## Linear mixed model fit by maximum likelihood . t-tests use
## Satterthwaite's method [lmerModLmerTest]
## Formula: bc_pa ~ outcome + PE + (1 + outcome + PE | cohort/id/exam_num)
## Data: df.pa.conf.12
##
## AIC BIC logLik deviance df.resid
## 22280.5 22411.5 -11118.3 22236.5 2823
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -5.5171 -0.4406 0.0310 0.4563 5.0784
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## exam_num:(id:cohort) (Intercept) 311.16372 17.6398
## outcome 3.46507 1.8615 -0.81
## PE 11.53248 3.3959 0.62 -0.96
## id:cohort (Intercept) 30.67039 5.5381
## outcome 0.76803 0.8764 -0.77
## PE 2.72475 1.6507 0.99 -0.84
## cohort (Intercept) 1.26183 1.1233
## outcome 0.04050 0.2012 -1.00
## PE 0.05241 0.2289 1.00 -1.00
## Residual 95.00459 9.7470
## Number of obs: 2845, groups:
## exam_num:(id:cohort), 453; id:cohort, 156; cohort, 3
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -1.7482 1.4413 5.2591 -1.213 0.277
## outcome 0.2092 0.2060 3.1995 1.015 0.380
## PE 3.0964 0.4299 10.1783 7.203 2.65e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) outcom
## outcome -0.884
## PE 0.497 -0.596Model AIC: 2.228051610^{4}
## Linear mixed model fit by maximum likelihood . t-tests use
## Satterthwaite's method [lmerModLmerTest]
## Formula: bc_na ~ outcome + PE + (1 + outcome + PE | cohort/id/exam_num)
## Data: df.na.conf.12
##
## AIC BIC logLik deviance df.resid
## 22796.3 22927.3 -11376.2 22752.3 2823
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.9334 -0.4815 -0.0464 0.4142 5.0573
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## exam_num:(id:cohort) (Intercept) 623.81336 24.9763
## outcome 8.05189 2.8376 -0.88
## PE 11.48466 3.3889 0.61 -0.91
## id:cohort (Intercept) 147.40561 12.1411
## outcome 1.88055 1.3713 -0.96
## PE 7.29592 2.7011 0.96 -0.84
## cohort (Intercept) 0.15227 0.3902
## outcome 0.04173 0.2043 -1.00
## PE 0.00558 0.0747 1.00 -1.00
## Residual 111.28068 10.5490
## Number of obs: 2845, groups:
## exam_num:(id:cohort), 453; id:cohort, 156; cohort, 3
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 7.1773 2.0384 64.4244 3.521 0.000795 ***
## outcome -0.6854 0.2741 7.8571 -2.500 0.037438 *
## PE -2.6913 0.4824 75.4970 -5.579 3.61e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) outcom
## outcome -0.880
## PE 0.511 -0.547Model AIC: 2.27963110^{4}
## Linear mixed model fit by maximum likelihood . t-tests use
## Satterthwaite's method [lmerModLmerTest]
## Formula: bc_pa ~ outcome + PE + (1 + outcome + PE | cohort/id/exam_num)
## Data: df.pa.conf.100
##
## AIC BIC logLik deviance df.resid
## 22412.2 22543.2 -11184.1 22368.2 2823
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -5.8065 -0.4476 0.0285 0.4818 5.3176
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## exam_num:(id:cohort) (Intercept) 1.538e+02 12.40273
## outcome 1.688e-03 0.04108 -1.00
## PE 6.644e-01 0.81512 -0.22 0.22
## id:cohort (Intercept) 2.118e+02 14.55172
## outcome 4.326e-02 0.20798 -0.94
## PE 5.011e-01 0.70788 0.68 -0.69
## cohort (Intercept) 2.921e+02 17.09229
## outcome 1.809e+02 13.45114 0.07
## PE 1.456e+02 12.06830 -0.12 0.26
## Residual 9.826e+01 9.91250
## Number of obs: 2845, groups:
## exam_num:(id:cohort), 453; id:cohort, 156; cohort, 3
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -1.335170 10.007587 271.386813 -0.133 0.894
## outcome -0.007585 7.766050 16.169136 -0.001 0.999
## PE 1.063375 6.968888 4.367755 0.153 0.886
##
## Correlation of Fixed Effects:
## (Intr) outcom
## outcome 0.069
## PE -0.117 0.260
## convergence code: 1
## unable to evaluate scaled gradient
## Model failed to converge: degenerate Hessian with 5 negative eigenvaluesModel AIC: 2.241224710^{4}
## Linear mixed model fit by maximum likelihood . t-tests use
## Satterthwaite's method [lmerModLmerTest]
## Formula: bc_na ~ outcome + PE + (1 + outcome + PE | cohort/id/exam_num)
## Data: df.na.conf.100
##
## AIC BIC logLik deviance df.resid
## 22832.3 22963.2 -11394.1 22788.3 2823
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.8010 -0.4806 -0.0441 0.4054 4.8953
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## exam_num:(id:cohort) (Intercept) 618.7647 24.8750
## outcome 0.1088 0.3299 -0.87
## PE 0.2790 0.5282 0.48 -0.85
## id:cohort (Intercept) 356.2874 18.8756
## outcome 0.0497 0.2229 -0.99
## PE 0.3906 0.6250 0.95 -0.89
## cohort (Intercept) 556.2738 23.5855
## outcome 48.4986 6.9641 0.90
## PE 174.0223 13.1918 0.04 0.24
## Residual 108.1842 10.4012
## Number of obs: 2845, groups:
## exam_num:(id:cohort), 453; id:cohort, 156; cohort, 3
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 6.94473 13.84387 16.40073 0.502 0.623
## outcome -0.05052 4.02085 0.39794 -0.013 0.994
## PE -0.82741 7.61733 2.87692 -0.109 0.921
##
## Correlation of Fixed Effects:
## (Intr) outcom
## outcome 0.880
## PE 0.038 0.236
## convergence code: 1
## unable to evaluate scaled gradient
## Model failed to converge: degenerate Hessian with 3 negative eigenvaluesModel AIC: 2.283225210^{4}