OSA analysis
1 participants info
LEARNING EXPS:
OSA 2: N = 102 (no exclusion due to no attention checking)
OSA 3: N = 221 (35 exclusion due to failing both attention checkers)
combined included subs N = 323
SURPRISE & EXPLANATION EXPS:
OSA1: N = 242 (qualtrics, half expected and half unexpected, has attention-checking questions)
OSA4: N = 80 (4 key trials and 9 fillers, has checker questions) (2 excluded)
combined included subs N = 322
2 LEARNING
2.1 all trials
OSA 2 & OSA 3
data_curve_analysis(OSA_learning_core, "score")Warning in jonckheere.test(data[[dv]], as.numeric(data$violation_number)): Sample size > 100 or data with ties
p-value based on normal approximation. Specify nperm for permutation p-value$model_summary
Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: formula_lmer
Data: data
REML criterion at convergence: 1689.6
Scaled residuals:
Min 1Q Median 3Q Max
-1.2124 -0.9506 -0.7410 0.9548 1.2166
Random effects:
Groups Name Variance Std.Dev.
subject (Intercept) 0.0150 0.1225
Residual 0.2351 0.4849
Number of obs: 1156, groups: subject, 289
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 0.48789 0.02942 1139.70738 16.585 <2e-16 ***
violation_number1 0.01384 0.04034 864.00000 0.343 0.732
violation_number2 0.04844 0.04034 864.00000 1.201 0.230
violation_number3 -0.02768 0.04034 864.00000 -0.686 0.493
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Correlation of Fixed Effects:
(Intr) vltn_1 vltn_2
viltn_nmbr1 -0.686
viltn_nmbr2 -0.686 0.500
viltn_nmbr3 -0.686 0.500 0.500
$emmeans
violation_number emmean SE df lower.CL upper.CL
0 0.488 0.0294 1140 0.414 0.561
1 0.502 0.0294 1140 0.428 0.575
2 0.536 0.0294 1140 0.463 0.610
3 0.460 0.0294 1140 0.387 0.534
Degrees-of-freedom method: kenward-roger
Confidence level used: 0.95
Conf-level adjustment: bonferroni method for 4 estimates
$pairwise_comparisons
contrast estimate SE df t.ratio p.value
violation_number0 - violation_number1 -0.0138 0.0403 864 -0.343 1.0000
violation_number0 - violation_number2 -0.0484 0.0403 864 -1.201 1.0000
violation_number0 - violation_number3 0.0277 0.0403 864 0.686 1.0000
violation_number1 - violation_number2 -0.0346 0.0403 864 -0.858 1.0000
violation_number1 - violation_number3 0.0415 0.0403 864 1.029 1.0000
violation_number2 - violation_number3 0.0761 0.0403 864 1.887 0.3568
Degrees-of-freedom method: kenward-roger
P value adjustment: holm method for 6 tests
$jonckheere_test
Jonckheere-Terpstra test
data:
JT = 248540, p-value = 0.7499
alternative hypothesis: two.sidedOSA2 only
data_curve_analysis(filter(OSA_learning_core, exp == "OSA2"), "score")boundary (singular) fit: see help('isSingular')Warning in jonckheere.test(data[[dv]], as.numeric(data$violation_number)): Sample size > 100 or data with ties
p-value based on normal approximation. Specify nperm for permutation p-value$model_summary
Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: formula_lmer
Data: data
REML criterion at convergence: 402.9
Scaled residuals:
Min 1Q Median 3Q Max
-1.2718 -1.0352 0.7394 0.8651 1.0352
Random effects:
Groups Name Variance Std.Dev.
subject (Intercept) 0.0000 0.0000
Residual 0.2472 0.4972
Number of obs: 272, groups: subject, 68
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 0.48529 0.06029 268.00000 8.049 2.73e-14 ***
violation_number1 0.14706 0.08527 268.00000 1.725 0.0857 .
violation_number2 0.10294 0.08527 268.00000 1.207 0.2284
violation_number3 0.02941 0.08527 268.00000 0.345 0.7304
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Correlation of Fixed Effects:
(Intr) vltn_1 vltn_2
viltn_nmbr1 -0.707
viltn_nmbr2 -0.707 0.500
viltn_nmbr3 -0.707 0.500 0.500
optimizer (nloptwrap) convergence code: 0 (OK)
boundary (singular) fit: see help('isSingular')
$emmeans
violation_number emmean SE df lower.CL upper.CL
0 0.485 0.0603 268 0.334 0.637
1 0.632 0.0603 268 0.481 0.784
2 0.588 0.0603 268 0.437 0.740
3 0.515 0.0603 268 0.363 0.666
Degrees-of-freedom method: kenward-roger
Confidence level used: 0.95
Conf-level adjustment: bonferroni method for 4 estimates
$pairwise_comparisons
contrast estimate SE df t.ratio p.value
violation_number0 - violation_number1 -0.1471 0.0853 201 -1.725 0.5167
violation_number0 - violation_number2 -0.1029 0.0853 201 -1.207 0.9150
violation_number0 - violation_number3 -0.0294 0.0853 201 -0.345 1.0000
violation_number1 - violation_number2 0.0441 0.0853 201 0.517 1.0000
violation_number1 - violation_number3 0.1176 0.0853 201 1.380 0.8460
violation_number2 - violation_number3 0.0735 0.0853 201 0.862 1.0000
Degrees-of-freedom method: kenward-roger
P value adjustment: holm method for 6 tests
$jonckheere_test
Jonckheere-Terpstra test
data:
JT = 13974, p-value = 0.8882
alternative hypothesis: two.sidedOSA3 only
data_curve_analysis(filter(OSA_learning_core, exp == "OSA3"), "score")Warning in jonckheere.test(data[[dv]], as.numeric(data$violation_number)): Sample size > 100 or data with ties
p-value based on normal approximation. Specify nperm for permutation p-value$model_summary
Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: formula_lmer
Data: data
REML criterion at convergence: 1290.4
Scaled residuals:
Min 1Q Median 3Q Max
-1.2340 -0.9373 -0.6648 0.9894 1.2861
Random effects:
Groups Name Variance Std.Dev.
subject (Intercept) 0.02027 0.1424
Residual 0.22955 0.4791
Number of obs: 884, groups: subject, 221
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 0.48869 0.03362 862.95230 14.535 <2e-16 ***
violation_number1 -0.02715 0.04558 659.99999 -0.596 0.552
violation_number2 0.03167 0.04558 659.99999 0.695 0.487
violation_number3 -0.04525 0.04558 659.99999 -0.993 0.321
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Correlation of Fixed Effects:
(Intr) vltn_1 vltn_2
viltn_nmbr1 -0.678
viltn_nmbr2 -0.678 0.500
viltn_nmbr3 -0.678 0.500 0.500
$emmeans
violation_number emmean SE df lower.CL upper.CL
0 0.489 0.0336 863 0.405 0.573
1 0.462 0.0336 863 0.377 0.546
2 0.520 0.0336 863 0.436 0.605
3 0.443 0.0336 863 0.359 0.528
Degrees-of-freedom method: kenward-roger
Confidence level used: 0.95
Conf-level adjustment: bonferroni method for 4 estimates
$pairwise_comparisons
contrast estimate SE df t.ratio p.value
violation_number0 - violation_number1 0.0271 0.0456 660 0.596 1.0000
violation_number0 - violation_number2 -0.0317 0.0456 660 -0.695 1.0000
violation_number0 - violation_number3 0.0452 0.0456 660 0.993 1.0000
violation_number1 - violation_number2 -0.0588 0.0456 660 -1.291 0.9865
violation_number1 - violation_number3 0.0181 0.0456 660 0.397 1.0000
violation_number2 - violation_number3 0.0769 0.0456 660 1.688 0.5516
Degrees-of-freedom method: kenward-roger
P value adjustment: holm method for 6 tests
$jonckheere_test
Jonckheere-Terpstra test
data:
JT = 144644, p-value = 0.6581
alternative hypothesis: two.sided2.2 first block (first three trials)
OSA 2 & OSA 3
data_curve_analysis(filter(OSA_learning_core, trial_id <= 3), "score")Warning in jonckheere.test(data[[dv]], as.numeric(data$violation_number)): Sample size > 100 or data with ties
p-value based on normal approximation. Specify nperm for permutation p-value$model_summary
Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: formula_lmer
Data: data
REML criterion at convergence: 850.8
Scaled residuals:
Min 1Q Median 3Q Max
-1.1500 -0.9106 -0.8336 1.0227 1.1408
Random effects:
Groups Name Variance Std.Dev.
subject (Intercept) 0.007668 0.08757
Residual 0.241433 0.49136
Number of obs: 578, groups: subject, 289
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 4.396e-01 4.427e-02 5.739e+02 9.930 <2e-16 ***
violation_number1 5.613e-02 6.020e-02 5.317e+02 0.932 0.3515
violation_number2 1.256e-01 5.985e-02 4.736e+02 2.098 0.0364 *
violation_number3 6.044e-03 5.950e-02 5.389e+02 0.102 0.9191
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Correlation of Fixed Effects:
(Intr) vltn_1 vltn_2
viltn_nmbr1 -0.730
viltn_nmbr2 -0.730 0.537
viltn_nmbr3 -0.739 0.546 0.545
$emmeans
violation_number emmean SE df lower.CL upper.CL
0 0.440 0.0444 574 0.328 0.551
1 0.496 0.0412 574 0.392 0.599
2 0.565 0.0410 574 0.463 0.668
3 0.446 0.0402 574 0.345 0.546
Degrees-of-freedom method: kenward-roger
Confidence level used: 0.95
Conf-level adjustment: bonferroni method for 4 estimates
$pairwise_comparisons
contrast estimate SE df t.ratio p.value
violation_number0 - violation_number1 -0.05614 0.0604 532 -0.930 1.0000
violation_number0 - violation_number2 -0.12559 0.0600 474 -2.093 0.2210
violation_number0 - violation_number3 -0.00604 0.0597 539 -0.101 1.0000
violation_number1 - violation_number2 -0.06945 0.0579 529 -1.199 0.9248
violation_number1 - violation_number3 0.05009 0.0572 456 0.876 1.0000
violation_number2 - violation_number3 0.11954 0.0571 510 2.093 0.2210
Degrees-of-freedom method: kenward-roger
P value adjustment: holm method for 6 tests
$jonckheere_test
Jonckheere-Terpstra test
data:
JT = 63033, p-value = 0.8226
alternative hypothesis: two.sidedOSA2 only
data_curve_analysis(filter(OSA_learning_core, trial_id <= 3, exp == "OSA2"), "score")boundary (singular) fit: see help('isSingular')Warning in jonckheere.test(data[[dv]], as.numeric(data$violation_number)): Sample size > 100 or data with ties
p-value based on normal approximation. Specify nperm for permutation p-value$model_summary
Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: formula_lmer
Data: data
REML criterion at convergence: 203.1
Scaled residuals:
Min 1Q Median 3Q Max
-1.4624 -0.9838 0.5571 0.8803 1.0445
Random effects:
Groups Name Variance Std.Dev.
subject (Intercept) 0.0000 0.0000
Residual 0.2452 0.4952
Number of obs: 136, groups: subject, 68
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 4.828e-01 9.195e-02 1.320e+02 5.250 5.92e-07 ***
violation_number1 8.134e-02 1.214e-01 1.320e+02 0.670 0.5041
violation_number2 2.414e-01 1.300e-01 1.320e+02 1.856 0.0657 .
violation_number3 4.421e-03 1.214e-01 1.320e+02 0.036 0.9710
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Correlation of Fixed Effects:
(Intr) vltn_1 vltn_2
viltn_nmbr1 -0.757
viltn_nmbr2 -0.707 0.536
viltn_nmbr3 -0.757 0.574 0.536
optimizer (nloptwrap) convergence code: 0 (OK)
boundary (singular) fit: see help('isSingular')
$emmeans
violation_number emmean SE df lower.CL upper.CL
0 0.483 0.0920 132 0.250 0.716
1 0.564 0.0793 132 0.363 0.765
2 0.724 0.0920 132 0.491 0.957
3 0.487 0.0793 132 0.286 0.688
Degrees-of-freedom method: kenward-roger
Confidence level used: 0.95
Conf-level adjustment: bonferroni method for 4 estimates
$pairwise_comparisons
contrast estimate SE df t.ratio p.value
violation_number0 - violation_number1 -0.08134 0.121 132 -0.670 1.0000
violation_number0 - violation_number2 -0.24138 0.130 66 -1.856 0.3395
violation_number0 - violation_number3 -0.00442 0.121 132 -0.036 1.0000
violation_number1 - violation_number2 -0.16004 0.121 132 -1.318 0.7591
violation_number1 - violation_number3 0.07692 0.112 66 0.686 1.0000
violation_number2 - violation_number3 0.23696 0.121 132 1.952 0.3187
Degrees-of-freedom method: kenward-roger
P value adjustment: holm method for 6 tests
$jonckheere_test
Jonckheere-Terpstra test
data:
JT = 3491, p-value = 0.8516
alternative hypothesis: two.sidedOSA3 only
data_curve_analysis(filter(OSA_learning_core, trial_id <= 3, exp == "OSA3"), "score")Warning in jonckheere.test(data[[dv]], as.numeric(data$violation_number)): Sample size > 100 or data with ties
p-value based on normal approximation. Specify nperm for permutation p-value$model_summary
Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: formula_lmer
Data: data
REML criterion at convergence: 653.3
Scaled residuals:
Min 1Q Median 3Q Max
-1.0854 -0.8890 -0.7937 1.0609 1.1706
Random effects:
Groups Name Variance Std.Dev.
subject (Intercept) 0.01159 0.1077
Residual 0.23793 0.4878
Number of obs: 442, groups: subject, 221
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 4.270e-01 5.042e-02 4.378e+02 8.468 3.81e-16 ***
violation_number1 4.318e-02 6.907e-02 3.823e+02 0.625 0.532
violation_number2 1.004e-01 6.749e-02 3.956e+02 1.488 0.138
violation_number3 4.872e-03 6.799e-02 3.923e+02 0.072 0.943
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Correlation of Fixed Effects:
(Intr) vltn_1 vltn_2
viltn_nmbr1 -0.719
viltn_nmbr2 -0.738 0.538
viltn_nmbr3 -0.732 0.533 0.549
$emmeans
violation_number emmean SE df lower.CL upper.CL
0 0.427 0.0506 438 0.300 0.554
1 0.470 0.0482 438 0.349 0.591
2 0.527 0.0457 438 0.413 0.642
3 0.432 0.0465 438 0.315 0.548
Degrees-of-freedom method: kenward-roger
Confidence level used: 0.95
Conf-level adjustment: bonferroni method for 4 estimates
$pairwise_comparisons
contrast estimate SE df t.ratio p.value
violation_number0 - violation_number1 -0.04318 0.0693 382 -0.623 1.0000
violation_number0 - violation_number2 -0.10042 0.0678 396 -1.482 0.8352
violation_number0 - violation_number3 -0.00487 0.0683 392 -0.071 1.0000
violation_number1 - violation_number2 -0.05723 0.0659 382 -0.868 1.0000
violation_number1 - violation_number3 0.03831 0.0665 391 0.576 1.0000
violation_number2 - violation_number3 0.09554 0.0646 361 1.480 0.8352
Degrees-of-freedom method: kenward-roger
P value adjustment: holm method for 6 tests
$jonckheere_test
Jonckheere-Terpstra test
data:
JT = 36802, p-value = 0.8719
alternative hypothesis: two.sided2.3 first trial
OSA 2 & OSA 3
data_curve_analysis(filter(OSA_learning_core, trial_id == 1), "score", use_lmer = FALSE)Warning in jonckheere.test(data[[dv]], as.numeric(data$violation_number)): Sample size > 100 or data with ties
p-value based on normal approximation. Specify nperm for permutation p-value$model_summary
Call:
lm(formula = formula_lm, data = data)
Residuals:
Min 1Q Median 3Q Max
-0.6140 -0.5957 0.3860 0.3966 0.5849
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.595745 0.072116 8.261 1.58e-14 ***
violation_number1 0.018290 0.097411 0.188 0.8512
violation_number2 0.007704 0.097031 0.079 0.9368
violation_number3 -0.180650 0.099059 -1.824 0.0696 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.4944 on 211 degrees of freedom
Multiple R-squared: 0.02731, Adjusted R-squared: 0.01348
F-statistic: 1.975 on 3 and 211 DF, p-value: 0.1188
$emmeans
violation_number emmean SE df lower.CL upper.CL
0 0.596 0.0721 211 0.414 0.777
1 0.614 0.0655 211 0.449 0.779
2 0.603 0.0649 211 0.440 0.767
3 0.415 0.0679 211 0.244 0.586
Confidence level used: 0.95
Conf-level adjustment: bonferroni method for 4 estimates
$pairwise_comparisons
contrast estimate SE df t.ratio p.value
violation_number0 - violation_number1 -0.0183 0.0974 211 -0.188 1.0000
violation_number0 - violation_number2 -0.0077 0.0970 211 -0.079 1.0000
violation_number0 - violation_number3 0.1807 0.0991 211 1.824 0.2785
violation_number1 - violation_number2 0.0106 0.0922 211 0.115 1.0000
violation_number1 - violation_number3 0.1989 0.0943 211 2.109 0.2169
violation_number2 - violation_number3 0.1884 0.0939 211 2.005 0.2313
P value adjustment: holm method for 6 tests
$jonckheere_test
Jonckheere-Terpstra test
data:
JT = 7851, p-value = 0.1178
alternative hypothesis: two.sidedOSA2 only
data_curve_analysis(filter(OSA_learning_core, trial_id == 1, exp == "OSA2"), "score", use_lmer = FALSE)Warning in jonckheere.test(data[[dv]], as.numeric(data$violation_number)): Sample size > 100 or data with ties
p-value based on normal approximation. Specify nperm for permutation p-value$model_summary
Call:
lm(formula = formula_lm, data = data)
Residuals:
Min 1Q Median 3Q Max
-0.6667 -0.5600 0.3431 0.3571 0.4400
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.66667 0.14399 4.630 1.84e-05 ***
violation_number1 -0.10667 0.17517 -0.609 0.545
violation_number2 -0.01961 0.18807 -0.104 0.917
violation_number3 -0.02381 0.19623 -0.121 0.904
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.4988 on 64 degrees of freedom
Multiple R-squared: 0.008439, Adjusted R-squared: -0.03804
F-statistic: 0.1816 on 3 and 64 DF, p-value: 0.9085
$emmeans
violation_number emmean SE df lower.CL upper.CL
0 0.667 0.1440 64 0.297 1.037
1 0.560 0.0998 64 0.304 0.816
2 0.647 0.1210 64 0.336 0.958
3 0.643 0.1330 64 0.300 0.985
Confidence level used: 0.95
Conf-level adjustment: bonferroni method for 4 estimates
$pairwise_comparisons
contrast estimate SE df t.ratio p.value
violation_number0 - violation_number1 0.1067 0.175 64 0.609 1.0000
violation_number0 - violation_number2 0.0196 0.188 64 0.104 1.0000
violation_number0 - violation_number3 0.0238 0.196 64 0.121 1.0000
violation_number1 - violation_number2 -0.0871 0.157 64 -0.555 1.0000
violation_number1 - violation_number3 -0.0829 0.167 64 -0.498 1.0000
violation_number2 - violation_number3 0.0042 0.180 64 0.023 1.0000
P value adjustment: holm method for 6 tests
$jonckheere_test
Jonckheere-Terpstra test
data:
JT = 855, p-value = 0.8901
alternative hypothesis: two.sidedOSA3 only
data_curve_analysis(filter(OSA_learning_core, trial_id == 1, exp == "OSA3"), "score", use_lmer = FALSE)Warning in jonckheere.test(data[[dv]], as.numeric(data$violation_number)): Sample size > 100 or data with ties
p-value based on normal approximation. Specify nperm for permutation p-value$model_summary
Call:
lm(formula = formula_lm, data = data)
Residuals:
Min 1Q Median 3Q Max
-0.6562 -0.5714 0.3438 0.4146 0.6667
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.57143 0.08291 6.892 1.63e-10 ***
violation_number1 0.08482 0.11997 0.707 0.4807
violation_number2 0.01394 0.11289 0.123 0.9019
violation_number3 -0.23810 0.11421 -2.085 0.0389 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.4905 on 143 degrees of freedom
Multiple R-squared: 0.0602, Adjusted R-squared: 0.04049
F-statistic: 3.054 on 3 and 143 DF, p-value: 0.03051
$emmeans
violation_number emmean SE df lower.CL upper.CL
0 0.571 0.0829 143 0.362 0.781
1 0.656 0.0867 143 0.437 0.876
2 0.585 0.0766 143 0.392 0.779
3 0.333 0.0785 143 0.135 0.532
Confidence level used: 0.95
Conf-level adjustment: bonferroni method for 4 estimates
$pairwise_comparisons
contrast estimate SE df t.ratio p.value
violation_number0 - violation_number1 -0.0848 0.120 143 -0.707 1.0000
violation_number0 - violation_number2 -0.0139 0.113 143 -0.123 1.0000
violation_number0 - violation_number3 0.2381 0.114 143 2.085 0.1555
violation_number1 - violation_number2 0.0709 0.116 143 0.613 1.0000
violation_number1 - violation_number3 0.3229 0.117 143 2.760 0.0392
violation_number2 - violation_number3 0.2520 0.110 143 2.297 0.1153
P value adjustment: holm method for 6 tests
$jonckheere_test
Jonckheere-Terpstra test
data:
JT = 3485, p-value = 0.05463
alternative hypothesis: two.sided3 SURPRISE
3.1 preprocessing
3.2 plots and models
Coordinate system already present. Adding new coordinate system, which will
replace the existing one.
Coordinate system already present. Adding new coordinate system, which will
replace the existing one.
Coordinate system already present. Adding new coordinate system, which will
replace the existing one.
OSA 1 & OSA 4
data_curve_analysis(OSA_surprise_core, "surprise")Warning in jonckheere.test(data[[dv]], as.numeric(data$violation_number)): Sample size > 100 or data with ties
p-value based on normal approximation. Specify nperm for permutation p-value$model_summary
Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: formula_lmer
Data: data
REML criterion at convergence: 4692.9
Scaled residuals:
Min 1Q Median 3Q Max
-2.75591 -0.67017 -0.03989 0.71665 3.10394
Random effects:
Groups Name Variance Std.Dev.
subject (Intercept) 0.8476 0.9207
Residual 1.6431 1.2818
Number of obs: 1296, groups: subject, 324
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 1.23148 0.08768 958.85926 14.05 <2e-16 ***
violation_number1 2.47531 0.10071 969.00000 24.58 <2e-16 ***
violation_number2 3.18827 0.10071 969.00000 31.66 <2e-16 ***
violation_number3 3.48148 0.10071 969.00000 34.57 <2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Correlation of Fixed Effects:
(Intr) vltn_1 vltn_2
viltn_nmbr1 -0.574
viltn_nmbr2 -0.574 0.500
viltn_nmbr3 -0.574 0.500 0.500
$emmeans
violation_number emmean SE df lower.CL upper.CL
0 1.23 0.0877 959 1.01 1.45
1 3.71 0.0877 959 3.49 3.93
2 4.42 0.0877 959 4.20 4.64
3 4.71 0.0877 959 4.49 4.93
Degrees-of-freedom method: kenward-roger
Confidence level used: 0.95
Conf-level adjustment: bonferroni method for 4 estimates
$pairwise_comparisons
contrast estimate SE df t.ratio p.value
violation_number0 - violation_number1 -2.475 0.101 969 -24.578 <.0001
violation_number0 - violation_number2 -3.188 0.101 969 -31.658 <.0001
violation_number0 - violation_number3 -3.481 0.101 969 -34.569 <.0001
violation_number1 - violation_number2 -0.713 0.101 969 -7.079 <.0001
violation_number1 - violation_number3 -1.006 0.101 969 -9.991 <.0001
violation_number2 - violation_number3 -0.293 0.101 969 -2.911 0.0037
Degrees-of-freedom method: kenward-roger
P value adjustment: holm method for 6 tests
$jonckheere_test
Jonckheere-Terpstra test
data:
JT = 487938, p-value < 2.2e-16
alternative hypothesis: two.sidedOSA 1 only
data_curve_analysis(filter(OSA_surprise_core, exp == "OSA1"), "surprise")Warning in jonckheere.test(data[[dv]], as.numeric(data$violation_number)): Sample size > 100 or data with ties
p-value based on normal approximation. Specify nperm for permutation p-value$model_summary
Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: formula_lmer
Data: data
REML criterion at convergence: 3478.1
Scaled residuals:
Min 1Q Median 3Q Max
-2.63159 -0.60793 -0.00032 0.66600 3.09224
Random effects:
Groups Name Variance Std.Dev.
subject (Intercept) 0.7776 0.8818
Residual 1.6141 1.2705
Number of obs: 968, groups: subject, 242
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 1.19835 0.09941 731.90328 12.05 <2e-16 ***
violation_number1 2.61570 0.11550 723.00000 22.65 <2e-16 ***
violation_number2 3.38017 0.11550 723.00000 29.27 <2e-16 ***
violation_number3 3.72314 0.11550 723.00000 32.23 <2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Correlation of Fixed Effects:
(Intr) vltn_1 vltn_2
viltn_nmbr1 -0.581
viltn_nmbr2 -0.581 0.500
viltn_nmbr3 -0.581 0.500 0.500
$emmeans
violation_number emmean SE df lower.CL upper.CL
0 1.20 0.0994 732 0.949 1.45
1 3.81 0.0994 732 3.565 4.06
2 4.58 0.0994 732 4.330 4.83
3 4.92 0.0994 732 4.673 5.17
Degrees-of-freedom method: kenward-roger
Confidence level used: 0.95
Conf-level adjustment: bonferroni method for 4 estimates
$pairwise_comparisons
contrast estimate SE df t.ratio p.value
violation_number0 - violation_number1 -2.616 0.115 723 -22.647 <.0001
violation_number0 - violation_number2 -3.380 0.115 723 -29.266 <.0001
violation_number0 - violation_number3 -3.723 0.115 723 -32.235 <.0001
violation_number1 - violation_number2 -0.764 0.115 723 -6.619 <.0001
violation_number1 - violation_number3 -1.107 0.115 723 -9.588 <.0001
violation_number2 - violation_number3 -0.343 0.115 723 -2.970 0.0031
Degrees-of-freedom method: kenward-roger
P value adjustment: holm method for 6 tests
$jonckheere_test
Jonckheere-Terpstra test
data:
JT = 276668, p-value < 2.2e-16
alternative hypothesis: two.sidedOSA 4 only
data_curve_analysis(filter(OSA_surprise_core, exp == "OSA4"), "surprise")Warning in jonckheere.test(data[[dv]], as.numeric(data$violation_number)): Sample size > 100 or data with ties
p-value based on normal approximation. Specify nperm for permutation p-value$model_summary
Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: formula_lmer
Data: data
REML criterion at convergence: 1191.7
Scaled residuals:
Min 1Q Median 3Q Max
-2.46457 -0.66872 -0.02504 0.53703 2.80926
Random effects:
Groups Name Variance Std.Dev.
subject (Intercept) 0.9547 0.9771
Residual 1.6214 1.2733
Number of obs: 328, groups: subject, 82
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 1.3293 0.1772 229.4576 7.50 1.39e-12 ***
violation_number1 2.0610 0.1989 243.0000 10.36 < 2e-16 ***
violation_number2 2.6220 0.1989 243.0000 13.19 < 2e-16 ***
violation_number3 2.7683 0.1989 243.0000 13.92 < 2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Correlation of Fixed Effects:
(Intr) vltn_1 vltn_2
viltn_nmbr1 -0.561
viltn_nmbr2 -0.561 0.500
viltn_nmbr3 -0.561 0.500 0.500
$emmeans
violation_number emmean SE df lower.CL upper.CL
0 1.33 0.177 229 0.883 1.78
1 3.39 0.177 229 2.944 3.84
2 3.95 0.177 229 3.505 4.40
3 4.10 0.177 229 3.651 4.54
Degrees-of-freedom method: kenward-roger
Confidence level used: 0.95
Conf-level adjustment: bonferroni method for 4 estimates
$pairwise_comparisons
contrast estimate SE df t.ratio p.value
violation_number0 - violation_number1 -2.061 0.199 243 -10.364 <.0001
violation_number0 - violation_number2 -2.622 0.199 243 -13.185 <.0001
violation_number0 - violation_number3 -2.768 0.199 243 -13.921 <.0001
violation_number1 - violation_number2 -0.561 0.199 243 -2.821 0.0104
violation_number1 - violation_number3 -0.707 0.199 243 -3.557 0.0014
violation_number2 - violation_number3 -0.146 0.199 243 -0.736 0.4625
Degrees-of-freedom method: kenward-roger
P value adjustment: holm method for 6 tests
$jonckheere_test
Jonckheere-Terpstra test
data:
JT = 29824, p-value < 2.2e-16
alternative hypothesis: two.sided4 EXPLANATION
4.1 NUMBER OF EXPLANATION
4.1.1 all explanations included
Warning: Removed 2 rows containing non-finite outside the scale range
(`stat_summary()`).
Removed 2 rows containing non-finite outside the scale range
(`stat_summary()`).Warning: Removed 1 row containing non-finite outside the scale range (`stat_summary()`).
Removed 1 row containing non-finite outside the scale range (`stat_summary()`).
Removed 1 row containing non-finite outside the scale range (`stat_summary()`).
Removed 1 row containing non-finite outside the scale range (`stat_summary()`).
OSA 1 & OSA 4
data_curve_analysis(OSA_explan_core, "explanation_num")Warning in jonckheere.test(data[[dv]], as.numeric(data$violation_number)): Sample size > 100 or data with ties
p-value based on normal approximation. Specify nperm for permutation p-value$model_summary
Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: formula_lmer
Data: data
REML criterion at convergence: 1319
Scaled residuals:
Min 1Q Median 3Q Max
-2.5850 -0.0819 -0.0292 0.4344 4.5803
Random effects:
Groups Name Variance Std.Dev.
subject (Intercept) 0.05712 0.2390
Residual 0.12294 0.3506
Number of obs: 1294, groups: subject, 324
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 0.65338 0.02360 993.40235 27.680 <2e-16 ***
violation_number1 0.28489 0.02757 967.71095 10.332 <2e-16 ***
violation_number2 0.27872 0.02757 967.71095 10.108 <2e-16 ***
violation_number3 0.26641 0.02760 968.08540 9.653 <2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Correlation of Fixed Effects:
(Intr) vltn_1 vltn_2
viltn_nmbr1 -0.585
viltn_nmbr2 -0.585 0.501
viltn_nmbr3 -0.585 0.500 0.500
$emmeans
violation_number emmean SE df lower.CL upper.CL
0 0.653 0.0236 993 0.594 0.712
1 0.938 0.0236 991 0.879 0.997
2 0.932 0.0236 991 0.873 0.991
3 0.920 0.0236 993 0.861 0.979
Degrees-of-freedom method: kenward-roger
Confidence level used: 0.95
Conf-level adjustment: bonferroni method for 4 estimates
$pairwise_comparisons
contrast estimate SE df t.ratio p.value
violation_number0 - violation_number1 -0.28489 0.0276 967 -10.332 <.0001
violation_number0 - violation_number2 -0.27872 0.0276 967 -10.108 <.0001
violation_number0 - violation_number3 -0.26641 0.0276 968 -9.653 <.0001
violation_number1 - violation_number2 0.00617 0.0275 967 0.224 1.0000
violation_number1 - violation_number3 0.01848 0.0276 967 0.670 1.0000
violation_number2 - violation_number3 0.01231 0.0276 967 0.446 1.0000
Degrees-of-freedom method: kenward-roger
P value adjustment: holm method for 6 tests
$jonckheere_test
Jonckheere-Terpstra test
data:
JT = 353566, p-value = 1.359e-07
alternative hypothesis: two.sidedOSA 1 only
data_curve_analysis(filter(OSA_explan_core, exp == "OSA1"), "explanation_num")Warning in jonckheere.test(data[[dv]], as.numeric(data$violation_number)): Sample size > 100 or data with ties
p-value based on normal approximation. Specify nperm for permutation p-value$model_summary
Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: formula_lmer
Data: data
REML criterion at convergence: 1127.4
Scaled residuals:
Min 1Q Median 3Q Max
-2.2858 -0.0718 -0.0214 0.3934 4.5101
Random effects:
Groups Name Variance Std.Dev.
subject (Intercept) 0.06114 0.2473
Residual 0.14396 0.3794
Number of obs: 967, groups: subject, 242
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 0.53552 0.02916 762.43756 18.36 <2e-16 ***
violation_number1 0.35704 0.03453 722.57683 10.34 <2e-16 ***
violation_number2 0.36531 0.03453 722.57683 10.58 <2e-16 ***
violation_number3 0.34878 0.03453 722.57683 10.10 <2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Correlation of Fixed Effects:
(Intr) vltn_1 vltn_2
viltn_nmbr1 -0.594
viltn_nmbr2 -0.594 0.501
viltn_nmbr3 -0.594 0.501 0.501
$emmeans
violation_number emmean SE df lower.CL upper.CL
0 0.536 0.0292 762 0.463 0.609
1 0.893 0.0291 760 0.820 0.965
2 0.901 0.0291 760 0.828 0.974
3 0.884 0.0291 760 0.811 0.957
Degrees-of-freedom method: kenward-roger
Confidence level used: 0.95
Conf-level adjustment: bonferroni method for 4 estimates
$pairwise_comparisons
contrast estimate SE df t.ratio p.value
violation_number0 - violation_number1 -0.35704 0.0345 722 -10.339 <.0001
violation_number0 - violation_number2 -0.36531 0.0345 722 -10.578 <.0001
violation_number0 - violation_number3 -0.34878 0.0345 722 -10.099 <.0001
violation_number1 - violation_number2 -0.00826 0.0345 722 -0.240 1.0000
violation_number1 - violation_number3 0.00826 0.0345 722 0.240 1.0000
violation_number2 - violation_number3 0.01653 0.0345 722 0.479 1.0000
Degrees-of-freedom method: kenward-roger
P value adjustment: holm method for 6 tests
$jonckheere_test
Jonckheere-Terpstra test
data:
JT = 205154, p-value = 8.124e-10
alternative hypothesis: two.sidedOSA 4 only
data_curve_analysis(filter(OSA_explan_core, exp == "OSA4"), "explanation_num")Warning in jonckheere.test(data[[dv]], as.numeric(data$violation_number)): Sample size > 100 or data with ties
p-value based on normal approximation. Specify nperm for permutation p-value$model_summary
Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: formula_lmer
Data: data
REML criterion at convergence: -26.7
Scaled residuals:
Min 1Q Median 3Q Max
-4.4984 -0.2778 -0.0415 0.0776 7.1291
Random effects:
Groups Name Variance Std.Dev.
subject (Intercept) 0.01164 0.1079
Residual 0.04241 0.2059
Number of obs: 327, groups: subject, 82
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 1.00000 0.02567 283.78402 38.951 <2e-16 ***
violation_number1 0.07317 0.03216 242.24543 2.275 0.0238 *
violation_number2 0.02439 0.03216 242.24543 0.758 0.4490
violation_number3 0.02451 0.03228 242.71322 0.759 0.4483
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Correlation of Fixed Effects:
(Intr) vltn_1 vltn_2
viltn_nmbr1 -0.626
viltn_nmbr2 -0.626 0.500
viltn_nmbr3 -0.624 0.498 0.498
$emmeans
violation_number emmean SE df lower.CL upper.CL
0 1.00 0.0257 284 0.935 1.06
1 1.07 0.0257 284 1.009 1.14
2 1.02 0.0257 284 0.960 1.09
3 1.02 0.0258 285 0.960 1.09
Degrees-of-freedom method: kenward-roger
Confidence level used: 0.95
Conf-level adjustment: bonferroni method for 4 estimates
$pairwise_comparisons
contrast estimate SE df t.ratio p.value
violation_number0 - violation_number1 -0.07317 0.0322 242 -2.275 0.1426
violation_number0 - violation_number2 -0.02439 0.0322 242 -0.758 1.0000
violation_number0 - violation_number3 -0.02451 0.0323 242 -0.759 1.0000
violation_number1 - violation_number2 0.04878 0.0322 242 1.517 0.6532
violation_number1 - violation_number3 0.04866 0.0323 242 1.508 0.6532
violation_number2 - violation_number3 -0.00012 0.0323 242 -0.004 1.0000
Degrees-of-freedom method: kenward-roger
P value adjustment: holm method for 6 tests
$jonckheere_test
Jonckheere-Terpstra test
data:
JT = 20017, p-value = 0.9733
alternative hypothesis: two.sided4.1.2 no give up answers
Warning: Removed 2 rows containing non-finite outside the scale range
(`stat_summary()`).
Removed 2 rows containing non-finite outside the scale range
(`stat_summary()`).Warning: Removed 1 row containing non-finite outside the scale range (`stat_summary()`).
Removed 1 row containing non-finite outside the scale range (`stat_summary()`).
Removed 1 row containing non-finite outside the scale range (`stat_summary()`).
Removed 1 row containing non-finite outside the scale range (`stat_summary()`).
OSA 1 & OSA 4
data_curve_analysis(filter(OSA_explan_core, giveup == FALSE), "explanation_num")Warning in jonckheere.test(data[[dv]], as.numeric(data$violation_number)): Sample size > 100 or data with ties
p-value based on normal approximation. Specify nperm for permutation p-value$model_summary
Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: formula_lmer
Data: data
REML criterion at convergence: 90.1
Scaled residuals:
Min 1Q Median 3Q Max
-4.0959 -0.2651 -0.0803 0.0257 6.2645
Random effects:
Groups Name Variance Std.Dev.
subject (Intercept) 0.02145 0.1465
Residual 0.04853 0.2203
Number of obs: 751, groups: subject, 282
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 1.00888 0.01830 694.01560 55.140 < 2e-16 ***
violation_number1 0.08071 0.02314 557.70006 3.488 0.000526 ***
violation_number2 0.04011 0.02341 560.47616 1.714 0.087146 .
violation_number3 0.01663 0.02392 558.25970 0.695 0.487136
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Correlation of Fixed Effects:
(Intr) vltn_1 vltn_2
viltn_nmbr1 -0.616
viltn_nmbr2 -0.609 0.501
viltn_nmbr3 -0.592 0.489 0.493
$emmeans
violation_number emmean SE df lower.CL upper.CL
0 1.01 0.0183 701 0.963 1.05
1 1.09 0.0187 703 1.043 1.14
2 1.05 0.0190 708 1.001 1.10
3 1.03 0.0197 721 0.976 1.07
Degrees-of-freedom method: kenward-roger
Confidence level used: 0.95
Conf-level adjustment: bonferroni method for 4 estimates
$pairwise_comparisons
contrast estimate SE df t.ratio p.value
violation_number0 - violation_number1 -0.0807 0.0232 578 -3.485 0.0032
violation_number0 - violation_number2 -0.0401 0.0234 581 -1.712 0.3266
violation_number0 - violation_number3 -0.0166 0.0239 579 -0.695 0.6503
violation_number1 - violation_number2 0.0406 0.0233 542 1.744 0.3266
violation_number1 - violation_number3 0.0641 0.0238 544 2.690 0.0368
violation_number2 - violation_number3 0.0235 0.0238 526 0.985 0.6503
Degrees-of-freedom method: kenward-roger
P value adjustment: holm method for 6 tests
$jonckheere_test
Jonckheere-Terpstra test
data:
JT = 105853, p-value = 0.9461
alternative hypothesis: two.sidedOSA 1 only
data_curve_analysis(filter(OSA_explan_core, giveup == FALSE, exp == "OSA1"), "explanation_num")Warning in jonckheere.test(data[[dv]], as.numeric(data$violation_number)): Sample size > 100 or data with ties
p-value based on normal approximation. Specify nperm for permutation p-value$model_summary
Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: formula_lmer
Data: data
REML criterion at convergence: 92
Scaled residuals:
Min 1Q Median 3Q Max
-3.5260 -0.2519 -0.0855 0.0397 6.0071
Random effects:
Groups Name Variance Std.Dev.
subject (Intercept) 0.02828 0.1682
Residual 0.04824 0.2196
Number of obs: 460, groups: subject, 200
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 1.01309 0.02438 436.17550 41.556 <2e-16 ***
violation_number1 0.08010 0.03049 343.46990 2.627 0.009 **
violation_number2 0.04708 0.03095 344.72466 1.521 0.129
violation_number3 0.01170 0.03201 343.29845 0.366 0.715
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Correlation of Fixed Effects:
(Intr) vltn_1 vltn_2
viltn_nmbr1 -0.630
viltn_nmbr2 -0.621 0.529
viltn_nmbr3 -0.597 0.514 0.522
$emmeans
violation_number emmean SE df lower.CL upper.CL
0 1.01 0.0244 440 0.952 1.07
1 1.09 0.0243 438 1.032 1.15
2 1.06 0.0248 441 0.998 1.12
3 1.02 0.0263 450 0.959 1.09
Degrees-of-freedom method: kenward-roger
Confidence level used: 0.95
Conf-level adjustment: bonferroni method for 4 estimates
$pairwise_comparisons
contrast estimate SE df t.ratio p.value
violation_number0 - violation_number1 -0.0801 0.0305 360 -2.623 0.0546
violation_number0 - violation_number2 -0.0471 0.0310 361 -1.519 0.5190
violation_number0 - violation_number3 -0.0117 0.0321 360 -0.365 0.7559
violation_number1 - violation_number2 0.0330 0.0299 318 1.106 0.7559
violation_number1 - violation_number3 0.0684 0.0309 317 2.215 0.1375
violation_number2 - violation_number3 0.0354 0.0308 302 1.148 0.7559
Degrees-of-freedom method: kenward-roger
P value adjustment: holm method for 6 tests
$jonckheere_test
Jonckheere-Terpstra test
data:
JT = 39716, p-value = 0.949
alternative hypothesis: two.sidedOSA 4 only
data_curve_analysis(filter(OSA_explan_core, giveup == FALSE, exp == "OSA4"), "explanation_num")Warning in jonckheere.test(data[[dv]], as.numeric(data$violation_number)): Sample size > 100 or data with ties
p-value based on normal approximation. Specify nperm for permutation p-value$model_summary
Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: formula_lmer
Data: data
REML criterion at convergence: 10.9
Scaled residuals:
Min 1Q Median 3Q Max
-4.2859 -0.2930 -0.0443 0.0741 6.7585
Random effects:
Groups Name Variance Std.Dev.
subject (Intercept) 0.01273 0.1128
Residual 0.04755 0.2181
Number of obs: 291, groups: subject, 82
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 0.99988 0.02727 258.80613 36.668 <2e-16 ***
violation_number1 0.08144 0.03566 218.12596 2.284 0.0233 *
violation_number2 0.02720 0.03586 220.00399 0.759 0.4489
violation_number3 0.02665 0.03601 220.17750 0.740 0.4601
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Correlation of Fixed Effects:
(Intr) vltn_1 vltn_2
viltn_nmbr1 -0.604
viltn_nmbr2 -0.602 0.466
viltn_nmbr3 -0.599 0.461 0.462
$emmeans
violation_number emmean SE df lower.CL upper.CL
0 1.00 0.0273 257 0.931 1.07
1 1.08 0.0290 266 1.008 1.15
2 1.03 0.0292 267 0.954 1.10
3 1.03 0.0294 268 0.953 1.10
Degrees-of-freedom method: kenward-roger
Confidence level used: 0.95
Conf-level adjustment: bonferroni method for 4 estimates
$pairwise_comparisons
contrast estimate SE df t.ratio p.value
violation_number0 - violation_number1 -0.081440 0.0357 214 -2.283 0.1404
violation_number0 - violation_number2 -0.027198 0.0359 216 -0.758 1.0000
violation_number0 - violation_number3 -0.026645 0.0360 217 -0.740 1.0000
violation_number1 - violation_number2 0.054242 0.0370 214 1.467 0.7126
violation_number1 - violation_number3 0.054795 0.0372 217 1.472 0.7126
violation_number2 - violation_number3 0.000553 0.0373 215 0.015 1.0000
Degrees-of-freedom method: kenward-roger
P value adjustment: holm method for 6 tests
$jonckheere_test
Jonckheere-Terpstra test
data:
JT = 15880, p-value = 0.9751
alternative hypothesis: two.sided4.2 EVALUATION OF EXPLANATIONS
4.2.1 all explanations included
Warning: Removed 1 row containing non-finite outside the scale range (`stat_summary()`).
Removed 1 row containing non-finite outside the scale range (`stat_summary()`).
Removed 1 row containing non-finite outside the scale range (`stat_summary()`).
Removed 1 row containing non-finite outside the scale range (`stat_summary()`).
OSA 1 & OSA 4
data_curve_analysis(OSA_explan_core, "explanation_eval")Warning in jonckheere.test(data[[dv]], as.numeric(data$violation_number)): Sample size > 100 or data with ties
p-value based on normal approximation. Specify nperm for permutation p-value$model_summary
Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: formula_lmer
Data: data
REML criterion at convergence: 4939.6
Scaled residuals:
Min 1Q Median 3Q Max
-2.1030 -0.6957 -0.1337 0.7501 2.5690
Random effects:
Groups Name Variance Std.Dev.
subject (Intercept) 0.8723 0.934
Residual 2.0598 1.435
Number of obs: 1295, groups: subject, 324
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 1.73148 0.09513 1019.96941 18.201 < 2e-16 ***
violation_number1 0.46605 0.11276 967.61932 4.133 3.89e-05 ***
violation_number2 -0.13889 0.11276 967.61932 -1.232 0.218
violation_number3 -0.47387 0.11286 968.01058 -4.199 2.93e-05 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Correlation of Fixed Effects:
(Intr) vltn_1 vltn_2
viltn_nmbr1 -0.593
viltn_nmbr2 -0.593 0.500
viltn_nmbr3 -0.592 0.500 0.500
$emmeans
violation_number emmean SE df lower.CL upper.CL
0 1.73 0.0951 1020 1.49 1.97
1 2.20 0.0951 1020 1.96 2.44
2 1.59 0.0951 1020 1.35 1.83
3 1.26 0.0953 1022 1.02 1.50
Degrees-of-freedom method: kenward-roger
Confidence level used: 0.95
Conf-level adjustment: bonferroni method for 4 estimates
$pairwise_comparisons
contrast estimate SE df t.ratio p.value
violation_number0 - violation_number1 -0.466 0.113 968 -4.133 0.0001
violation_number0 - violation_number2 0.139 0.113 968 1.232 0.2184
violation_number0 - violation_number3 0.474 0.113 968 4.199 0.0001
violation_number1 - violation_number2 0.605 0.113 968 5.365 <.0001
violation_number1 - violation_number3 0.940 0.113 968 8.328 <.0001
violation_number2 - violation_number3 0.335 0.113 968 2.968 0.0061
Degrees-of-freedom method: kenward-roger
P value adjustment: holm method for 6 tests
$jonckheere_test
Jonckheere-Terpstra test
data:
JT = 284360, p-value = 6.384e-05
alternative hypothesis: two.sidedOSA 1 only
data_curve_analysis(filter(OSA_explan_core, exp == "OSA1"), "explanation_eval")Warning in jonckheere.test(data[[dv]], as.numeric(data$violation_number)): Sample size > 100 or data with ties
p-value based on normal approximation. Specify nperm for permutation p-value$model_summary
Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: formula_lmer
Data: data
REML criterion at convergence: 3704.3
Scaled residuals:
Min 1Q Median 3Q Max
-1.9280 -0.6998 -0.3243 0.7641 2.5307
Random effects:
Groups Name Variance Std.Dev.
subject (Intercept) 0.7456 0.8635
Residual 2.1475 1.4654
Number of obs: 968, groups: subject, 242
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 1.42975 0.10934 803.81919 13.076 < 2e-16 ***
violation_number1 0.49174 0.13322 723.00000 3.691 0.00024 ***
violation_number2 -0.02479 0.13322 723.00000 -0.186 0.85241
violation_number3 -0.40496 0.13322 723.00000 -3.040 0.00245 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Correlation of Fixed Effects:
(Intr) vltn_1 vltn_2
viltn_nmbr1 -0.609
viltn_nmbr2 -0.609 0.500
viltn_nmbr3 -0.609 0.500 0.500
$emmeans
violation_number emmean SE df lower.CL upper.CL
0 1.43 0.109 804 1.156 1.70
1 1.92 0.109 804 1.648 2.20
2 1.40 0.109 804 1.131 1.68
3 1.02 0.109 804 0.751 1.30
Degrees-of-freedom method: kenward-roger
Confidence level used: 0.95
Conf-level adjustment: bonferroni method for 4 estimates
$pairwise_comparisons
contrast estimate SE df t.ratio p.value
violation_number0 - violation_number1 -0.4917 0.133 723 -3.691 0.0010
violation_number0 - violation_number2 0.0248 0.133 723 0.186 0.8524
violation_number0 - violation_number3 0.4050 0.133 723 3.040 0.0074
violation_number1 - violation_number2 0.5165 0.133 723 3.877 0.0006
violation_number1 - violation_number3 0.8967 0.133 723 6.731 <.0001
violation_number2 - violation_number3 0.3802 0.133 723 2.854 0.0089
Degrees-of-freedom method: kenward-roger
P value adjustment: holm method for 6 tests
$jonckheere_test
Jonckheere-Terpstra test
data:
JT = 162100, p-value = 0.005192
alternative hypothesis: two.sidedOSA 4 only
data_curve_analysis(filter(OSA_explan_core, exp == "OSA4"), "explanation_eval")Warning in jonckheere.test(data[[dv]], as.numeric(data$violation_number)): Sample size > 100 or data with ties
p-value based on normal approximation. Specify nperm for permutation p-value$model_summary
Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: formula_lmer
Data: data
REML criterion at convergence: 1186.2
Scaled residuals:
Min 1Q Median 3Q Max
-2.3788 -0.7247 0.0771 0.7473 2.2556
Random effects:
Groups Name Variance Std.Dev.
subject (Intercept) 0.5334 0.7304
Residual 1.7929 1.3390
Number of obs: 327, groups: subject, 82
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 2.6220 0.1684 279.0081 15.567 < 2e-16 ***
violation_number1 0.3902 0.2091 241.8397 1.866 0.06323 .
violation_number2 -0.4756 0.2091 241.8397 -2.274 0.02382 *
violation_number3 -0.6755 0.2099 242.2945 -3.219 0.00146 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Correlation of Fixed Effects:
(Intr) vltn_1 vltn_2
viltn_nmbr1 -0.621
viltn_nmbr2 -0.621 0.500
viltn_nmbr3 -0.619 0.498 0.498
$emmeans
violation_number emmean SE df lower.CL upper.CL
0 2.62 0.168 279 2.20 3.05
1 3.01 0.168 279 2.59 3.44
2 2.15 0.168 279 1.72 2.57
3 1.95 0.169 281 1.52 2.37
Degrees-of-freedom method: kenward-roger
Confidence level used: 0.95
Conf-level adjustment: bonferroni method for 4 estimates
$pairwise_comparisons
contrast estimate SE df t.ratio p.value
violation_number0 - violation_number1 -0.390 0.209 242 -1.866 0.1265
violation_number0 - violation_number2 0.476 0.209 242 2.274 0.0715
violation_number0 - violation_number3 0.675 0.210 242 3.219 0.0059
violation_number1 - violation_number2 0.866 0.209 242 4.141 0.0002
violation_number1 - violation_number3 1.066 0.210 242 5.078 <.0001
violation_number2 - violation_number3 0.200 0.210 242 0.952 0.3418
Degrees-of-freedom method: kenward-roger
P value adjustment: holm method for 6 tests
$jonckheere_test
Jonckheere-Terpstra test
data:
JT = 16802, p-value = 0.0006838
alternative hypothesis: two.sided4.2.2 no give up answers
Warning: Removed 1 row containing non-finite outside the scale range (`stat_summary()`).
Removed 1 row containing non-finite outside the scale range (`stat_summary()`).
Removed 1 row containing non-finite outside the scale range (`stat_summary()`).
Removed 1 row containing non-finite outside the scale range (`stat_summary()`).
OSA 1 & OSA 4
data_curve_analysis(filter(OSA_explan_core, giveup == FALSE), "explanation_eval")Warning in jonckheere.test(data[[dv]], as.numeric(data$violation_number)): Sample size > 100 or data with ties
p-value based on normal approximation. Specify nperm for permutation p-value$model_summary
Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: formula_lmer
Data: data
REML criterion at convergence: 2402.9
Scaled residuals:
Min 1Q Median 3Q Max
-3.16152 -0.59350 0.06258 0.67453 2.28994
Random effects:
Groups Name Variance Std.Dev.
subject (Intercept) 0.3426 0.5853
Residual 1.1435 1.0693
Number of obs: 752, groups: subject, 282
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 2.78057 0.08479 722.50253 32.795 < 2e-16 ***
violation_number1 0.88420 0.11111 588.31958 7.958 9.05e-15 ***
violation_number2 -0.02338 0.11235 591.84901 -0.208 0.835200
violation_number3 -0.42594 0.11486 590.12699 -3.709 0.000228 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Correlation of Fixed Effects:
(Intr) vltn_1 vltn_2
viltn_nmbr1 -0.637
viltn_nmbr2 -0.631 0.495
viltn_nmbr3 -0.613 0.484 0.486
$emmeans
violation_number emmean SE df lower.CL upper.CL
0 2.78 0.0848 722 2.57 2.99
1 3.66 0.0868 724 3.45 3.88
2 2.76 0.0884 726 2.54 2.98
3 2.35 0.0919 734 2.12 2.58
Degrees-of-freedom method: kenward-roger
Confidence level used: 0.95
Conf-level adjustment: bonferroni method for 4 estimates
$pairwise_comparisons
contrast estimate SE df t.ratio p.value
violation_number0 - violation_number1 -0.8842 0.111 588 -7.952 <.0001
violation_number0 - violation_number2 0.0234 0.112 592 0.208 0.8353
violation_number0 - violation_number3 0.4259 0.115 590 3.706 0.0007
violation_number1 - violation_number2 0.9076 0.112 553 8.081 <.0001
violation_number1 - violation_number3 1.3101 0.115 555 11.401 <.0001
violation_number2 - violation_number3 0.4026 0.115 536 3.494 0.0010
Degrees-of-freedom method: kenward-roger
P value adjustment: holm method for 6 tests
$jonckheere_test
Jonckheere-Terpstra test
data:
JT = 91771, p-value = 2.187e-05
alternative hypothesis: two.sidedOSA 1 only
data_curve_analysis(filter(OSA_explan_core, giveup == FALSE, exp == "OSA1"), "explanation_eval")Warning in jonckheere.test(data[[dv]], as.numeric(data$violation_number)): Sample size > 100 or data with ties
p-value based on normal approximation. Specify nperm for permutation p-value$model_summary
Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: formula_lmer
Data: data
REML criterion at convergence: 1414.3
Scaled residuals:
Min 1Q Median 3Q Max
-3.12056 -0.65101 0.08915 0.64610 2.42294
Random effects:
Groups Name Variance Std.Dev.
subject (Intercept) 0.247 0.497
Residual 1.032 1.016
Number of obs: 461, groups: subject, 200
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 2.84400 0.10172 453.26114 27.960 < 2e-16 ***
violation_number1 0.90770 0.13582 372.18002 6.683 8.56e-11 ***
violation_number2 0.06756 0.13777 375.21803 0.490 0.62417
violation_number3 -0.43793 0.14253 374.85700 -3.072 0.00228 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Correlation of Fixed Effects:
(Intr) vltn_1 vltn_2
viltn_nmbr1 -0.670
viltn_nmbr2 -0.661 0.512
viltn_nmbr3 -0.636 0.496 0.498
$emmeans
violation_number emmean SE df lower.CL upper.CL
0 2.84 0.102 454 2.59 3.10
1 3.75 0.102 453 3.50 4.01
2 2.91 0.104 453 2.65 3.17
3 2.41 0.111 456 2.13 2.68
Degrees-of-freedom method: kenward-roger
Confidence level used: 0.95
Conf-level adjustment: bonferroni method for 4 estimates
$pairwise_comparisons
contrast estimate SE df t.ratio p.value
violation_number0 - violation_number1 -0.9077 0.136 378 -6.672 <.0001
violation_number0 - violation_number2 -0.0676 0.138 381 -0.490 0.6248
violation_number0 - violation_number3 0.4379 0.143 381 3.067 0.0046
violation_number1 - violation_number2 0.8401 0.135 338 6.207 <.0001
violation_number1 - violation_number3 1.3456 0.140 338 9.609 <.0001
violation_number2 - violation_number3 0.5055 0.141 321 3.595 0.0011
Degrees-of-freedom method: kenward-roger
P value adjustment: holm method for 6 tests
$jonckheere_test
Jonckheere-Terpstra test
data:
JT = 34272, p-value = 0.0005648
alternative hypothesis: two.sidedOSA 4 only
data_curve_analysis(filter(OSA_explan_core, giveup == FALSE, exp == "OSA4"), "explanation_eval")Warning in jonckheere.test(data[[dv]], as.numeric(data$violation_number)): Sample size > 100 or data with ties
p-value based on normal approximation. Specify nperm for permutation p-value$model_summary
Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: formula_lmer
Data: data
REML criterion at convergence: 979.2
Scaled residuals:
Min 1Q Median 3Q Max
-2.82239 -0.60185 0.03984 0.66667 2.05175
Random effects:
Groups Name Variance Std.Dev.
subject (Intercept) 0.4634 0.6807
Residual 1.3375 1.1565
Number of obs: 291, groups: subject, 82
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 2.6575 0.1490 242.7931 17.834 < 2e-16 ***
violation_number1 0.8475 0.1893 211.0831 4.477 1.24e-05 ***
violation_number2 -0.1667 0.1904 212.9626 -0.875 0.3823
violation_number3 -0.4021 0.1912 213.0728 -2.103 0.0367 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Correlation of Fixed Effects:
(Intr) vltn_1 vltn_2
viltn_nmbr1 -0.585
viltn_nmbr2 -0.584 0.466
viltn_nmbr3 -0.581 0.461 0.462
$emmeans
violation_number emmean SE df lower.CL upper.CL
0 2.66 0.149 245 2.28 3.03
1 3.51 0.158 256 3.11 3.90
2 2.49 0.159 257 2.09 2.89
3 2.26 0.160 259 1.85 2.66
Degrees-of-freedom method: kenward-roger
Confidence level used: 0.95
Conf-level adjustment: bonferroni method for 4 estimates
$pairwise_comparisons
contrast estimate SE df t.ratio p.value
violation_number0 - violation_number1 -0.848 0.189 214 -4.476 <.0001
violation_number0 - violation_number2 0.167 0.190 216 0.875 0.4714
violation_number0 - violation_number3 0.402 0.191 216 2.102 0.1102
violation_number1 - violation_number2 1.014 0.196 214 5.167 <.0001
violation_number1 - violation_number3 1.250 0.198 216 6.320 <.0001
violation_number2 - violation_number3 0.235 0.198 214 1.189 0.4714
Degrees-of-freedom method: kenward-roger
P value adjustment: holm method for 6 tests
$jonckheere_test
Jonckheere-Terpstra test
data:
JT = 13901, p-value = 0.01491
alternative hypothesis: two.sided