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 maximum likelihood ['merModLmerTest']
## Formula: FluencyWord ~ (Age + IPVstatus + Sex + Race)^4 + (Age | HNDid) + (1 | subclass)
## Data: IPVandCognitionDataSet2
## AIC BIC logLik deviance
## 782.2 841.7 -370.1 740.2
## Random effects:
## Groups Name Std.Dev. Corr
## HNDid (Intercept) 2.8559
## Age 0.0545 -1.00
## subclass (Intercept) 2.0241
## Residual 3.3529
## Number of obs: 126, groups: HNDid, 63; subclass, 21
## Fixed Effects:
## (Intercept) Age
## 30.375 0.808
## IPVstatus1 SexMen
## -8.075 -1.577
## RaceAfrAm Age:IPVstatus1
## -14.405 -0.675
## Age:SexMen Age:RaceAfrAm
## -0.642 -0.839
## IPVstatus1:SexMen IPVstatus1:RaceAfrAm
## 4.298 10.520
## SexMen:RaceAfrAm Age:IPVstatus1:SexMen
## 5.467 0.238
## Age:IPVstatus1:RaceAfrAm Age:SexMen:RaceAfrAm
## 0.667 0.735
## IPVstatus1:SexMen:RaceAfrAm Age:IPVstatus1:SexMen:RaceAfrAm
## -9.255 -0.624
## Warning: number of observations <= rank(Z); variance-covariance matrix will be unidentifiable
## Warning:
## model has been refitted with REML=TRUE
##
## Warning: number of observations <= rank(Z); variance-covariance matrix will be unidentifiable
## Warning: number of observations <= rank(Z); variance-covariance matrix will be unidentifiable
## Warning: number of observations <= rank(Z); variance-covariance matrix will be unidentifiable
## 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 effects:
## Chi.sq Chi.DF elim.num p.value
## (Age | HNDid) 0.21 1 1 0.6507
## (Age + 0 | HNDid) 0.04 1 2 0.8356
## (1 | subclass) 1.76 1 3 0.1852
## (1 | HNDid) 29.22 1 kept <1e-07
##
## Fixed effects:
## Sum Sq Mean Sq NumDF DenDF F.value elim.num
## Age:IPVstatus:Sex:Race 8.1710 8.1710 1 107.83 0.7087 1
## Age:IPVstatus:Sex 4.4384 4.4384 1 107.52 0.1495 2
## IPVstatus:Sex:Race 6.3902 6.3902 1 53.17 0.6160 3
## IPVstatus:Sex 0.1162 0.1162 1 54.08 0.0206 4
## Age:IPVstatus:Race 13.4025 13.4025 1 111.53 1.0931 5
## IPVstatus:Race 17.8292 17.8292 1 55.61 1.1440 6
## Age:Sex:Race 12.7817 12.7817 1 114.20 1.6792 7
## Sex:Race 2.2889 2.2889 1 59.23 0.4235 8
## Age:IPVstatus 17.6845 17.6845 1 114.07 1.6890 9
## IPVstatus 1.7013 1.7013 1 56.53 0.0322 10
## Age:Sex 20.6023 20.6023 1 117.26 2.9158 11
## Age:Race 57.6390 57.6390 1 120.66 3.8492 12
## Age 0.1338 0.1338 1 119.65 0.3519 13
## Sex 80.2474 80.2474 1 60.00 5.2225 kept
## Race 312.6800 312.6800 1 60.00 25.6343 kept
## Pr(>F)
## Age:IPVstatus:Sex:Race 0.4017
## Age:IPVstatus:Sex 0.6997
## IPVstatus:Sex:Race 0.4360
## IPVstatus:Sex 0.8864
## Age:IPVstatus:Race 0.2981
## IPVstatus:Race 0.2894
## Age:Sex:Race 0.1976
## Sex:Race 0.5177
## Age:IPVstatus 0.1964
## IPVstatus 0.8582
## Age:Sex 0.0904
## Age:Race 0.0521
## Age 0.5541
## Sex 0.0258
## Race 0
##
## Least squares means:
## Sex Race Estimate Standard Error DF t-value Lower CI Upper CI
## Sex Women 2 NA 20.329 0.972 60 20.9 18.4 22.3
## Sex Men 1 NA 23.320 0.913 60 25.6 21.5 25.1
## Race White NA 2 25.262 1.083 60 23.3 23.1 27.4
## Race AfrAm NA 1 18.387 0.819 60 22.5 16.7 20.0
## p-value
## Sex Women <2e-16
## Sex Men <2e-16
## Race White <2e-16
## Race AfrAm <2e-16
##
## Differences of LSMEANS:
## Estimate Standard Error DF t-value Lower CI Upper CI
## Sex Women-Men -3.0 1.31 60.0 -2.29 -5.61 -0.373
## Race White-AfrAm 6.9 1.36 60.0 5.06 4.16 9.590
## p-value
## Sex Women-Men 0.03
## Race White-AfrAm <2e-16
##
## Final model:
## lme4::lmer(formula = FluencyWord ~ Sex + Race + (1 | HNDid),
## data = IPVandCognitionDataSet2, REML = reml, contrasts = l)
Re-run suggested final Model 1
(mm1 = lmer(FluencyWord ~ Sex + Race + (1 | HNDid), data = IPVandCognitionDataSet2,
REML = F))
## Linear mixed model fit by maximum likelihood ['merModLmerTest']
## Formula: FluencyWord ~ Sex + Race + (1 | HNDid)
## Data: IPVandCognitionDataSet2
## AIC BIC logLik deviance
## 768.0 782.2 -379.0 758.0
## Random effects:
## Groups Name Std.Dev.
## HNDid (Intercept) 4.46
## Residual 3.36
## Number of obs: 126, groups: HNDid, 63
## Fixed Effects:
## (Intercept) SexMen RaceAfrAm
## 23.77 2.99 -6.87
summary(mm1)
## Linear mixed model fit by maximum likelihood ['merModLmerTest']
## Formula: FluencyWord ~ Sex + Race + (1 | HNDid)
## Data: IPVandCognitionDataSet2
##
## AIC BIC logLik deviance
## 768.0 782.2 -379.0 758.0
##
## Random effects:
## Groups Name Variance Std.Dev.
## HNDid (Intercept) 19.9 4.46
## Residual 11.3 3.36
## Number of obs: 126, groups: HNDid, 63
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 23.77 1.28 63.00 18.61 < 2e-16
## SexMen 2.99 1.28 63.00 2.34 0.022
## RaceAfrAm -6.87 1.32 63.00 -5.19 2.4e-06
##
## Correlation of Fixed Effects:
## (Intr) SexMen
## SexMen -0.565
## RaceAfrAm -0.692 0.063
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 + Race + CES1)^5 + (Age | HNDid) +
(1 | subclass), data = IPVandCognitionDataSet2, REML = F))
## Warning: number of observations <= rank(Z); variance-covariance matrix
## will be unidentifiable
## Linear mixed model fit by maximum likelihood ['merModLmerTest']
## Formula: FluencyWord ~ (Age + IPVstatus + Sex + Race + CES1)^5 + (Age | HNDid) + (1 | subclass)
## Data: IPVandCognitionDataSet2
## AIC BIC logLik deviance
## 777.8 882.7 -351.9 703.8
## Random effects:
## Groups Name Std.Dev. Corr
## HNDid (Intercept) 1.2483
## Age 0.0855 -1.00
## subclass (Intercept) 2.5322
## Residual 3.1320
## Number of obs: 126, groups: HNDid, 63; subclass, 21
## Fixed Effects:
## (Intercept)
## 30.165
## Age
## 0.859
## IPVstatus1
## -15.221
## SexMen
## -5.377
## RaceAfrAm
## -15.514
## CES11
## -0.457
## Age:IPVstatus1
## -0.693
## Age:SexMen
## -1.183
## Age:RaceAfrAm
## -1.057
## Age:CES11
## -0.359
## IPVstatus1:SexMen
## 10.664
## IPVstatus1:RaceAfrAm
## 22.485
## IPVstatus1:CES11
## 8.880
## SexMen:RaceAfrAm
## 11.410
## SexMen:CES11
## 11.973
## RaceAfrAm:CES11
## 3.492
## Age:IPVstatus1:SexMen
## 1.010
## Age:IPVstatus1:RaceAfrAm
## 1.206
## Age:IPVstatus1:CES11
## 0.235
## Age:SexMen:RaceAfrAm
## 1.490
## Age:SexMen:CES11
## 1.733
## Age:RaceAfrAm:CES11
## 0.730
## IPVstatus1:SexMen:RaceAfrAm
## -15.038
## IPVstatus1:SexMen:CES11
## 16.156
## IPVstatus1:RaceAfrAm:CES11
## -16.614
## SexMen:RaceAfrAm:CES11
## -16.971
## Age:IPVstatus1:SexMen:RaceAfrAm
## -1.633
## Age:IPVstatus1:SexMen:CES11
## 0.397
## Age:IPVstatus1:RaceAfrAm:CES11
## -1.251
## Age:SexMen:RaceAfrAm:CES11
## -2.174
## IPVstatus1:SexMen:RaceAfrAm:CES11
## -12.557
## Age:IPVstatus1:SexMen:RaceAfrAm:CES11
## 0.406
(st = step(mm2))
## Warning: number of observations <= rank(Z); variance-covariance matrix will be unidentifiable
## Warning:
## model has been refitted with REML=TRUE
##
## Warning: number of observations <= rank(Z); variance-covariance matrix will be unidentifiable
## Warning: number of observations <= rank(Z); variance-covariance matrix will be unidentifiable
## Warning: number of observations <= rank(Z); variance-covariance matrix will be unidentifiable
## 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 effects:
## Chi.sq Chi.DF elim.num p.value
## (Age | HNDid) 0.87 1 1 0.3504
## (Age + 0 | HNDid) 0.30 1 2 0.5819
## (1 | HNDid) 4.08 1 kept 0.0435
## (1 | subclass) 5.90 1 kept 0.0151
##
## Fixed effects:
## Sum Sq Mean Sq NumDF DenDF F.value
## Age:IPVstatus:Sex:Race:CES1 0.6118 0.6118 1 63.30 0.0567
## Age:IPVstatus:Sex:CES1 23.3460 23.3460 1 68.64 0.5175
## Age:IPVstatus:Sex:Race 24.3187 24.3187 1 84.77 0.6039
## Age:IPVstatus:Race:CES1 0.0012 0.0012 1 82.57 0.7614
## Age:IPVstatus:Sex 3.1290 3.1290 1 95.83 0.5048
## Age:IPVstatus:Race 6.4435 6.4435 1 93.72 0.3140
## Age:IPVstatus:CES1 15.8587 15.8587 1 90.37 1.1583
## Age:IPVstatus 26.3364 26.3364 1 81.91 0.0190
## IPVstatus:Sex:Race:CES1 18.3152 18.3152 1 43.16 1.9307
## IPVstatus:Sex:CES1 16.2610 16.2610 1 47.76 0.0805
## IPVstatus:Sex:Race 37.5555 37.5555 1 40.96 1.2702
## IPVstatus:Sex 2.2816 2.2816 1 49.14 0.9521
## Age 0.4828 0.4828 1 83.25 4.9629
## IPVstatus 2.6468 2.6468 1 39.27 0.1523
## Sex 88.6354 88.6354 1 82.10 8.0599
## Race 432.3779 432.3779 1 72.38 23.1707
## CES1 0.0406 0.0406 1 82.96 2.1164
## Age:Sex 20.0014 20.0014 1 103.69 0.0215
## Age:Race 53.2567 53.2567 1 105.77 7.5765
## Age:CES1 3.6740 3.6740 1 105.26 0.4812
## IPVstatus:Race 31.2891 31.2891 1 46.26 0.9382
## IPVstatus:CES1 31.5621 31.5621 1 48.05 3.8842
## Sex:Race 7.7817 7.7817 1 79.78 2.9825
## Sex:CES1 0.0003 0.0003 1 75.14 5.4104
## Race:CES1 31.1363 31.1363 1 77.03 5.7237
## Age:Sex:Race 33.1931 33.1931 1 104.25 0.4345
## Age:Sex:CES1 1.8964 1.8964 1 99.64 4.9887
## Age:Race:CES1 9.9870 9.9870 1 102.07 1.0768
## IPVstatus:Race:CES1 43.4721 43.4721 1 45.54 7.9501
## Sex:Race:CES1 7.2932 7.2932 1 79.86 12.8991
## Age:Sex:Race:CES1 51.1829 51.1829 1 98.35 11.8731
## elim.num Pr(>F)
## Age:IPVstatus:Sex:Race:CES1 1 0.8125
## Age:IPVstatus:Sex:CES1 2 0.4743
## Age:IPVstatus:Sex:Race 3 0.4392
## Age:IPVstatus:Race:CES1 4 0.3854
## Age:IPVstatus:Sex 5 0.4791
## Age:IPVstatus:Race 6 0.5766
## Age:IPVstatus:CES1 7 0.2847
## Age:IPVstatus 8 0.8908
## IPVstatus:Sex:Race:CES1 9 0.1718
## IPVstatus:Sex:CES1 10 0.7778
## IPVstatus:Sex:Race 11 0.2663
## IPVstatus:Sex 12 0.3340
## Age kept 0.0286
## IPVstatus kept 0.6985
## Sex kept 0.0057
## Race kept 0
## CES1 kept 0.1495
## Age:Sex kept 0.8837
## Age:Race kept 0.0070
## Age:CES1 kept 0.4894
## IPVstatus:Race kept 0.3378
## IPVstatus:CES1 kept 0.0545
## Sex:Race kept 0.0880
## Sex:CES1 kept 0.0227
## Race:CES1 kept 0.0192
## Age:Sex:Race kept 0.5112
## Age:Sex:CES1 kept 0.0277
## Age:Race:CES1 kept 0.3019
## IPVstatus:Race:CES1 kept 0.0071
## Sex:Race:CES1 kept 0.0006
## Age:Sex:Race:CES1 kept 0.0008
##
## Least squares means:
## IPVstatus Sex Race CES1 Estimate
## IPVstatus 0 1.0 NA NA NA 21.6
## IPVstatus 1 2.0 NA NA NA 22.1
## Sex Women NA 2.0 NA NA 19.1
## Sex Men NA 1.0 NA NA 24.6
## Race White NA NA 2.0 NA 24.8
## Race AfrAm NA NA 1.0 NA 18.9
## CES1 0 NA NA NA 1.0 20.8
## CES1 1 NA NA NA 2.0 22.9
## IPVstatus:Race 0 White 1.0 NA 2.0 NA 25.3
## IPVstatus:Race 1 White 2.0 NA 2.0 NA 24.4
## IPVstatus:Race 0 AfrAm 1.0 NA 1.0 NA 17.9
## IPVstatus:Race 1 AfrAm 2.0 NA 1.0 NA 19.9
## IPVstatus:CES1 0 0 1.0 NA NA 1.0 22.0
## IPVstatus:CES1 1 0 2.0 NA NA 1.0 19.6
## IPVstatus:CES1 0 1 1.0 NA NA 2.0 21.2
## IPVstatus:CES1 1 1 2.0 NA NA 2.0 24.6
## Sex:Race Women White NA 2.0 2.0 NA 20.8
## Sex:Race Men White NA 1.0 2.0 NA 28.9
## Sex:Race Women AfrAm NA 2.0 1.0 NA 17.5
## Sex:Race Men AfrAm NA 1.0 1.0 NA 20.3
## Sex:CES1 Women 0 NA 2.0 NA 1.0 19.0
## Sex:CES1 Men 0 NA 1.0 NA 1.0 22.7
## Sex:CES1 Women 1 NA 2.0 NA 2.0 19.2
## Sex:CES1 Men 1 NA 1.0 NA 2.0 26.6
## Race:CES1 White 0 NA NA 2.0 1.0 22.1
## Race:CES1 AfrAm 0 NA NA 1.0 1.0 19.6
## Race:CES1 White 1 NA NA 2.0 2.0 27.6
## Race:CES1 AfrAm 1 NA NA 1.0 2.0 18.2
## IPVstatus:Race:CES1 0 White 0 1.0 NA 2.0 1.0 26.0
## IPVstatus:Race:CES1 1 White 0 2.0 NA 2.0 1.0 18.1
## IPVstatus:Race:CES1 0 AfrAm 0 1.0 NA 1.0 1.0 18.1
## IPVstatus:Race:CES1 1 AfrAm 0 2.0 NA 1.0 1.0 21.1
## IPVstatus:Race:CES1 0 White 1 1.0 NA 2.0 2.0 24.5
## IPVstatus:Race:CES1 1 White 1 2.0 NA 2.0 2.0 30.7
## IPVstatus:Race:CES1 0 AfrAm 1 1.0 NA 1.0 2.0 17.8
## IPVstatus:Race:CES1 1 AfrAm 1 2.0 NA 1.0 2.0 18.6
## Sex:Race:CES1 Women White 0 NA 2.0 2.0 1.0 20.4
## Sex:Race:CES1 Men White 0 NA 1.0 2.0 1.0 23.7
## Sex:Race:CES1 Women AfrAm 0 NA 2.0 1.0 1.0 17.6
## Sex:Race:CES1 Men AfrAm 0 NA 1.0 1.0 1.0 21.6
## Sex:Race:CES1 Women White 1 NA 2.0 2.0 2.0 21.1
## Sex:Race:CES1 Men White 1 NA 1.0 2.0 2.0 34.1
## Sex:Race:CES1 Women AfrAm 1 NA 2.0 1.0 2.0 17.3
## Sex:Race:CES1 Men AfrAm 1 NA 1.0 1.0 2.0 19.1
## Standard Error DF t-value Lower CI
## IPVstatus 0 0.991 34.1 21.80 19.6
## IPVstatus 1 1.162 45.9 19.05 19.8
## Sex Women 1.091 39.7 17.51 16.9
## Sex Men 1.069 40.2 23.01 22.4
## Race White 1.213 43.8 20.47 22.4
## Race AfrAm 0.939 29.0 20.12 17.0
## CES1 0 1.065 37.3 19.57 18.7
## CES1 1 1.099 39.4 20.83 20.7
## IPVstatus:Race 0 White 1.587 47.8 15.92 22.1
## IPVstatus:Race 1 White 1.782 47.5 13.68 20.8
## IPVstatus:Race 0 AfrAm 1.032 36.5 17.37 15.8
## IPVstatus:Race 1 AfrAm 1.407 48.3 14.12 17.0
## IPVstatus:CES1 0 0 1.045 37.9 21.07 19.9
## IPVstatus:CES1 1 0 1.741 48.1 11.27 16.1
## IPVstatus:CES1 0 1 1.591 47.1 13.30 18.0
## IPVstatus:CES1 1 1 1.463 49.3 16.83 21.7
## Sex:Race Women White 1.752 50.2 11.86 17.3
## Sex:Race Men White 1.586 52.4 18.21 25.7
## Sex:Race Women AfrAm 1.175 37.0 14.86 15.1
## Sex:Race Men AfrAm 1.255 43.3 16.20 17.8
## Sex:CES1 Women 0 1.361 46.8 13.96 16.3
## Sex:CES1 Men 0 1.305 44.5 17.37 20.0
## Sex:CES1 Women 1 1.573 49.7 12.22 16.1
## Sex:CES1 Men 1 1.556 53.8 17.07 23.4
## Race:CES1 White 0 1.603 46.2 13.76 18.8
## Race:CES1 AfrAm 0 1.252 45.0 15.66 17.1
## Race:CES1 White 1 1.746 51.3 15.81 24.1
## Race:CES1 AfrAm 1 1.181 42.9 15.39 15.8
## IPVstatus:Race:CES1 0 White 0 1.554 49.2 16.74 22.9
## IPVstatus:Race:CES1 1 White 0 2.680 46.4 6.76 12.7
## IPVstatus:Race:CES1 0 AfrAm 0 1.310 42.8 13.78 15.4
## IPVstatus:Race:CES1 1 AfrAm 0 2.184 48.0 9.69 16.8
## IPVstatus:Race:CES1 0 White 1 2.722 50.4 9.01 19.1
## IPVstatus:Race:CES1 1 White 1 2.320 46.9 13.22 26.0
## IPVstatus:Race:CES1 0 AfrAm 1 1.465 48.2 12.14 14.8
## IPVstatus:Race:CES1 1 AfrAm 1 1.688 49.1 11.01 15.2
## Sex:Race:CES1 Women White 0 2.244 46.4 9.09 15.9
## Sex:Race:CES1 Men White 0 1.778 47.8 13.34 20.1
## Sex:Race:CES1 Women AfrAm 0 1.482 45.9 11.88 14.6
## Sex:Race:CES1 Men AfrAm 0 1.783 47.3 12.11 18.0
## Sex:Race:CES1 Women White 1 2.642 53.5 8.00 15.8
## Sex:Race:CES1 Men White 1 2.586 52.1 13.17 28.9
## Sex:Race:CES1 Women AfrAm 1 1.647 46.2 10.51 14.0
## Sex:Race:CES1 Men AfrAm 1 1.562 51.9 12.21 15.9
## Upper CI p-value
## IPVstatus 0 23.6 <2e-16
## IPVstatus 1 24.5 <2e-16
## Sex Women 21.3 <2e-16
## Sex Men 26.8 <2e-16
## Race White 27.3 <2e-16
## Race AfrAm 20.8 <2e-16
## CES1 0 23.0 <2e-16
## CES1 1 25.1 <2e-16
## IPVstatus:Race 0 White 28.5 <2e-16
## IPVstatus:Race 1 White 28.0 <2e-16
## IPVstatus:Race 0 AfrAm 20.0 <2e-16
## IPVstatus:Race 1 AfrAm 22.7 <2e-16
## IPVstatus:CES1 0 0 24.1 <2e-16
## IPVstatus:CES1 1 0 23.1 <2e-16
## IPVstatus:CES1 0 1 24.4 <2e-16
## IPVstatus:CES1 1 1 27.6 <2e-16
## Sex:Race Women White 24.3 <2e-16
## Sex:Race Men White 32.1 <2e-16
## Sex:Race Women AfrAm 19.8 <2e-16
## Sex:Race Men AfrAm 22.9 <2e-16
## Sex:CES1 Women 0 21.7 <2e-16
## Sex:CES1 Men 0 25.3 <2e-16
## Sex:CES1 Women 1 22.4 <2e-16
## Sex:CES1 Men 1 29.7 <2e-16
## Race:CES1 White 0 25.3 <2e-16
## Race:CES1 AfrAm 0 22.1 <2e-16
## Race:CES1 White 1 31.1 <2e-16
## Race:CES1 AfrAm 1 20.6 <2e-16
## IPVstatus:Race:CES1 0 White 0 29.1 <2e-16
## IPVstatus:Race:CES1 1 White 0 23.5 <2e-16
## IPVstatus:Race:CES1 0 AfrAm 0 20.7 <2e-16
## IPVstatus:Race:CES1 1 AfrAm 0 25.5 <2e-16
## IPVstatus:Race:CES1 0 White 1 30.0 <2e-16
## IPVstatus:Race:CES1 1 White 1 35.3 <2e-16
## IPVstatus:Race:CES1 0 AfrAm 1 20.7 <2e-16
## IPVstatus:Race:CES1 1 AfrAm 1 22.0 <2e-16
## Sex:Race:CES1 Women White 0 24.9 <2e-16
## Sex:Race:CES1 Men White 0 27.3 <2e-16
## Sex:Race:CES1 Women AfrAm 0 20.6 <2e-16
## Sex:Race:CES1 Men AfrAm 0 25.2 <2e-16
## Sex:Race:CES1 Women White 1 26.4 <2e-16
## Sex:Race:CES1 Men White 1 39.2 <2e-16
## Sex:Race:CES1 Women AfrAm 1 20.6 <2e-16
## Sex:Race:CES1 Men AfrAm 1 22.2 <2e-16
##
## Differences of LSMEANS:
## Estimate Standard Error DF t-value
## IPVstatus 0-1 -0.5 1.379 39.3 -0.39
## Sex Women-Men -5.5 1.381 48.1 -3.98
## Race White-AfrAm 5.9 1.395 47.8 4.26
## CES1 0-1 -2.1 1.386 48.1 -1.49
## IPVstatus:Race 0 White- 1 White 0.9 2.347 44.4 0.37
## IPVstatus:Race 0 White- 0 AfrAm 7.4 1.801 50.6 4.08
## IPVstatus:Race 0 White- 1 AfrAm 5.4 2.003 46.7 2.69
## IPVstatus:Race 1 White- 0 AfrAm 6.5 1.917 40.9 3.38
## IPVstatus:Race 1 White- 1 AfrAm 4.5 2.217 43.9 2.04
## IPVstatus:Race 0 AfrAm- 1 AfrAm -2.0 1.601 40.2 -1.22
## IPVstatus:CES1 0 0- 1 0 2.4 1.928 44.8 1.24
## IPVstatus:CES1 0 0- 0 1 0.9 1.823 50.0 0.48
## IPVstatus:CES1 0 0- 1 1 -2.6 1.608 37.7 -1.62
## IPVstatus:CES1 1 0- 0 1 -1.5 2.249 46.8 -0.68
## IPVstatus:CES1 1 0- 1 1 -5.0 2.225 46.3 -2.25
## IPVstatus:CES1 0 1- 1 1 -3.5 2.125 44.7 -1.63
## Sex:Race Women White- Men White -8.1 2.299 47.1 -3.53
## Sex:Race Women White- Women AfrAm 3.3 2.033 48.6 1.63
## Sex:Race Women White- Men AfrAm 0.4 2.075 49.6 0.21
## Sex:Race Men White- Women AfrAm 11.4 1.844 45.3 6.20
## Sex:Race Men White- Men AfrAm 8.6 1.899 45.4 4.51
## Sex:Race Women AfrAm- Men AfrAm -2.9 1.543 47.9 -1.86
## Sex:CES1 Women 0- Men 0 -3.7 1.604 41.4 -2.28
## Sex:CES1 Women 0- Women 1 -0.2 1.971 46.7 -0.12
## Sex:CES1 Women 0- Men 1 -7.6 1.953 45.1 -3.87
## Sex:CES1 Men 0- Women 1 3.4 1.960 49.9 1.75
## Sex:CES1 Men 0- Men 1 -3.9 1.917 46.9 -2.04
## Sex:CES1 Women 1- Men 1 -7.3 2.227 49.5 -3.29
## Race:CES1 White 0- AfrAm 0 2.5 1.934 44.4 1.27
## Race:CES1 White 0- White 1 -5.5 2.313 50.0 -2.40
## Race:CES1 White 0- AfrAm 1 3.9 1.877 46.1 2.06
## Race:CES1 AfrAm 0- White 1 -8.0 2.052 49.5 -3.90
## Race:CES1 AfrAm 0- AfrAm 1 1.4 1.549 40.3 0.91
## Race:CES1 White 1- AfrAm 1 9.4 2.014 50.7 4.67
## Lower CI Upper CI p-value
## IPVstatus 0-1 -3.3256 2.2496 0.699
## Sex Women-Men -8.2720 -2.7181 2e-04
## Race White-AfrAm 3.1319 8.7404 1e-04
## CES1 0-1 -4.8513 0.7210 0.143
## IPVstatus:Race 0 White- 1 White -3.8514 5.6060 0.710
## IPVstatus:Race 0 White- 0 AfrAm 3.7354 10.9673 2e-04
## IPVstatus:Race 0 White- 1 AfrAm 1.3670 9.4294 0.010
## IPVstatus:Race 1 White- 0 AfrAm 2.6018 10.3464 0.002
## IPVstatus:Race 1 White- 1 AfrAm 0.0520 8.9898 0.048
## IPVstatus:Race 0 AfrAm- 1 AfrAm -5.1877 1.2813 0.230
## IPVstatus:CES1 0 0- 1 0 -1.4882 6.2789 0.221
## IPVstatus:CES1 0 0- 0 1 -2.7929 4.5292 0.636
## IPVstatus:CES1 0 0- 1 1 -5.8586 0.6524 0.114
## IPVstatus:CES1 1 0- 0 1 -6.0516 2.9972 0.500
## IPVstatus:CES1 1 0- 1 1 -9.4756 -0.5214 0.029
## IPVstatus:CES1 0 1- 1 1 -7.7511 0.8085 0.109
## Sex:Race Women White- Men White -12.7447 -3.4969 9e-04
## Sex:Race Women White- Women AfrAm -0.7749 7.3956 0.110
## Sex:Race Women White- Men AfrAm -3.7272 4.6094 0.833
## Sex:Race Men White- Women AfrAm 7.7180 15.1444 <2e-16
## Sex:Race Men White- Men AfrAm 4.7378 12.3861 <2e-16
## Sex:Race Women AfrAm- Men AfrAm -5.9711 0.2327 0.069
## Sex:CES1 Women 0- Men 0 -6.8968 -0.4189 0.028
## Sex:CES1 Women 0- Women 1 -4.1945 3.7386 0.908
## Sex:CES1 Women 0- Men 1 -11.4931 -3.6273 3e-04
## Sex:CES1 Men 0- Women 1 -0.5078 7.3676 0.086
## Sex:CES1 Men 0- Men 1 -7.7585 -0.0463 0.047
## Sex:CES1 Women 1- Men 1 -11.8072 -2.8574 0.002
## Race:CES1 White 0- AfrAm 0 -1.4383 6.3561 0.210
## Race:CES1 White 0- White 1 -10.1880 -0.8969 0.020
## Race:CES1 White 0- AfrAm 1 0.0939 7.6481 0.045
## Race:CES1 AfrAm 0- White 1 -12.1230 -3.8796 3e-04
## Race:CES1 AfrAm 0- AfrAm 1 -1.7175 4.5417 0.367
## Race:CES1 White 1- AfrAm 1 5.3697 13.4571 <2e-16
##
## Final model:
## lme4::lmer(formula = FluencyWord ~ Age + IPVstatus + Sex + Race +
## CES1 + (1 | subclass) + (1 | HNDid) + Age:Sex + Age:Race +
## Age:CES1 + IPVstatus:Race + IPVstatus:CES1 + Sex:Race + Sex:CES1 +
## Race:CES1 + Age:Sex:Race + Age:Sex:CES1 + Age:Race:CES1 +
## IPVstatus:Race:CES1 + Sex:Race:CES1 + Age:Sex:Race:CES1,
## data = IPVandCognitionDataSet2, REML = reml, contrasts = l)
Re-run suggested final Model 2
(mm2 = lmer(FluencyWord ~ Age + IPVstatus + Sex + Race + CES1 + (1 | subclass) +
(1 | HNDid) + Age:Sex + Age:Race + Age:CES1 + IPVstatus:Race + IPVstatus +
CES1 + Sex:Race + Sex:CES1 + Race:CES1 + Age:Sex:Race + Age:Sex:CES1 + Age:Race:CES1 +
IPVstatus:Race:CES1 + Sex:Race:CES1 + Age:Sex:Race:CES1, data = IPVandCognitionDataSet2,
REML = F))
## Linear mixed model fit by maximum likelihood ['merModLmerTest']
## Formula: FluencyWord ~ Age + IPVstatus + Sex + Race + CES1 + (1 | subclass) + (1 | HNDid) + Age:Sex + Age:Race + Age:CES1 + IPVstatus:Race + IPVstatus + CES1 + Sex:Race + Sex:CES1 + Race:CES1 + Age:Sex:Race + Age:Sex:CES1 + Age:Race:CES1 + IPVstatus:Race:CES1 + Sex:Race:CES1 + Age:Sex:Race:CES1
## Data: IPVandCognitionDataSet2
## AIC BIC logLik deviance
## 762.6 827.9 -358.3 716.6
## Random effects:
## Groups Name Std.Dev.
## HNDid (Intercept) 2.67
## subclass (Intercept) 2.25
## Residual 3.09
## Number of obs: 126, groups: HNDid, 63; subclass, 21
## Fixed Effects:
## (Intercept) Age
## 29.464 0.792
## IPVstatus1 SexMen
## -7.825 -2.820
## RaceAfrAm CES11
## -13.938 -10.784
## Age:SexMen Age:RaceAfrAm
## -0.937 -0.874
## Age:CES11 IPVstatus1:RaceAfrAm
## -0.743 10.884
## SexMen:RaceAfrAm SexMen:CES11
## 7.612 23.087
## RaceAfrAm:CES11 Age:SexMen:RaceAfrAm
## 12.810 1.056
## Age:SexMen:CES11 Age:RaceAfrAm:CES11
## 2.096 0.918
## IPVstatus1:RaceWhite:CES11 IPVstatus1:RaceAfrAm:CES11
## 13.724 -2.284
## SexMen:RaceAfrAm:CES11 Age:SexMen:RaceAfrAm:CES11
## -28.153 -2.507
summary(mm2)
## Linear mixed model fit by maximum likelihood ['merModLmerTest']
## Formula: FluencyWord ~ Age + IPVstatus + Sex + Race + CES1 + (1 | subclass) + (1 | HNDid) + Age:Sex + Age:Race + Age:CES1 + IPVstatus:Race + IPVstatus + CES1 + Sex:Race + Sex:CES1 + Race:CES1 + Age:Sex:Race + Age:Sex:CES1 + Age:Race:CES1 + IPVstatus:Race:CES1 + Sex:Race:CES1 + Age:Sex:Race:CES1
## Data: IPVandCognitionDataSet2
##
## AIC BIC logLik deviance
## 762.6 827.9 -358.3 716.6
##
## Random effects:
## Groups Name Variance Std.Dev.
## HNDid (Intercept) 7.11 2.67
## subclass (Intercept) 5.06 2.25
## Residual 9.55 3.09
## Number of obs: 126, groups: HNDid, 63; subclass, 21
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 29.464 2.167 78.900 13.59 < 2e-16
## Age 0.792 0.256 121.300 3.10 0.00241
## IPVstatus1 -7.825 2.557 58.900 -3.06 0.00333
## SexMen -2.820 2.806 68.500 -1.00 0.31847
## RaceAfrAm -13.938 2.794 79.300 -4.99 3.5e-06
## CES11 -10.784 4.926 87.400 -2.19 0.03123
## Age:SexMen -0.937 0.308 105.800 -3.04 0.00297
## Age:RaceAfrAm -0.874 0.305 120.100 -2.87 0.00490
## Age:CES11 -0.743 0.395 118.800 -1.88 0.06285
## IPVstatus1:RaceAfrAm 10.884 3.478 64.300 3.13 0.00264
## SexMen:RaceAfrAm 7.612 3.637 78.900 2.09 0.03958
## SexMen:CES11 23.087 6.259 103.300 3.69 0.00036
## RaceAfrAm:CES11 12.810 5.602 87.300 2.29 0.02462
## Age:SexMen:RaceAfrAm 1.056 0.389 117.100 2.71 0.00764
## Age:SexMen:CES11 2.096 0.580 114.000 3.61 0.00045
## Age:RaceAfrAm:CES11 0.918 0.455 118.000 2.02 0.04604
## IPVstatus1:RaceWhite:CES11 13.724 4.066 58.400 3.38 0.00132
## IPVstatus1:RaceAfrAm:CES11 -2.284 2.977 64.300 -0.77 0.44565
## SexMen:RaceAfrAm:CES11 -28.153 7.054 99.200 -3.99 0.00013
## Age:SexMen:RaceAfrAm:CES11 -2.507 0.671 114.100 -3.73 0.00030
##
## Correlation of Fixed Effects:
## (Intr) Age IPVst1 SexMen RcAfrA CES11 Ag:SxM Ag:RAA A:CES1
## Age 0.544
## IPVstatus1 -0.086 0.238
## SexMen -0.650 -0.448 -0.221
## RaceAfrAm -0.766 -0.425 0.070 0.509
## CES11 -0.435 -0.230 0.034 0.300 0.374
## Age:SexMen -0.387 -0.799 -0.291 0.663 0.301 0.172
## Age:RcAfrAm -0.450 -0.841 -0.223 0.385 0.542 0.216 0.689
## Age:CES11 -0.356 -0.616 -0.139 0.295 0.326 0.638 0.500 0.543
## IPVstt1:RAA 0.074 -0.177 -0.769 0.187 -0.212 -0.028 0.221 0.223 0.086
## SxMn:RcAfrA 0.548 0.374 0.176 -0.779 -0.729 -0.281 -0.511 -0.467 -0.277
## SexMn:CES11 0.295 0.198 0.104 -0.463 -0.253 -0.689 -0.302 -0.188 -0.499
## RcAfA:CES11 0.390 0.201 -0.028 -0.268 -0.503 -0.893 -0.152 -0.263 -0.581
## Ag:SxMn:RAA 0.339 0.660 0.252 -0.530 -0.443 -0.164 -0.807 -0.780 -0.433
## Ag:SM:CES11 0.212 0.408 0.147 -0.360 -0.195 -0.433 -0.523 -0.367 -0.660
## A:RAA:CES11 0.311 0.535 0.129 -0.263 -0.381 -0.566 -0.439 -0.639 -0.881
## IPV1:RW:CES 0.060 -0.164 -0.639 0.146 -0.020 -0.490 0.196 0.156 0.026
## IPV1:RAA:CE -0.008 0.002 0.021 -0.037 0.186 -0.004 -0.005 -0.073 0.021
## SM:RAA:CES1 -0.264 -0.177 -0.097 0.406 0.357 0.625 0.261 0.229 0.457
## A:SM:RAA:CE -0.180 -0.351 -0.139 0.298 0.245 0.376 0.447 0.425 0.577
## IPVs1:RAA SxM:RAA SM:CES RAA:CE Ag:SM:RAA A:SM:C A:RAA:
## Age
## IPVstatus1
## SexMen
## RaceAfrAm
## CES11
## Age:SexMen
## Age:RcAfrAm
## Age:CES11
## IPVstt1:RAA
## SxMn:RcAfrA -0.042
## SexMn:CES11 -0.091 0.381
## RcAfA:CES11 0.099 0.373 0.627
## Ag:SxMn:RAA -0.153 0.634 0.250 0.219
## Ag:SM:CES11 -0.111 0.299 0.794 0.403 0.432
## A:RAA:CES11 -0.133 0.320 0.452 0.640 0.511 0.589
## IPV1:RW:CES 0.461 -0.122 0.194 0.411 -0.177 -0.044 -0.032
## IPV1:RAA:CE -0.529 -0.074 0.026 -0.178 -0.042 0.009 0.005
## SM:RAA:CES1 0.042 -0.514 -0.897 -0.703 -0.318 -0.714 -0.504
## A:SM:RAA:CE 0.100 -0.359 -0.682 -0.438 -0.561 -0.862 -0.666
## IPV1:RW IPV1:RAA: SM:RAA:
## Age
## IPVstatus1
## SexMen
## RaceAfrAm
## CES11
## Age:SexMen
## Age:RcAfrAm
## Age:CES11
## IPVstt1:RAA
## SxMn:RcAfrA
## SexMn:CES11
## RcAfA:CES11
## Ag:SxMn:RAA
## Ag:SM:CES11
## A:RAA:CES11
## IPV1:RW:CES
## IPV1:RAA:CE 0.030
## SM:RAA:CES1 -0.168 0.007
## A:SM:RAA:CE 0.051 0.010 0.746
plot(st)
plot(mm2)
