Fluency (Word) Regression Model 1
## Loading required package: Matrix
## KernSmooth 2.23 loaded
## Copyright M. P. Wand 1997-2009
##
## Attaching package: 'lmerTest'
##
## The following object is masked from 'package:lme4':
##
## lmer
##
## The following object is masked from 'package:stats':
##
## step
## Warning: number of observations <= rank(Z); variance-covariance matrix
## will be unidentifiable
## Linear mixed model fit by REML ['merModLmerTest']
## Formula: FluencyWord ~ (Age + IPVstatus + Sex + PovStat)^4 + (Age | HNDid) + (1 | subclass)
## Data: IPVandCognitionDataSet2
## REML criterion at convergence: 734
## Random effects:
## Groups Name Std.Dev. Corr
## HNDid (Intercept) 6.674879
## Age 0.190552 1.00
## subclass (Intercept) 0.000207
## Residual 3.306109
## Number of obs: 126, groups: HNDid, 63; subclass, 21
## Fixed Effects:
## (Intercept) Age
## 17.1247 -0.3395
## IPVstatus1 SexMen
## 3.8133 6.8968
## PovStatBelow Age:IPVstatus1
## -2.7692 0.4190
## Age:SexMen Age:PovStatBelow
## 0.3221 0.1136
## IPVstatus1:SexMen IPVstatus1:PovStatBelow
## -2.5374 0.3843
## SexMen:PovStatBelow Age:IPVstatus1:SexMen
## -0.4661 -0.4154
## Age:IPVstatus1:PovStatBelow Age:SexMen:PovStatBelow
## 0.0147 -0.2484
## IPVstatus1:SexMen:PovStatBelow Age:IPVstatus1:SexMen:PovStatBelow
## -4.1289 -0.0112
## Warning: number of observations <= rank(Z); variance-covariance matrix will be unidentifiable
## Warning: number of observations <= rank(Z); variance-covariance matrix will be unidentifiable
## Random term (Age | HNDid) was eliminated because of having correlation +-1 or NaN
## Warning: number of observations <= rank(Z); variance-covariance matrix
## will be unidentifiable
## Random term (Age + 0 | HNDid) was eliminated because of standard deviation being equal to 0
##
## Random term (1 | subclass) was eliminated because of standard deviation being equal to 0
##
## Random effects:
## Chi.sq Chi.DF elim.num p.value
## (1 | HNDid) 32.97 1 kept < 1e-07
##
## Fixed effects:
## Sum Sq Mean Sq NumDF DenDF F.value elim.num
## Age:IPVstatus:Sex:PovStat 0.2058 0.2058 1 109.62 0.0171 1
## IPVstatus:Sex:PovStat 4.9810 4.9810 1 57.99 0.4170 2
## Age:IPVstatus:PovStat 2.1765 2.1765 1 111.99 0.3176 3
## IPVstatus:PovStat 4.2670 4.2670 1 56.47 0.1922 4
## Age:Sex:PovStat 3.8761 3.8761 1 110.20 0.1672 5
## Sex:PovStat 0.3764 0.3764 1 60.42 0.0054 6
## Age:PovStat 2.8484 2.8484 1 108.75 0.0552 7
## Age:IPVstatus:Sex 8.9212 8.9212 1 111.79 1.2422 8
## Age:Sex 1.4435 1.4435 1 114.02 0.1261 9
## IPVstatus:Sex 1.8697 1.8697 1 56.02 0.1771 10
## Age:IPVstatus 2.1851 2.1851 1 113.68 0.1953 11
## IPVstatus 0.0000 0.0000 1 56.98 0.0396 12
## Age 60.5725 60.5725 1 115.41 4.2756 kept
## Sex 105.7626 105.7626 1 58.23 10.2953 kept
## PovStat 67.5447 67.5447 1 60.11 6.1562 kept
## Pr(>F)
## Age:IPVstatus:Sex:PovStat 0.8961
## IPVstatus:Sex:PovStat 0.5210
## Age:IPVstatus:PovStat 0.5742
## IPVstatus:PovStat 0.6628
## Age:Sex:PovStat 0.6834
## Sex:PovStat 0.9418
## Age:PovStat 0.8147
## Age:IPVstatus:Sex 0.2674
## Age:Sex 0.7232
## IPVstatus:Sex 0.6755
## Age:IPVstatus 0.6594
## IPVstatus 0.8430
## Age 0.0409
## Sex 0.0022
## PovStat 0.0159
##
## Least squares means:
## Sex PovStat Estimate Standard Error DF t-value Lower CI
## Sex Women 2.0 NA 18.089 0.937 57.9 19.3 16.2
## Sex Men 1.0 NA 22.322 0.996 58.5 22.4 20.3
## PovStat Above NA 1.0 21.994 0.801 58.6 27.4 20.4
## PovStat Below NA 2.0 18.418 1.181 59.5 15.6 16.1
## Upper CI p-value
## Sex Women 20.0 <2e-16
## Sex Men 24.3 <2e-16
## PovStat Above 23.6 <2e-16
## PovStat Below 20.8 <2e-16
##
## Differences of LSMEANS:
## Estimate Standard Error DF t-value Lower CI Upper CI
## Sex Women-Men -4.2 1.32 58.2 -3.21 -6.873 -1.59
## PovStat Above-Below 3.6 1.44 60.1 2.48 0.693 6.46
## p-value
## Sex Women-Men 0.002
## PovStat Above-Below 0.016
##
## Final model:
## lme4::lmer(formula = FluencyWord ~ Age + Sex + PovStat + (1 |
## HNDid), data = IPVandCognitionDataSet2, REML = reml, contrasts = l)
Re-run suggested final Model 1
(mm1 = lmer(FluencyWord ~ Age + Sex + PovStat + (Age | HNDid) + (1 | subclass),
data = IPVandCognitionDataSet2))
## Warning: number of observations <= rank(Z); variance-covariance matrix
## will be unidentifiable
## Linear mixed model fit by REML ['merModLmerTest']
## Formula: FluencyWord ~ Age + Sex + PovStat + (Age | HNDid) + (1 | subclass)
## Data: IPVandCognitionDataSet2
## REML criterion at convergence: 754.7
## Random effects:
## Groups Name Std.Dev. Corr
## HNDid (Intercept) 5.360750
## Age 0.079632 1.00
## subclass (Intercept) 0.000524
## Residual 3.290914
## Number of obs: 126, groups: HNDid, 63; subclass, 21
## Fixed Effects:
## (Intercept) Age SexMen PovStatBelow
## 18.938 -0.147 4.110 -3.570
summary(mm1)
## Warning: number of observations <= rank(Z); variance-covariance matrix will be unidentifiable
## Warning: number of observations <= rank(Z); variance-covariance matrix will be unidentifiable
## Linear mixed model fit by REML ['merModLmerTest']
## Formula: FluencyWord ~ Age + Sex + PovStat + (Age | HNDid) + (1 | subclass)
## Data: IPVandCognitionDataSet2
##
## REML criterion at convergence: 754.7
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## HNDid (Intercept) 2.87e+01 5.360750
## Age 6.34e-03 0.079632 1.00
## subclass (Intercept) 2.74e-07 0.000524
## Residual 1.08e+01 3.290914
## Number of obs: 126, groups: HNDid, 63; subclass, 21
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 18.9385 1.3097 34.4000 14.46 4.4e-16
## Age -0.1470 0.0828 66.9000 -1.78 0.0803
## SexMen 4.1098 1.3357 58.6000 3.08 0.0032
## PovStatBelow -3.5696 1.4719 57.4000 -2.43 0.0185
##
## Correlation of Fixed Effects:
## (Intr) Age SexMen
## Age 0.658
## SexMen -0.513 -0.095
## PovStatBelw -0.438 -0.183 0.005
plot(st)
plot(mm1)
Fluency (Word) Regression Model 2 (with CES)
load("~/Desktop/Megan/Research/IPV and Cognition Paper/IPV R Output/IPVandCognitionDataSet2.rda")
library(lme4)
library(lmerTest)
(mm2 = lmer(FluencyWord ~ (Age + IPVstatus + Sex + PovStat + CES1)^5 + (Age |
HNDid) + (1 | subclass), data = IPVandCognitionDataSet2))
## Warning: number of observations <= rank(Z); variance-covariance matrix
## will be unidentifiable
## Linear mixed model fit by REML ['merModLmerTest']
## Formula: FluencyWord ~ (Age + IPVstatus + Sex + PovStat + CES1)^5 + (Age | HNDid) + (1 | subclass)
## Data: IPVandCognitionDataSet2
## REML criterion at convergence: 672.9
## Random effects:
## Groups Name Std.Dev. Corr
## HNDid (Intercept) 6.29e+00
## Age 1.71e-01 1.00
## subclass (Intercept) 2.28e-05
## Residual 3.48e+00
## Number of obs: 126, groups: HNDid, 63; subclass, 21
## Fixed Effects:
## (Intercept)
## 18.8238
## Age
## -0.3028
## IPVstatus1
## 2.5449
## SexMen
## 8.1998
## PovStatBelow
## -2.7206
## CES11
## -3.8723
## Age:IPVstatus1
## 0.6012
## Age:SexMen
## 0.3839
## Age:PovStatBelow
## 0.2195
## Age:CES11
## -0.0975
## IPVstatus1:SexMen
## -4.5686
## IPVstatus1:PovStatBelow
## -3.0481
## IPVstatus1:CES11
## 3.3879
## SexMen:PovStatBelow
## -5.6642
## SexMen:CES11
## -10.1661
## PovStatBelow:CES11
## 1.1385
## Age:IPVstatus1:SexMen
## -0.6824
## Age:IPVstatus1:PovStatBelow
## -0.3179
## Age:IPVstatus1:CES11
## -0.3208
## Age:SexMen:PovStatBelow
## -0.5475
## Age:SexMen:CES11
## -0.3703
## Age:PovStatBelow:CES11
## -0.1134
## IPVstatus1:SexMen:PovStatBelow
## 6.1938
## IPVstatus1:SexMen:CES11
## 10.0368
## IPVstatus1:PovStatBelow:CES11
## 2.4319
## SexMen:PovStatBelow:CES11
## 19.7528
## Age:IPVstatus1:SexMen:PovStatBelow
## 0.5514
## Age:IPVstatus1:SexMen:CES11
## 0.6314
## Age:IPVstatus1:PovStatBelow:CES11
## 0.3409
## Age:SexMen:PovStatBelow:CES11
## 0.9804
## IPVstatus1:SexMen:PovStatBelow:CES11
## -25.5100
## Age:IPVstatus1:SexMen:PovStatBelow:CES11
## -1.0208
(st = step(mm2))
## Warning: number of observations <= rank(Z); variance-covariance matrix will be unidentifiable
## Warning: number of observations <= rank(Z); variance-covariance matrix will be unidentifiable
## Random term (Age | HNDid) was eliminated because of having correlation +-1 or NaN
## Warning: number of observations <= rank(Z); variance-covariance matrix
## will be unidentifiable
## Random term (Age + 0 | HNDid) was eliminated because of standard deviation being equal to 0
##
## Random term (1 | subclass) was eliminated because of standard deviation being equal to 0
##
## Random effects:
## Chi.sq Chi.DF elim.num p.value
## (1 | HNDid) 23.08 1 kept 0
##
## Fixed effects:
## Sum Sq Mean Sq NumDF DenDF F.value
## Age:IPVstatus:Sex:PovStat:CES1 1.4233 1.4233 1 67.96 0.1094
## Age:IPVstatus:Sex:PovStat 0.6362 0.6362 1 72.27 0.0003
## Age:IPVstatus:PovStat:CES1 0.8953 0.8953 1 76.06 0.0011
## Age:IPVstatus:PovStat 0.0019 0.0019 1 83.45 0.0018
## Age:IPVstatus:Sex:CES1 0.7355 0.7355 1 95.86 0.0098
## Age:IPVstatus:Sex 8.4292 8.4292 1 97.60 0.2338
## Age:IPVstatus:CES1 14.5610 14.5610 1 100.00 0.2092
## Age:Sex:PovStat:CES1 13.8055 13.8055 1 93.12 0.8377
## Age:Sex:CES1 3.6410 3.6410 1 99.82 0.0442
## Age:Sex:PovStat 4.9768 4.9768 1 97.81 0.1678
## Age:Sex 1.2138 1.2138 1 103.87 0.0775
## Age:PovStat:CES1 6.2860 6.2860 1 104.45 0.7376
## Age:PovStat 2.4022 2.4022 1 98.62 0.0215
## Age:IPVstatus 3.4952 3.4952 1 103.06 0.4520
## Age:CES1 3.1371 3.1371 1 99.20 1.0587
## IPVstatus:Sex:PovStat:CES1 7.6855 7.6855 1 44.65 2.4357
## IPVstatus:Sex:CES1 4.8691 4.8691 1 46.01 0.1713
## IPVstatus:Sex:PovStat 8.1055 8.1055 1 46.66 1.1244
## IPVstatus:Sex 1.0108 1.0108 1 47.47 0.0301
## Sex:PovStat:CES1 18.9302 18.9302 1 48.60 0.9252
## Sex:PovStat 2.2484 2.2484 1 51.05 0.5597
## Sex:CES1 8.5038 8.5038 1 50.56 0.7283
## IPVstatus:PovStat:CES1 13.6571 13.6571 1 51.58 2.0764
## IPVstatus:PovStat 6.1151 6.1151 1 52.74 0.6488
## PovStat:CES1 37.4172 37.4172 1 53.72 1.4555
## IPVstatus:CES1 35.0329 35.0329 1 55.12 3.4278
## IPVstatus 0.0004 0.0004 1 56.04 0.1486
## CES1 7.8188 7.8188 1 57.57 0.5323
## Age 72.3776 72.3776 1 115.41 4.2756
## Sex 120.5149 120.5149 1 58.23 10.2953
## PovStat 74.7668 74.7668 1 60.11 6.1562
## elim.num Pr(>F)
## Age:IPVstatus:Sex:PovStat:CES1 1 0.7418
## Age:IPVstatus:Sex:PovStat 2 0.9853
## Age:IPVstatus:PovStat:CES1 3 0.9742
## Age:IPVstatus:PovStat 4 0.9659
## Age:IPVstatus:Sex:CES1 5 0.9213
## Age:IPVstatus:Sex 6 0.6298
## Age:IPVstatus:CES1 7 0.6484
## Age:Sex:PovStat:CES1 8 0.3624
## Age:Sex:CES1 9 0.8339
## Age:Sex:PovStat 10 0.6830
## Age:Sex 11 0.7813
## Age:PovStat:CES1 12 0.3924
## Age:PovStat 13 0.8837
## Age:IPVstatus 14 0.5029
## Age:CES1 15 0.3060
## IPVstatus:Sex:PovStat:CES1 16 0.1257
## IPVstatus:Sex:CES1 17 0.6809
## IPVstatus:Sex:PovStat 18 0.2944
## IPVstatus:Sex 19 0.8630
## Sex:PovStat:CES1 20 0.3409
## Sex:PovStat 21 0.4578
## Sex:CES1 22 0.3975
## IPVstatus:PovStat:CES1 23 0.1556
## IPVstatus:PovStat 24 0.4242
## PovStat:CES1 25 0.2329
## IPVstatus:CES1 26 0.0695
## IPVstatus 27 0.7013
## CES1 28 0.4686
## Age kept 0.0409
## Sex kept 0.0022
## PovStat kept 0.0159
##
## Least squares means:
## Sex PovStat Estimate Standard Error DF t-value Lower CI
## Sex Women 2.0 NA 18.089 0.937 57.9 19.3 16.2
## Sex Men 1.0 NA 22.322 0.996 58.5 22.4 20.3
## PovStat Above NA 1.0 21.994 0.801 58.6 27.4 20.4
## PovStat Below NA 2.0 18.418 1.181 59.5 15.6 16.1
## Upper CI p-value
## Sex Women 20.0 <2e-16
## Sex Men 24.3 <2e-16
## PovStat Above 23.6 <2e-16
## PovStat Below 20.8 <2e-16
##
## Differences of LSMEANS:
## Estimate Standard Error DF t-value Lower CI Upper CI
## Sex Women-Men -4.2 1.32 58.2 -3.21 -6.873 -1.59
## PovStat Above-Below 3.6 1.44 60.1 2.48 0.693 6.46
## p-value
## Sex Women-Men 0.002
## PovStat Above-Below 0.016
##
## Final model:
## lme4::lmer(formula = FluencyWord ~ Age + Sex + PovStat + (1 |
## HNDid), data = IPVandCognitionDataSet2, REML = reml, contrasts = l)
Re-run suggested final Model 2
(mm2 = lmer(FluencyWord ~ Age + Sex + PovStat + (Age | HNDid) + (1 | subclass),
data = IPVandCognitionDataSet2))
## Warning: number of observations <= rank(Z); variance-covariance matrix
## will be unidentifiable
## Linear mixed model fit by REML ['merModLmerTest']
## Formula: FluencyWord ~ Age + Sex + PovStat + (Age | HNDid) + (1 | subclass)
## Data: IPVandCognitionDataSet2
## REML criterion at convergence: 754.7
## Random effects:
## Groups Name Std.Dev. Corr
## HNDid (Intercept) 5.360750
## Age 0.079632 1.00
## subclass (Intercept) 0.000524
## Residual 3.290914
## Number of obs: 126, groups: HNDid, 63; subclass, 21
## Fixed Effects:
## (Intercept) Age SexMen PovStatBelow
## 18.938 -0.147 4.110 -3.570
summary(mm2)
## Warning: number of observations <= rank(Z); variance-covariance matrix will be unidentifiable
## Warning: number of observations <= rank(Z); variance-covariance matrix will be unidentifiable
## Linear mixed model fit by REML ['merModLmerTest']
## Formula: FluencyWord ~ Age + Sex + PovStat + (Age | HNDid) + (1 | subclass)
## Data: IPVandCognitionDataSet2
##
## REML criterion at convergence: 754.7
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## HNDid (Intercept) 2.87e+01 5.360750
## Age 6.34e-03 0.079632 1.00
## subclass (Intercept) 2.74e-07 0.000524
## Residual 1.08e+01 3.290914
## Number of obs: 126, groups: HNDid, 63; subclass, 21
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 18.9385 1.3097 34.4000 14.46 4.4e-16
## Age -0.1470 0.0828 66.9000 -1.78 0.0803
## SexMen 4.1098 1.3357 58.6000 3.08 0.0032
## PovStatBelow -3.5696 1.4719 57.4000 -2.43 0.0185
##
## Correlation of Fixed Effects:
## (Intr) Age SexMen
## Age 0.658
## SexMen -0.513 -0.095
## PovStatBelw -0.438 -0.183 0.005
plot(st)
plot(mm2)