LifeIPV x CESD Interaction Plot
Trails A Regression Model 1
load("/Users/meganwilliams/Desktop/Research/IPV and Cognition Paper/R Commands and Scripts/Abusew01Neupsy.rda")
library(lme4)
library(lmerTest)
library(compute.es)
library(zStat)
library(zUtil)
(lm1 = lmer(log(TrailsAtestSec)~(LifeIPV + Sex + PovStat)^3 + (1|subclass), data=Abusew01Neupsy))
step(lm1)
Trails A Final Regression Model 1
(lm1 = lmer(log(TrailsAtestSec)~PovStat + (1|subclass), data=Abusew01Neupsy))
## Linear mixed model fit by REML ['merModLmerTest']
## Formula: log(TrailsAtestSec) ~ PovStat + (1 | subclass)
## Data: Abusew01Neupsy
## REML criterion at convergence: 503.1
## Random effects:
## Groups Name Std.Dev.
## subclass (Intercept) 0.132
## Residual 0.436
## Number of obs: 393, groups: subclass, 131
## Fixed Effects:
## (Intercept) PovStatBelow
## 3.452 0.177
summary(lm1)
## Linear mixed model fit by REML ['merModLmerTest']
## Formula: log(TrailsAtestSec) ~ PovStat + (1 | subclass)
## Data: Abusew01Neupsy
##
## REML criterion at convergence: 503.1
##
## Random effects:
## Groups Name Variance Std.Dev.
## subclass (Intercept) 0.0173 0.132
## Residual 0.1905 0.436
## Number of obs: 393, groups: subclass, 131
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 3.4518 0.0320 230.0000 108.01 < 2e-16
## PovStatBelow 0.1773 0.0462 390.0000 3.84 0.00015
##
## Correlation of Fixed Effects:
## (Intr)
## PovStatBelw -0.629
Trails A Regression Model 2 (with CES)
(lm2 = lmer(log(TrailsAtestSec)~(LifeIPV + Sex + PovStat + CESD)^4 + (1|subclass), data=Abusew01Neupsy))
step(lm2)
Trails A Final Regression Model 2 (with CES)
lm2=lmer(log(TrailsAtestSec)~ PovStat + (1|subclass),data = Abusew01Neupsy)
summary(lm2)
## Linear mixed model fit by REML ['merModLmerTest']
## Formula: log(TrailsAtestSec) ~ PovStat + (1 | subclass)
## Data: Abusew01Neupsy
##
## REML criterion at convergence: 503.1
##
## Random effects:
## Groups Name Variance Std.Dev.
## subclass (Intercept) 0.0173 0.132
## Residual 0.1905 0.436
## Number of obs: 393, groups: subclass, 131
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 3.4518 0.0320 230.0000 108.01 < 2e-16
## PovStatBelow 0.1773 0.0462 390.0000 3.84 0.00015
##
## Correlation of Fixed Effects:
## (Intr)
## PovStatBelw -0.629
Trails B Regression Model 1
(lm1 = lmer(log(TrailsBtestSec)~(LifeIPV + Sex + PovStat)^3 + (1|subclass) , data=Abusew01Neupsy))
(st = step(lm1))
Trails B Final Regression Model 1
lm1 = lmer(log(TrailsBtestSec)~PovStat + (1|subclass) , data=Abusew01Neupsy)
step(lm1)
## Error: subscript out of bounds
summary(lm1)
## Linear mixed model fit by REML ['merModLmerTest']
## Formula: log(TrailsBtestSec) ~ PovStat + (1 | subclass)
## Data: Abusew01Neupsy
##
## REML criterion at convergence: 896.8
##
## Random effects:
## Groups Name Variance Std.Dev.
## subclass (Intercept) 0.0638 0.253
## Residual 0.5079 0.713
## Number of obs: 393, groups: subclass, 131
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 4.5771 0.0537 229.0000 85.24 < 2e-16
## PovStatBelow 0.3604 0.0764 388.0000 4.72 3.3e-06
##
## Correlation of Fixed Effects:
## (Intr)
## PovStatBelw -0.619
Trails B Regression Model 2 (with CES)
lm1 = lmer(log(TrailsBtestSec)~(LifeIPV + Sex + PovStat + CESD)^4 + (1|subclass) , data=Abusew01Neupsy)
step(lm1)
Trails B Final Regression Model 2 (with CES)
lm2 = lmer(log(TrailsBtestSec)~LifeIPV + PovStat + CESD + LifeIPV:CESD + (1|subclass) , data=Abusew01Neupsy)
summary(lm2)
## Linear mixed model fit by REML ['merModLmerTest']
## Formula: log(TrailsBtestSec) ~ LifeIPV + PovStat + CESD + LifeIPV:CESD + (1 | subclass)
## Data: Abusew01Neupsy
##
## REML criterion at convergence: 888.9
##
## Random effects:
## Groups Name Variance Std.Dev.
## subclass (Intercept) 0.0724 0.269
## Residual 0.4827 0.695
## Number of obs: 393, groups: subclass, 131
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 4.4380 0.0640 330.0000 69.34 < 2e-16
## LifeIPV1 0.2500 0.1215 351.0000 2.06 0.04027
## PovStatBelow 0.3012 0.0778 386.0000 3.87 0.00013
## CESD1 0.3717 0.0953 377.0000 3.90 0.00011
## LifeIPV1:CESD1 -0.4056 0.1660 385.0000 -2.44 0.01502
##
## Correlation of Fixed Effects:
## (Intr) LfIPV1 PvSttB CESD1
## LifeIPV1 -0.358
## PovStatBelw -0.370 -0.089
## CESD1 -0.466 0.302 -0.151
## LIPV1:CESD1 0.308 -0.763 0.000 -0.575
Effect Size
es = tes(t=-2.39, n.1=180, n.2=213, level=95, dig=2, id=HNDid, data=Abusew01Neupsy)
summary(es)
## id N.total n.1 n.2
## Min. :8.11e+09 Min. :393 Min. :180 Min. :213
## 1st Qu.:8.17e+09 1st Qu.:393 1st Qu.:180 1st Qu.:213
## Median :8.21e+09 Median :393 Median :180 Median :213
## Mean :8.19e+09 Mean :393 Mean :180 Mean :213
## 3rd Qu.:8.22e+09 3rd Qu.:393 3rd Qu.:180 3rd Qu.:213
## Max. :8.22e+09 Max. :393 Max. :180 Max. :213
## d var.d l.d u.d
## Min. :-0.24 Min. :0.01 Min. :-0.44 Min. :-0.04
## 1st Qu.:-0.24 1st Qu.:0.01 1st Qu.:-0.44 1st Qu.:-0.04
## Median :-0.24 Median :0.01 Median :-0.44 Median :-0.04
## Mean :-0.24 Mean :0.01 Mean :-0.44 Mean :-0.04
## 3rd Qu.:-0.24 3rd Qu.:0.01 3rd Qu.:-0.44 3rd Qu.:-0.04
## Max. :-0.24 Max. :0.01 Max. :-0.44 Max. :-0.04
## U3.d cl.d cliffs.d pval.d
## Min. :40.4 Min. :43.2 Min. :-0.14 Min. :0.02
## 1st Qu.:40.4 1st Qu.:43.2 1st Qu.:-0.14 1st Qu.:0.02
## Median :40.4 Median :43.2 Median :-0.14 Median :0.02
## Mean :40.4 Mean :43.2 Mean :-0.14 Mean :0.02
## 3rd Qu.:40.4 3rd Qu.:43.2 3rd Qu.:-0.14 3rd Qu.:0.02
## Max. :40.4 Max. :43.2 Max. :-0.14 Max. :0.02
## g var.g l.g u.g
## Min. :-0.24 Min. :0.01 Min. :-0.44 Min. :-0.04
## 1st Qu.:-0.24 1st Qu.:0.01 1st Qu.:-0.44 1st Qu.:-0.04
## Median :-0.24 Median :0.01 Median :-0.44 Median :-0.04
## Mean :-0.24 Mean :0.01 Mean :-0.44 Mean :-0.04
## 3rd Qu.:-0.24 3rd Qu.:0.01 3rd Qu.:-0.44 3rd Qu.:-0.04
## Max. :-0.24 Max. :0.01 Max. :-0.44 Max. :-0.04
## U3.g cl.g pval.g r var.r
## Min. :40.5 Min. :43.2 Min. :0.02 Min. :0.12 Min. :0
## 1st Qu.:40.5 1st Qu.:43.2 1st Qu.:0.02 1st Qu.:0.12 1st Qu.:0
## Median :40.5 Median :43.2 Median :0.02 Median :0.12 Median :0
## Mean :40.5 Mean :43.2 Mean :0.02 Mean :0.12 Mean :0
## 3rd Qu.:40.5 3rd Qu.:43.2 3rd Qu.:0.02 3rd Qu.:0.12 3rd Qu.:0
## Max. :40.5 Max. :43.2 Max. :0.02 Max. :0.12 Max. :0
## l.r u.r pval.r fisher.z var.z
## Min. :0.02 Min. :0.22 Min. :0.02 Min. :0.12 Min. :0
## 1st Qu.:0.02 1st Qu.:0.22 1st Qu.:0.02 1st Qu.:0.12 1st Qu.:0
## Median :0.02 Median :0.22 Median :0.02 Median :0.12 Median :0
## Mean :0.02 Mean :0.22 Mean :0.02 Mean :0.12 Mean :0
## 3rd Qu.:0.02 3rd Qu.:0.22 3rd Qu.:0.02 3rd Qu.:0.12 3rd Qu.:0
## Max. :0.02 Max. :0.22 Max. :0.02 Max. :0.12 Max. :0
## l.z u.z OR l.or
## Min. :0.02 Min. :0.22 Min. :0.64 Min. :0.45
## 1st Qu.:0.02 1st Qu.:0.22 1st Qu.:0.64 1st Qu.:0.45
## Median :0.02 Median :0.22 Median :0.64 Median :0.45
## Mean :0.02 Mean :0.22 Mean :0.64 Mean :0.45
## 3rd Qu.:0.02 3rd Qu.:0.22 3rd Qu.:0.64 3rd Qu.:0.45
## Max. :0.02 Max. :0.22 Max. :0.64 Max. :0.45
## u.or pval.or lOR l.lor
## Min. :0.93 Min. :0.02 Min. :-0.44 Min. :-0.8
## 1st Qu.:0.93 1st Qu.:0.02 1st Qu.:-0.44 1st Qu.:-0.8
## Median :0.93 Median :0.02 Median :-0.44 Median :-0.8
## Mean :0.93 Mean :0.02 Mean :-0.44 Mean :-0.8
## 3rd Qu.:0.93 3rd Qu.:0.02 3rd Qu.:-0.44 3rd Qu.:-0.8
## Max. :0.93 Max. :0.02 Max. :-0.44 Max. :-0.8
## u.lor pval.lor NNT
## Min. :-0.08 Min. :0.02 Min. :-16.5
## 1st Qu.:-0.08 1st Qu.:0.02 1st Qu.:-16.5
## Median :-0.08 Median :0.02 Median :-16.5
## Mean :-0.08 Mean :0.02 Mean :-16.5
## 3rd Qu.:-0.08 3rd Qu.:0.02 3rd Qu.:-16.5
## Max. :-0.08 Max. :0.02 Max. :-16.5
LifeIPV x CESD Interaction Plot
FluencyWord Regression Model 1
lm1 = lmer(FluencyWord~(LifeIPV + Sex + PovStat)^3 + (1|subclass), data=Abusew01Neupsy)
step(lm1)
NOTE: Because the step() function eliminates a nonsignificant random term in the above regression analysis, I cannot see what the final model would be if I forced it to keep the random term; so, I manually entered the model with various combinations of interaction terms.
lm1 = lmer(FluencyWord~LifeIPV + (1|subclass), data=Abusew01Neupsy)
summary(lm1)
## Linear mixed model fit by REML ['merModLmerTest']
## Formula: FluencyWord ~ LifeIPV + (1 | subclass)
## Data: Abusew01Neupsy
##
## REML criterion at convergence: 2469
##
## Random effects:
## Groups Name Variance Std.Dev.
## subclass (Intercept) 1.24 1.11
## Residual 30.27 5.50
## Number of obs: 393, groups: subclass, 131
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 19.187 0.354 247.100 54.27 <2e-16
## LifeIPV1 -1.210 0.589 261.000 -2.06 0.041
##
## Correlation of Fixed Effects:
## (Intr)
## LifeIPV1 -0.555
lm2 = lmer(FluencyWord~LifeIPV*Sex + (1|subclass), data=Abusew01Neupsy)
summary(lm2)
## Linear mixed model fit by REML ['merModLmerTest']
## Formula: FluencyWord ~ LifeIPV * Sex + (1 | subclass)
## Data: Abusew01Neupsy
##
## REML criterion at convergence: 2464
##
## Random effects:
## Groups Name Variance Std.Dev.
## subclass (Intercept) 1.26 1.12
## Residual 30.33 5.51
## Number of obs: 393, groups: subclass, 131
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 18.848 0.476 334.000 39.56 <2e-16
## LifeIPV1 -0.837 0.697 312.000 -1.20 0.23
## SexMen 0.740 0.696 388.000 1.06 0.29
## LifeIPV1:SexMen -0.999 1.620 389.000 -0.62 0.54
##
## Correlation of Fixed Effects:
## (Intr) LfIPV1 SexMen
## LifeIPV1 -0.656
## SexMen -0.669 0.459
## LfIPV1:SxMn 0.291 -0.444 -0.435
lm3 = lmer(FluencyWord~LifeIPV*PovStat + (1|subclass), data=Abusew01Neupsy)
summary(lm3)
## Linear mixed model fit by REML ['merModLmerTest']
## Formula: FluencyWord ~ LifeIPV * PovStat + (1 | subclass)
## Data: Abusew01Neupsy
##
## REML criterion at convergence: 2457
##
## Random effects:
## Groups Name Variance Std.Dev.
## subclass (Intercept) 1.46 1.21
## Residual 29.56 5.44
## Number of obs: 393, groups: subclass, 131
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 19.929 0.439 321.000 45.35 <2e-16
## LifeIPV1 -1.536 0.844 332.000 -1.82 0.070
## PovStatBelow -2.003 0.710 385.000 -2.82 0.005
## LifeIPV1:PovStatBelow 1.267 1.206 377.000 1.05 0.294
##
## Correlation of Fixed Effects:
## (Intr) LfIPV1 PvSttB
## LifeIPV1 -0.488
## PovStatBelw -0.598 0.307
## LfIPV1:PvSB 0.349 -0.712 -0.583
lm4 = lmer(FluencyWord~PovStat*Sex + (1|subclass), data=Abusew01Neupsy)
summary(lm4)
## Linear mixed model fit by REML ['merModLmerTest']
## Formula: FluencyWord ~ PovStat * Sex + (1 | subclass)
## Data: Abusew01Neupsy
##
## REML criterion at convergence: 2458
##
## Random effects:
## Groups Name Variance Std.Dev.
## subclass (Intercept) 1.4 1.18
## Residual 29.7 5.45
## Number of obs: 393, groups: subclass, 131
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 19.078 0.477 302.000 40.01 <2e-16
## PovStatBelow -1.344 0.701 388.000 -1.92 0.056
## SexMen 1.256 0.774 383.000 1.62 0.105
## PovStatBelow:SexMen -1.034 1.193 388.000 -0.87 0.387
##
## Correlation of Fixed Effects:
## (Intr) PvSttB SexMen
## PovStatBelw -0.661
## SexMen -0.594 0.408
## PvSttBlw:SM 0.388 -0.587 -0.652
lm5 = lmer(FluencyWord~LifeIPV*Sex*PovStat + (1|subclass), data=Abusew01Neupsy)
summary(lm5)
## Linear mixed model fit by REML ['merModLmerTest']
## Formula: FluencyWord ~ LifeIPV * Sex * PovStat + (1 | subclass)
## Data: Abusew01Neupsy
##
## REML criterion at convergence: 2444
##
## Random effects:
## Groups Name Variance Std.Dev.
## subclass (Intercept) 1.6 1.26
## Residual 29.6 5.44
## Number of obs: 393, groups: subclass, 131
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 19.59325 0.59516 370.00000 32.92
## LifeIPV1 -1.40289 0.96098 339.00000 -1.46
## SexMen 0.72831 0.86855 381.00000 0.84
## PovStatBelow -2.00204 0.96360 378.00000 -2.08
## LifeIPV1:SexMen 1.59200 2.74733 383.00000 0.58
## LifeIPV1:PovStatBelow 1.68848 1.41488 361.00000 1.19
## SexMen:PovStatBelow 0.00837 1.42337 370.00000 0.01
## LifeIPV1:SexMen:PovStatBelow -3.68998 3.44087 375.00000 -1.07
## Pr(>|t|)
## (Intercept) <2e-16
## LifeIPV1 0.145
## SexMen 0.402
## PovStatBelow 0.038
## LifeIPV1:SexMen 0.563
## LifeIPV1:PovStatBelow 0.234
## SexMen:PovStatBelow 0.995
## LifeIPV1:SexMen:PovStatBelow 0.284
##
## Correlation of Fixed Effects:
## (Intr) LfIPV1 SexMen PvSttB LfIPV1:SM LIPV1:P SM:PSB
## LifeIPV1 -0.595
## SexMen -0.672 0.416
## PovStatBelw -0.604 0.367 0.412
## LfIPV1:SxMn 0.208 -0.355 -0.316 -0.122
## LfIPV1:PvSB 0.407 -0.685 -0.278 -0.673 0.236
## SxMn:PvSttB 0.408 -0.249 -0.606 -0.675 0.188 0.454
## LIPV1:SM:PS -0.163 0.280 0.245 0.270 -0.795 -0.407 -0.407
FluencyWord Regression Model 2 (with CES)
lm2 = lmer(FluencyWord~(LifeIPV + Sex + PovStat + CESD)^4 + (1|subclass), data=Abusew01Neupsy)
step(lm2,keep.effs="subclass")
NOTE: Because the step() function eliminates a nonsignificant random term in the above regression analysis, I cannot see what the final model would be if I forced it to keep the random term; so, I manually entered the model with various combinations of interaction terms.
lm1 = lmer(FluencyWord~LifeIPV*CESD + (1|subclass), data=Abusew01Neupsy)
summary(lm1)
## Linear mixed model fit by REML ['merModLmerTest']
## Formula: FluencyWord ~ LifeIPV * CESD + (1 | subclass)
## Data: Abusew01Neupsy
##
## REML criterion at convergence: 2456
##
## Random effects:
## Groups Name Variance Std.Dev.
## subclass (Intercept) 1.69 1.30
## Residual 29.24 5.41
## Number of obs: 393, groups: subclass, 131
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 19.891 0.436 317.000 45.63 <2e-16
## LifeIPV1 -0.890 0.923 360.000 -0.96 0.3356
## CESD1 -1.962 0.713 384.000 -2.75 0.0062
## LifeIPV1:CESD1 0.403 1.250 389.000 0.32 0.7475
##
## Correlation of Fixed Effects:
## (Intr) LfIPV1 CESD1
## LifeIPV1 -0.444
## CESD1 -0.587 0.283
## LIPV1:CESD1 0.339 -0.756 -0.577
lm2 = lmer(FluencyWord~LifeIPV*Sex*CESD + (1|subclass), data=Abusew01Neupsy)
summary(lm2)
## Linear mixed model fit by REML ['merModLmerTest']
## Formula: FluencyWord ~ LifeIPV * Sex * CESD + (1 | subclass)
## Data: Abusew01Neupsy
##
## REML criterion at convergence: 2444
##
## Random effects:
## Groups Name Variance Std.Dev.
## subclass (Intercept) 1.7 1.30
## Residual 29.5 5.43
## Number of obs: 393, groups: subclass, 131
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 19.555 0.602 371.000 32.48 <2e-16
## LifeIPV1 -0.621 1.049 370.000 -0.59 0.554
## SexMen 0.697 0.860 384.000 0.81 0.418
## CESD1 -1.844 0.958 381.000 -1.92 0.055
## LifeIPV1:SexMen 0.118 3.044 384.000 0.04 0.969
## LifeIPV1:CESD1 0.413 1.452 385.000 0.28 0.776
## SexMen:CESD1 -0.174 1.447 382.000 -0.12 0.904
## LifeIPV1:SexMen:CESD1 -1.070 3.661 382.000 -0.29 0.770
##
## Correlation of Fixed Effects:
## (Intr) LfIPV1 SexMen CESD1 LfIPV1:SM LIPV1:C SM:CES
## LifeIPV1 -0.559
## SexMen -0.687 0.400
## CESD1 -0.615 0.355 0.429
## LfIPV1:SxMn 0.192 -0.349 -0.287 -0.111
## LIPV1:CESD1 0.411 -0.734 -0.289 -0.663 0.246
## SexMn:CESD1 0.409 -0.235 -0.594 -0.663 0.166 0.438
## LIPV1:SM:CE -0.158 0.288 0.237 0.253 -0.830 -0.389 -0.394
lm3 = lmer(FluencyWord~LifeIPV*PovStat*CESD + (1|subclass), data=Abusew01Neupsy)
summary(lm3)
## Linear mixed model fit by REML ['merModLmerTest']
## Formula: FluencyWord ~ LifeIPV * PovStat * CESD + (1 | subclass)
## Data: Abusew01Neupsy
##
## REML criterion at convergence: 2440
##
## Random effects:
## Groups Name Variance Std.Dev.
## subclass (Intercept) 1.66 1.29
## Residual 29.04 5.39
## Number of obs: 393, groups: subclass, 131
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 20.596 0.519 357.000 39.69 <2e-16
## LifeIPV1 -1.369 1.213 373.000 -1.13 0.260
## PovStatBelow -2.287 0.919 376.000 -2.49 0.013
## CESD1 -2.259 0.943 385.000 -2.40 0.017
## LifeIPV1:PovStatBelow 1.780 1.896 383.000 0.94 0.349
## LifeIPV1:CESD1 0.760 1.758 384.000 0.43 0.666
## PovStatBelow:CESD1 1.442 1.466 382.000 0.98 0.326
## LifeIPV1:PovStatBelow:CESD1 -1.393 2.545 384.000 -0.55 0.584
##
## Correlation of Fixed Effects:
## (Intr) LfIPV1 PvSttB CESD1 LfIPV1:PSB LIPV1:C PSB:CE
## LifeIPV1 -0.411
## PovStatBelw -0.548 0.234
## CESD1 -0.537 0.237 0.301
## LfIPV1:PvSB 0.268 -0.646 -0.486 -0.153
## LIPV1:CESD1 0.294 -0.702 -0.166 -0.544 0.451
## PvStB:CESD1 0.345 -0.150 -0.626 -0.644 0.309 0.350
## LIPV1:PSB:C -0.204 0.484 0.367 0.378 -0.751 -0.692 -0.584
lm4 = lmer(FluencyWord~PovStat*Sex*CESD + (1|subclass), data=Abusew01Neupsy)
summary(lm4)
## Linear mixed model fit by REML ['merModLmerTest']
## Formula: FluencyWord ~ PovStat * Sex * CESD + (1 | subclass)
## Data: Abusew01Neupsy
##
## REML criterion at convergence: 2440
##
## Random effects:
## Groups Name Variance Std.Dev.
## subclass (Intercept) 1.76 1.33
## Residual 28.91 5.38
## Number of obs: 393, groups: subclass, 131
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 19.851 0.604 374.000 32.87 <2e-16
## PovStatBelow -1.477 1.033 383.000 -1.43 0.154
## SexMen 1.281 0.950 382.000 1.35 0.178
## CESD1 -1.984 0.946 374.000 -2.10 0.037
## PovStatBelow:SexMen -1.150 1.634 384.000 -0.70 0.482
## PovStatBelow:CESD1 0.987 1.422 377.000 0.69 0.488
## SexMen:CESD1 -0.560 1.634 383.000 -0.34 0.732
## PovStatBelow:SexMen:CESD1 0.389 2.457 384.000 0.16 0.874
##
## Correlation of Fixed Effects:
## (Intr) PvSttB SexMen CESD1 PvSB:SM PSB:CE SM:CES
## PovStatBelw -0.571
## SexMen -0.621 0.369
## CESD1 -0.616 0.356 0.394
## PvSttBlw:SM 0.362 -0.636 -0.585 -0.227
## PvStB:CESD1 0.412 -0.720 -0.267 -0.665 0.461
## SexMn:CESD1 0.362 -0.215 -0.585 -0.583 0.344 0.394
## PSB:SM:CESD -0.240 0.425 0.391 0.385 -0.669 -0.588 -0.670
lm5 = lmer(FluencyWord~LifeIPV*Sex*PovStat*CESD + (1|subclass), data=Abusew01Neupsy)
summary(lm5)
## Linear mixed model fit by REML ['merModLmerTest']
## Formula: FluencyWord ~ LifeIPV * Sex * PovStat * CESD + (1 | subclass)
## Data: Abusew01Neupsy
##
## REML criterion at convergence: 2410
##
## Random effects:
## Groups Name Variance Std.Dev.
## subclass (Intercept) 1.74 1.32
## Residual 29.36 5.42
## Number of obs: 393, groups: subclass, 131
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 20.1102 0.7083 377.0000 28.39
## LifeIPV1 -0.9767 1.3510 363.0000 -0.72
## SexMen 1.0507 1.0347 375.0000 1.02
## PovStatBelow -1.9550 1.3158 373.0000 -1.49
## CESD1 -1.7554 1.2873 377.0000 -1.36
## LifeIPV1:SexMen 0.1473 4.2289 375.0000 0.03
## LifeIPV1:PovStatBelow 1.5144 2.1872 372.0000 0.69
## SexMen:PovStatBelow -0.7598 1.8582 375.0000 -0.41
## LifeIPV1:CESD1 0.0525 2.0257 375.0000 0.03
## SexMen:CESD1 -1.0970 1.9097 377.0000 -0.57
## PovStatBelow:CESD1 0.6999 2.0024 377.0000 0.35
## LifeIPV1:SexMen:PovStatBelow 0.0218 6.1150 374.0000 0.00
## LifeIPV1:SexMen:CESD1 3.0472 5.6495 377.0000 0.54
## LifeIPV1:PovStatBelow:CESD1 -0.1333 2.9827 376.0000 -0.04
## SexMen:PovStatBelow:CESD1 1.8468 3.0014 376.0000 0.62
## LifeIPV1:SexMen:PovStatBelow:CESD1 -5.8362 7.6243 376.0000 -0.77
## Pr(>|t|)
## (Intercept) <2e-16
## LifeIPV1 0.47
## SexMen 0.31
## PovStatBelow 0.14
## CESD1 0.17
## LifeIPV1:SexMen 0.97
## LifeIPV1:PovStatBelow 0.49
## SexMen:PovStatBelow 0.68
## LifeIPV1:CESD1 0.98
## SexMen:CESD1 0.57
## PovStatBelow:CESD1 0.73
## LifeIPV1:SexMen:PovStatBelow 1.00
## LifeIPV1:SexMen:CESD1 0.59
## LifeIPV1:PovStatBelow:CESD1 0.96
## SexMen:PovStatBelow:CESD1 0.54
## LifeIPV1:SexMen:PovStatBelow:CESD1 0.44
##
## Correlation of Fixed Effects:
## (Intr) LfIPV1 SexMen PvSttB CESD1 LfIPV1:SM LfIPV1:PSB
## LifeIPV1 -0.513
## SexMen -0.675 0.355
## PovStatBelw -0.528 0.276 0.363
## CESD1 -0.543 0.291 0.370 0.290
## LfIPV1:SxMn 0.162 -0.320 -0.245 -0.088 -0.093
## LfIPV1:PvSB 0.316 -0.620 -0.213 -0.600 -0.179 0.197
## SxMn:PvSttB 0.374 -0.195 -0.555 -0.710 -0.206 0.136 0.424
## LIPV1:CESD1 0.350 -0.679 -0.237 -0.189 -0.642 0.216 0.420
## SexMn:CESD1 0.369 -0.196 -0.545 -0.199 -0.676 0.139 0.118
## PvStB:CESD1 0.350 -0.187 -0.242 -0.659 -0.645 0.067 0.398
## LfIPV1:SM:PSB -0.110 0.220 0.165 0.214 0.070 -0.690 -0.357
## LIPV1:SM:CE -0.122 0.241 0.184 0.067 0.228 -0.750 -0.148
## LIPV1:PSB:C -0.236 0.460 0.159 0.448 0.438 -0.151 -0.740
## SM:PSB:CESD -0.234 0.124 0.349 0.441 0.431 -0.089 -0.260
## LIPV1:SM:PSB: 0.091 -0.178 -0.135 -0.173 -0.176 0.556 0.285
## SxM:PSB LIPV1:C SM:CES PSB:CE LfIPV1:SM:PSB LIPV1:SM:C
## LifeIPV1
## SexMen
## PovStatBelw
## CESD1
## LfIPV1:SxMn
## LfIPV1:PvSB
## SxMn:PvSttB
## LIPV1:CESD1 0.133
## SexMn:CESD1 0.306 0.433
## PvStB:CESD1 0.472 0.415 0.438
## LfIPV1:SM:PSB -0.302 -0.154 -0.100 -0.150
## LIPV1:SM:CE -0.102 -0.357 -0.342 -0.150 0.520
## LIPV1:PSB:C -0.318 -0.681 -0.293 -0.676 0.269 0.244
## SM:PSB:CESD -0.626 -0.276 -0.640 -0.671 0.192 0.216
## LIPV1:SM:PSB: 0.245 0.269 0.257 0.268 -0.804 -0.741
## LIPV1:PSB: SM:PSB:
## LifeIPV1
## SexMen
## PovStatBelw
## CESD1
## LfIPV1:SxMn
## LfIPV1:PvSB
## SxMn:PvSttB
## LIPV1:CESD1
## SexMn:CESD1
## PvStB:CESD1
## LfIPV1:SM:PSB
## LIPV1:SM:CE
## LIPV1:PSB:C
## SM:PSB:CESD 0.447
## LIPV1:SM:PSB: -0.391 -0.393