grade
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: unique_perhour2 ~ actual_grade + (1 | subject)
## Data: fm
##
## REML criterion at convergence: 2319.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.0374 -0.6513 -0.3504 0.3398 6.6257
##
## Random effects:
## Groups Name Variance Std.Dev.
## subject (Intercept) 0.00 0.0
## Residual 15.21 3.9
## Number of obs: 416, groups: subject, 124
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 3.692e+00 8.312e-01 4.140e+02 4.441 1.15e-05 ***
## actual_grade 4.485e-03 1.115e-02 4.140e+02 0.402 0.688
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## actual_grad -0.973
## convergence code: 0
## boundary (singular) fit: see ?isSingularUnique Locations/Hour (day after) ~ Prediction Error
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: unique_perhour2 ~ PE + (1 | subject)
## Data: fm
##
## REML criterion at convergence: 2319
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.0203 -0.6494 -0.3411 0.3240 6.6446
##
## Random effects:
## Groups Name Variance Std.Dev.
## subject (Intercept) 0.00 0.0
## Residual 15.21 3.9
## Number of obs: 416, groups: subject, 124
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 4.016521 0.191246 414.000000 21.002 <2e-16 ***
## PE -0.002704 0.014885 414.000000 -0.182 0.856
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## PE 0.012
## convergence code: 0
## boundary (singular) fit: see ?isSingular
Unique Locations/Hour (day after) ~ PHQ
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: unique_perhour2 ~ PHQ + (1 | subject)
## Data: fm
##
## REML criterion at convergence: 2326.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.0056 -0.6489 -0.3351 0.3404 6.6421
##
## Random effects:
## Groups Name Variance Std.Dev.
## subject (Intercept) 0.00 0.000
## Residual 15.15 3.893
## Number of obs: 418, groups: subject, 124
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 3.95624 0.26818 416.00000 14.752 <2e-16 ***
## PHQ 0.01029 0.03729 416.00000 0.276 0.783
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## PHQ -0.704
## convergence code: 0
## boundary (singular) fit: see ?isSingularUnique Locations/Hour (day after) * Prediction Error
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: unique_perhour2 ~ actual_grade * PE + (1 | subject)
## Data: fm
##
## REML criterion at convergence: 2338.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.0537 -0.6398 -0.3421 0.3318 6.6146
##
## Random effects:
## Groups Name Variance Std.Dev.
## subject (Intercept) 0.00 0.000
## Residual 15.27 3.908
## Number of obs: 416, groups: subject, 124
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 3.492e+00 9.688e-01 4.120e+02 3.605 0.000351 ***
## actual_grade 7.364e-03 1.299e-02 4.120e+02 0.567 0.570962
## PE -1.880e-03 4.902e-02 4.120e+02 -0.038 0.969424
## actual_grade:PE -9.344e-05 7.316e-04 4.120e+02 -0.128 0.898430
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) actl_g PE
## actual_grad -0.977
## PE 0.289 -0.216
## actl_grd:PE -0.122 0.039 -0.936
## convergence code: 0
## boundary (singular) fit: see ?isSingular
Extraversion
low -> 8-18
med-low -> 19-22
med-high -> 23-25
high -> 26-30
Unique Locations/Hour (day after) ~ Actual Grade * Extraversion
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: unique_perhour2 ~ actual_grade * extraversion + (1 | subject)
## Data: fm
##
## REML criterion at convergence: 2278.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.1827 -0.6487 -0.3366 0.3467 6.5719
##
## Random effects:
## Groups Name Variance Std.Dev.
## subject (Intercept) 0.00 0.000
## Residual 15.49 3.936
## Number of obs: 405, groups: subject, 121
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 8.454529 4.355469 401.000000 1.941 0.0529 .
## actual_grade -0.051388 0.058238 401.000000 -0.882 0.3781
## extraversion -0.198201 0.193042 401.000000 -1.027 0.3052
## actual_grade:extraversion 0.002314 0.002597 401.000000 0.891 0.3734
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) actl_g extrvr
## actual_grad -0.980
## extraversin -0.980 0.962
## actl_grd:xt 0.957 -0.980 -0.978
## convergence code: 0
## boundary (singular) fit: see ?isSingularUnique Locations/Hour (day after) ~ Prediction Error + Extraversion
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: unique_perhour2 ~ PE + extraversion + (1 | subject)
## Data: fm
##
## REML criterion at convergence: 2268.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.0723 -0.6406 -0.3220 0.3554 6.5419
##
## Random effects:
## Groups Name Variance Std.Dev.
## subject (Intercept) 0.00 0.000
## Residual 15.49 3.935
## Number of obs: 405, groups: subject, 121
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 4.707684 0.874123 402.000000 5.386 1.23e-07 ***
## PE -0.003198 0.015093 402.000000 -0.212 0.832
## extraversion -0.030267 0.040260 402.000000 -0.752 0.453
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) PE
## PE -0.046
## extraversin -0.975 0.050
## convergence code: 0
## boundary (singular) fit: see ?isSingular
Unique Locations/Hour (day after) ~ Actual Grade * Exam number
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: unique_perhour2 ~ actual_grade * exam_num + (1 | subject)
## Data: fm
##
## REML criterion at convergence: 2260.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.7477 -0.4774 -0.1695 0.2701 6.7093
##
## Random effects:
## Groups Name Variance Std.Dev.
## subject (Intercept) 0.00 0.000
## Residual 12.91 3.593
## Number of obs: 416, groups: subject, 124
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.907765 1.293651 408.000000 2.248 0.0251 *
## actual_grade -0.008642 0.019500 408.000000 -0.443 0.6579
## exam_num2 3.269958 1.992166 408.000000 1.641 0.1015
## exam_num3 0.208801 2.144476 408.000000 0.097 0.9225
## exam_num4 4.354223 2.736611 408.000000 1.591 0.1124
## actual_grade:exam_num2 -0.003379 0.028706 408.000000 -0.118 0.9064
## actual_grade:exam_num3 0.003635 0.028835 408.000000 0.126 0.8997
## actual_grade:exam_num4 -0.008187 0.036010 408.000000 -0.227 0.8203
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) actl_g exm_n2 exm_n3 exm_n4 ac_:_2 ac_:_3
## actual_grad -0.966
## exam_num2 -0.649 0.627
## exam_num3 -0.603 0.582 0.392
## exam_num4 -0.473 0.456 0.307 0.285
## actl_grd:_2 0.656 -0.679 -0.969 -0.396 -0.310
## actl_grd:_3 0.653 -0.676 -0.424 -0.969 -0.309 0.459
## actl_grd:_4 0.523 -0.541 -0.340 -0.315 -0.979 0.368 0.366
## convergence code: 0
## boundary (singular) fit: see ?isSingular
Unique Locations/Hour (day after) ~ Grade Reveal Time
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: unique_perhour2 ~ grade_reveal_time + (1 | subject)
## Data: fm
##
## REML criterion at convergence: 2330.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.1527 -0.6313 -0.3132 0.2896 6.7149
##
## Random effects:
## Groups Name Variance Std.Dev.
## subject (Intercept) 0.00 0.000
## Residual 15.06 3.881
## Number of obs: 418, groups: subject, 124
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 6.100544 1.301525 416.000000 4.687 3.76e-06 ***
## grade_reveal_time -0.002360 0.001453 416.000000 -1.625 0.105
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## grad_rvl_tm -0.989
## convergence code: 0
## boundary (singular) fit: see ?isSingular
PHQ
low -> 0
med-low -> 1-4
med-high -> 5-8
high -> 9-25
Unique Locations/Hour (day after) ~ Actual Grade * PHQ
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: unique_perhour2 ~ actual_grade * PHQ + (1 | subject)
## Data: fm
##
## REML criterion at convergence: 2334.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.0264 -0.6455 -0.3475 0.3433 6.5903
##
## Random effects:
## Groups Name Variance Std.Dev.
## subject (Intercept) 0.00 0.000
## Residual 15.28 3.909
## Number of obs: 416, groups: subject, 124
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 3.553e+00 1.252e+00 4.120e+02 2.838 0.00477 **
## actual_grade 5.368e-03 1.629e-02 4.120e+02 0.330 0.74186
## PHQ 1.680e-02 1.740e-01 4.120e+02 0.097 0.92315
## actual_grade:PHQ -2.926e-05 2.364e-03 4.120e+02 -0.012 0.99013
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) actl_g PHQ
## actual_grad -0.977
## PHQ -0.739 0.726
## actl_gr:PHQ 0.698 -0.718 -0.976
## convergence code: 0
## boundary (singular) fit: see ?isSingularNovel Locations per Hour
Day after Grade Reveal N
Novel Locations/Hour (day after) ~ Actual Grade
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: novel_perhour2 ~ actual_grade + (1 | subject)
## Data: fm
##
## REML criterion at convergence: 2336.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -0.8747 -0.6612 -0.3797 0.3110 6.5895
##
## Random effects:
## Groups Name Variance Std.Dev.
## subject (Intercept) 0.00 0.000
## Residual 15.86 3.982
## Number of obs: 416, groups: subject, 124
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 3.512e+00 8.488e-01 4.140e+02 4.137 4.26e-05 ***
## actual_grade -5.261e-04 1.139e-02 4.140e+02 -0.046 0.963
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## actual_grad -0.973
## convergence code: 0
## boundary (singular) fit: see ?isSingularNovel Locations/Hour (day after) ~ Prediction Error
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: novel_perhour2 ~ PE + (1 | subject)
## Data: fm
##
## REML criterion at convergence: 2335.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -0.8982 -0.6557 -0.3759 0.3123 6.6062
##
## Random effects:
## Groups Name Variance Std.Dev.
## subject (Intercept) 0.00 0.00
## Residual 15.84 3.98
## Number of obs: 416, groups: subject, 124
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 3.47200 0.19517 414.00000 17.79 <2e-16 ***
## PE -0.00972 0.01519 414.00000 -0.64 0.523
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## PE 0.012
## convergence code: 0
## boundary (singular) fit: see ?isSingular
Novel Locations/Hour (day after) ~ PHQ
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: novel_perhour2 ~ PHQ + (1 | subject)
## Data: fm
##
## REML criterion at convergence: 2343.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -0.8955 -0.6566 -0.3775 0.3155 6.5939
##
## Random effects:
## Groups Name Variance Std.Dev.
## subject (Intercept) 0.00 0.000
## Residual 15.79 3.974
## Number of obs: 418, groups: subject, 124
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 3.428e+00 2.738e-01 4.160e+02 12.521 <2e-16 ***
## PHQ 7.694e-03 3.807e-02 4.160e+02 0.202 0.84
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## PHQ -0.704
## convergence code: 0
## boundary (singular) fit: see ?isSingularNovel Locations/Hour (day after) * Prediction Error
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: novel_perhour2 ~ actual_grade * PE + (1 | subject)
## Data: fm
##
## REML criterion at convergence: 2354.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -0.9089 -0.6553 -0.3739 0.3248 6.5884
##
## Random effects:
## Groups Name Variance Std.Dev.
## subject (Intercept) 0.0 0.000
## Residual 15.9 3.988
## Number of obs: 416, groups: subject, 124
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 3.232e+00 9.886e-01 4.120e+02 3.269 0.00117 **
## actual_grade 3.971e-03 1.325e-02 4.120e+02 0.300 0.76456
## PE 1.466e-02 5.002e-02 4.120e+02 0.293 0.76965
## actual_grade:PE -4.351e-04 7.465e-04 4.120e+02 -0.583 0.56034
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) actl_g PE
## actual_grad -0.977
## PE 0.289 -0.216
## actl_grd:PE -0.122 0.039 -0.936
## convergence code: 0
## boundary (singular) fit: see ?isSingular
Extraversion
low -> 8-18
med-low -> 19-22
med-high -> 23-25
high -> 26-30
Novel Locations/Hour (day after) ~ Actual Grade * Extraversion
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: novel_perhour2 ~ actual_grade * extraversion + (1 | subject)
## Data: fm
##
## REML criterion at convergence: 2295.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.0459 -0.6479 -0.3914 0.3161 6.5342
##
## Random effects:
## Groups Name Variance Std.Dev.
## subject (Intercept) 0.00 0.000
## Residual 16.18 4.023
## Number of obs: 405, groups: subject, 121
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 7.822270 4.451001 401.000000 1.757 0.0796 .
## actual_grade -0.051811 0.059516 401.000000 -0.871 0.3845
## extraversion -0.177400 0.197276 401.000000 -0.899 0.3691
## actual_grade:extraversion 0.002103 0.002654 401.000000 0.792 0.4287
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) actl_g extrvr
## actual_grad -0.980
## extraversin -0.980 0.962
## actl_grd:xt 0.957 -0.980 -0.978
## convergence code: 0
## boundary (singular) fit: see ?isSingularNovel Locations/Hour (day after) ~ Prediction Error + Extraversion
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: novel_perhour2 ~ PE + extraversion + (1 | subject)
## Data: fm
##
## REML criterion at convergence: 2285.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -0.9157 -0.6516 -0.3759 0.3087 6.5057
##
## Random effects:
## Groups Name Variance Std.Dev.
## subject (Intercept) 0.00 0.00
## Residual 16.16 4.02
## Number of obs: 405, groups: subject, 121
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 4.03175 0.89292 402.00000 4.515 8.32e-06 ***
## PE -0.01017 0.01542 402.00000 -0.660 0.510
## extraversion -0.02427 0.04113 402.00000 -0.590 0.555
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) PE
## PE -0.046
## extraversin -0.975 0.050
## convergence code: 0
## boundary (singular) fit: see ?isSingular
Novel Locations/Hour (day after) ~ Actual Grade * Exam number
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: novel_perhour2 ~ actual_grade * exam_num + (1 | subject)
## Data: fm
##
## REML criterion at convergence: 2255.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.6633 -0.4177 -0.1774 0.2311 6.7656
##
## Random effects:
## Groups Name Variance Std.Dev.
## subject (Intercept) 0.00 0.000
## Residual 12.75 3.571
## Number of obs: 416, groups: subject, 124
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.714e+00 1.286e+00 4.080e+02 2.111 0.0354 *
## actual_grade -1.182e-02 1.938e-02 4.080e+02 -0.610 0.5422
## exam_num2 3.240e+00 1.980e+00 4.080e+02 1.636 0.1025
## exam_num3 -5.865e-01 2.131e+00 4.080e+02 -0.275 0.7833
## exam_num4 3.860e+00 2.720e+00 4.080e+02 1.419 0.1566
## actual_grade:exam_num2 -1.261e-03 2.853e-02 4.080e+02 -0.044 0.9648
## actual_grade:exam_num3 4.283e-03 2.866e-02 4.080e+02 0.149 0.8813
## actual_grade:exam_num4 -6.014e-05 3.579e-02 4.080e+02 -0.002 0.9987
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) actl_g exm_n2 exm_n3 exm_n4 ac_:_2 ac_:_3
## actual_grad -0.966
## exam_num2 -0.649 0.627
## exam_num3 -0.603 0.582 0.392
## exam_num4 -0.473 0.456 0.307 0.285
## actl_grd:_2 0.656 -0.679 -0.969 -0.396 -0.310
## actl_grd:_3 0.653 -0.676 -0.424 -0.969 -0.309 0.459
## actl_grd:_4 0.523 -0.541 -0.340 -0.315 -0.979 0.368 0.366
## convergence code: 0
## boundary (singular) fit: see ?isSingular
Novel Locations/Hour (day after) ~ Grade Reveal Time
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: novel_perhour2 ~ grade_reveal_time + (1 | subject)
## Data: fm
##
## REML criterion at convergence: 2348.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.0140 -0.6420 -0.3545 0.2793 6.6484
##
## Random effects:
## Groups Name Variance Std.Dev.
## subject (Intercept) 0.00 0.000
## Residual 15.73 3.966
## Number of obs: 418, groups: subject, 124
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 5.190171 1.330013 416.000000 3.902 0.000111 ***
## grade_reveal_time -0.001944 0.001484 416.000000 -1.310 0.190976
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## grad_rvl_tm -0.989
## convergence code: 0
## boundary (singular) fit: see ?isSingular
PHQ
low -> 0
med-low -> 1-4
med-high -> 5-8
high -> 9-25
Novel Locations/Hour (day after) ~ Actual Grade * PHQ
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: novel_perhour2 ~ actual_grade * PHQ + (1 | subject)
## Data: fm
##
## REML criterion at convergence: 2351.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -0.8840 -0.6647 -0.3705 0.3212 6.5425
##
## Random effects:
## Groups Name Variance Std.Dev.
## subject (Intercept) 0.00 0.000
## Residual 15.93 3.991
## Number of obs: 416, groups: subject, 124
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 3.777e+00 1.278e+00 4.120e+02 2.954 0.00331 **
## actual_grade -4.655e-03 1.663e-02 4.120e+02 -0.280 0.77970
## PHQ -5.762e-02 1.777e-01 4.120e+02 -0.324 0.74595
## actual_grade:PHQ 9.246e-04 2.414e-03 4.120e+02 0.383 0.70195
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) actl_g PHQ
## actual_grad -0.977
## PHQ -0.739 0.726
## actl_gr:PHQ 0.698 -0.718 -0.976
## convergence code: 0
## boundary (singular) fit: see ?isSingularPost Reveal Entropy
Post Reveal RE ~ Actual Grade
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: post_reveal_entropy ~ actual_grade + (1 | subject)
## Data: fm
##
## REML criterion at convergence: 710.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.5108 -0.6739 -0.0358 0.5819 4.8329
##
## Random effects:
## Groups Name Variance Std.Dev.
## subject (Intercept) 0.006947 0.08335
## Residual 0.299393 0.54717
## Number of obs: 421, groups: subject, 125
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.087e+00 1.191e-01 2.647e+02 9.127 < 2e-16 ***
## actual_grade 4.528e-03 1.598e-03 2.586e+02 2.833 0.00498 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## actual_grad -0.972Post Reveal RE ~ Actual Grade (Exams 2, 3, 4)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: post_reveal_entropy ~ actual_grade + (1 | subject)
## Data: fm2
##
## REML criterion at convergence: 533
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.4686 -0.5574 0.0386 0.5465 4.5380
##
## Random effects:
## Groups Name Variance Std.Dev.
## subject (Intercept) 0.02539 0.1593
## Residual 0.29803 0.5459
## Number of obs: 305, groups: subject, 117
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.547e+00 1.626e-01 2.202e+02 9.515 <2e-16 ***
## actual_grade 8.889e-05 2.101e-03 2.184e+02 0.042 0.966
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## actual_grad -0.977
Post Reveal RE ~ Prediction Error
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: post_reveal_entropy ~ PE + (1 | subject)
## Data: fm
##
## REML criterion at convergence: 714.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.4700 -0.6558 -0.0277 0.6049 4.9120
##
## Random effects:
## Groups Name Variance Std.Dev.
## subject (Intercept) 0.004364 0.06606
## Residual 0.305191 0.55244
## Number of obs: 421, groups: subject, 125
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.417e+00 2.763e-02 1.089e+02 51.275 <2e-16 ***
## PE 3.882e-03 2.128e-03 3.979e+02 1.824 0.0689 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## PE 0.022Post Reveal RE ~ Prediction Error (Exams 2, 3, 4)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: post_reveal_entropy ~ PE + (1 | subject)
## Data: fm2
##
## REML criterion at convergence: 531.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.5229 -0.5475 0.0336 0.5441 4.5563
##
## Random effects:
## Groups Name Variance Std.Dev.
## subject (Intercept) 0.02382 0.1543
## Residual 0.29852 0.5464
## Number of obs: 305, groups: subject, 117
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.558145 0.034974 110.139613 44.55 <2e-16 ***
## PE -0.002514 0.002762 302.331036 -0.91 0.364
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## PE -0.146
Post Reveal RE ~ PHQ
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: post_reveal_entropy ~ PHQ + (1 | subject)
## Data: fm
##
## REML criterion at convergence: 713.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.5327 -0.6548 -0.0326 0.5907 4.9211
##
## Random effects:
## Groups Name Variance Std.Dev.
## subject (Intercept) 0.004322 0.06574
## Residual 0.303045 0.55050
## Number of obs: 423, groups: subject, 125
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.474284 0.038777 143.979700 38.019 <2e-16 ***
## PHQ -0.011707 0.005349 221.562667 -2.189 0.0297 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## PHQ -0.706Post Reveal RE ~ Actual Grade * Prediction Error
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: post_reveal_entropy ~ actual_grade * PE + (1 | subject)
## Data: fm
##
## REML criterion at convergence: 737.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.5037 -0.6670 -0.0335 0.5962 4.8277
##
## Random effects:
## Groups Name Variance Std.Dev.
## subject (Intercept) 0.007261 0.08521
## Residual 0.300301 0.54800
## Number of obs: 421, groups: subject, 125
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.125e+00 1.399e-01 2.491e+02 8.037 3.7e-14 ***
## actual_grade 4.078e-03 1.874e-03 2.424e+02 2.176 0.0305 *
## PE 3.769e-03 6.966e-03 4.133e+02 0.541 0.5888
## actual_grade:PE -4.278e-05 1.039e-04 4.138e+02 -0.412 0.6806
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) actl_g PE
## actual_grad -0.977
## PE 0.294 -0.222
## actl_grd:PE -0.121 0.041 -0.934
Extraversion
low -> 8-18
med-low -> 19-22
med-high -> 23-25
high -> 26-30
Post Reveal RE ~ Actual Grade * Extraversion
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: post_reveal_entropy ~ actual_grade * extraversion + (1 | subject)
## Data: fm
##
## REML criterion at convergence: 703.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.5219 -0.6786 -0.0219 0.6134 4.8567
##
## Random effects:
## Groups Name Variance Std.Dev.
## subject (Intercept) 0.006967 0.08347
## Residual 0.292402 0.54074
## Number of obs: 410, groups: subject, 122
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 9.779e-01 5.949e-01 3.045e+02 1.644 0.101
## actual_grade 7.311e-03 7.990e-03 3.072e+02 0.915 0.361
## extraversion 3.399e-03 2.651e-02 2.835e+02 0.128 0.898
## actual_grade:extraversion -1.131e-04 3.581e-04 2.854e+02 -0.316 0.752
##
## Correlation of Fixed Effects:
## (Intr) actl_g extrvr
## actual_grad -0.978
## extraversin -0.979 0.959
## actl_grd:xt 0.955 -0.979 -0.977Post Reveal RE ~ Prediction Error + Extraversion
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: post_reveal_entropy ~ PE + extraversion + (1 | subject)
## Data: fm
##
## REML criterion at convergence: 695.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.4476 -0.6566 -0.0256 0.6256 4.9421
##
## Random effects:
## Groups Name Variance Std.Dev.
## subject (Intercept) 0.006316 0.07948
## Residual 0.296784 0.54478
## Number of obs: 410, groups: subject, 122
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.529644 0.123253 95.975952 12.411 <2e-16 ***
## PE 0.003515 0.002118 386.397009 1.660 0.0978 .
## extraversion -0.005642 0.005690 96.980539 -0.992 0.3239
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) PE
## PE -0.033
## extraversin -0.974 0.040
Post Reveal RE ~ Actual Grade * Exam number
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: post_reveal_entropy ~ actual_grade * exam_num + (1 | subject)
## Data: fm
##
## REML criterion at convergence: 669.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.8723 -0.4696 0.0270 0.5748 4.8003
##
## Random effects:
## Groups Name Variance Std.Dev.
## subject (Intercept) 0.01772 0.1331
## Residual 0.24035 0.4903
## Number of obs: 421, groups: subject, 125
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 9.902e-01 1.824e-01 4.130e+02 5.428 9.72e-08 ***
## actual_grade 9.647e-04 2.747e-03 4.130e+02 0.351 0.7256
## exam_num2 3.352e-01 2.744e-01 3.703e+02 1.222 0.2226
## exam_num3 6.092e-01 2.990e-01 3.759e+02 2.038 0.0423 *
## exam_num4 4.995e-01 3.803e-01 3.846e+02 1.314 0.1898
## actual_grade:exam_num2 3.334e-03 3.956e-03 3.680e+02 0.843 0.4000
## actual_grade:exam_num3 -3.002e-03 4.005e-03 3.694e+02 -0.750 0.4539
## actual_grade:exam_num4 3.819e-04 4.989e-03 3.793e+02 0.077 0.9390
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) actl_g exm_n2 exm_n3 exm_n4 ac_:_2 ac_:_3
## actual_grad -0.966
## exam_num2 -0.639 0.618
## exam_num3 -0.580 0.561 0.390
## exam_num4 -0.454 0.438 0.311 0.288
## actl_grd:_2 0.647 -0.669 -0.969 -0.393 -0.313
## actl_grd:_3 0.636 -0.658 -0.424 -0.969 -0.311 0.457
## actl_grd:_4 0.507 -0.525 -0.345 -0.318 -0.980 0.371 0.367Post Reveal RE ~ Actual Grade * Exam number (Exams 2, 3, 4)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: post_reveal_entropy ~ actual_grade * exam_num + (1 | subject)
## Data: fm2
##
## REML criterion at convergence: 547.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.4405 -0.5632 0.0491 0.5689 4.4611
##
## Random effects:
## Groups Name Variance Std.Dev.
## subject (Intercept) 0.02483 0.1576
## Residual 0.30024 0.5479
## Number of obs: 305, groups: subject, 117
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.337970 0.591079 261.017024 2.264 0.0244 *
## actual_grade 0.003585 0.007878 260.104195 0.455 0.6495
## exam_num 0.072112 0.211225 269.329986 0.341 0.7331
## actual_grade:exam_num -0.001184 0.002748 264.680216 -0.431 0.6669
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) actl_g exm_nm
## actual_grad -0.978
## exam_num -0.960 0.931
## actl_grd:x_ 0.952 -0.961 -0.982Post Reveal RE ~ Prediction Error + Exam number
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: post_reveal_entropy ~ PE + exam_num + (1 | subject)
## Data: fm
##
## REML criterion at convergence: 644.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.8924 -0.4873 0.0356 0.5741 4.9656
##
## Random effects:
## Groups Name Variance Std.Dev.
## subject (Intercept) 0.01699 0.1304
## Residual 0.24087 0.4908
## Number of obs: 421, groups: subject, 125
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.052e+00 4.876e-02 4.106e+02 21.571 < 2e-16 ***
## PE -1.847e-05 2.087e-03 3.940e+02 -0.009 0.993
## exam_num2 5.734e-01 6.684e-02 3.228e+02 8.579 4.09e-16 ***
## exam_num3 3.863e-01 7.107e-02 3.413e+02 5.436 1.04e-07 ***
## exam_num4 5.436e-01 6.972e-02 3.359e+02 7.796 8.10e-14 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) PE exm_n2 exm_n3
## PE 0.254
## exam_num2 -0.671 -0.118
## exam_num3 -0.686 -0.331 0.479
## exam_num4 -0.677 -0.249 0.479 0.508
PHQ
low -> 0
med-low -> 1-4
med-high -> 5-8
high -> 9-25
Post Reveal RE ~ Actual Grade * PHQ
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: post_reveal_entropy ~ actual_grade * PHQ + (1 | subject)
## Data: fm
##
## REML criterion at convergence: 730.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.5230 -0.6549 -0.0616 0.5798 4.8135
##
## Random effects:
## Groups Name Variance Std.Dev.
## subject (Intercept) 0.008143 0.09024
## Residual 0.297532 0.54546
## Number of obs: 421, groups: subject, 125
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.119e+00 1.801e-01 2.617e+02 6.214 2.02e-09 ***
## actual_grade 4.721e-03 2.340e-03 2.604e+02 2.017 0.0447 *
## PHQ -7.271e-06 2.451e-02 3.490e+02 0.000 0.9998
## actual_grade:PHQ -1.296e-04 3.331e-04 3.380e+02 -0.389 0.6975
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) actl_g PHQ
## actual_grad -0.976
## PHQ -0.742 0.729
## actl_gr:PHQ 0.699 -0.719 -0.975
Grade Reveal Time ~ Exam number
##
## Call:
## lm(formula = grade_reveal_time ~ exam_num, data = fm)
##
## Residuals:
## Min 1Q Median 3Q Max
## -837.09 -31.20 -22.94 8.88 590.91
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 983.94 11.10 88.658 < 2e-16 ***
## exam_num2 -139.64 15.76 -8.859 < 2e-16 ***
## exam_num3 -138.55 15.98 -8.673 < 2e-16 ***
## exam_num4 -136.85 16.09 -8.505 2.69e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 121.1 on 451 degrees of freedom
## (15827 observations deleted due to missingness)
## Multiple R-squared: 0.203, Adjusted R-squared: 0.1977
## F-statistic: 38.28 on 3 and 451 DF, p-value: < 2.2e-16
Actual Grade ~ Exam number
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: actual_grade ~ exam_num + (1 | subject)
## Data: fm
##
## REML criterion at convergence: 3562
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.7123 -0.5002 0.1070 0.5163 2.8868
##
## Random effects:
## Groups Name Variance Std.Dev.
## subject (Intercept) 175.47 13.246
## Residual 89.98 9.486
## Number of obs: 452, groups: subject, 126
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 63.946 1.472 209.161 43.443 < 2e-16 ***
## exam_num2 5.273 1.259 322.983 4.189 3.61e-05 ***
## exam_num3 13.373 1.280 324.760 10.449 < 2e-16 ***
## exam_num4 11.610 1.296 326.984 8.962 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) exm_n2 exm_n3
## exam_num2 -0.417
## exam_num3 -0.409 0.488
## exam_num4 -0.408 0.486 0.491Post Reveal Entropy ~ Actual Grade + Grade Reveal Time
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: post_reveal_entropy ~ actual_grade + grade_reveal_time + (1 |
## subject)
## Data: fm
##
## REML criterion at convergence: 579.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -5.1387 -0.5884 -0.0411 0.5792 4.5133
##
## Random effects:
## Groups Name Variance Std.Dev.
## subject (Intercept) 0.009694 0.09846
## Residual 0.207098 0.45508
## Number of obs: 421, groups: subject, 125
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 3.493e+00 2.085e-01 3.917e+02 16.750 <2e-16 ***
## actual_grade 3.167e-04 1.399e-03 2.722e+02 0.226 0.821
## grade_reveal_time -2.374e-03 1.791e-04 4.180e+02 -13.252 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) actl_g
## actual_grad -0.663
## grad_rvl_tm -0.874 0.234