Trails A Regression Model 2 (with CES)
load("~/Desktop/Megan/Research/IPV and Cognition Paper/IPV R Output/IPVandCognitionDataSet2.rda")
library(lme4)
## Loading required package: Matrix
library(lmerTest)
## 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
(mm2 = lmer(TrailsAtestSec ~ (Age + IPVstatus + Sex + Race + w1CES)^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: TrailsAtestSec ~ (Age + IPVstatus + Sex + Race + w1CES)^5 + (Age | HNDid) + (1 | subclass)
## Data: IPVandCognitionDataSet2
## AIC BIC logLik deviance
## 984.2 1089.2 -455.1 910.2
## Random effects:
## Groups Name Std.Dev. Corr
## HNDid (Intercept) 7.212
## Age 0.139 1.00
## subclass (Intercept) 3.885
## Residual 6.648
## Number of obs: 126, groups: HNDid, 63; subclass, 21
## Fixed Effects:
## (Intercept)
## 21.6283
## Age
## -0.4689
## IPVstatus1
## 25.3160
## SexMen
## 18.5360
## RaceAfrAm
## 12.3838
## w1CES
## 0.8157
## Age:IPVstatus1
## 2.6662
## Age:SexMen
## 2.1147
## Age:RaceAfrAm
## 0.5083
## Age:w1CES
## 0.0748
## IPVstatus1:SexMen
## -17.7044
## IPVstatus1:RaceAfrAm
## -1.6308
## IPVstatus1:w1CES
## -1.8357
## SexMen:RaceAfrAm
## -16.6838
## SexMen:w1CES
## -1.5506
## RaceAfrAm:w1CES
## -0.5726
## Age:IPVstatus1:SexMen
## -3.7964
## Age:IPVstatus1:RaceAfrAm
## 0.0250
## Age:IPVstatus1:w1CES
## -0.1873
## Age:SexMen:RaceAfrAm
## -2.2664
## Age:SexMen:w1CES
## -0.1651
## Age:RaceAfrAm:w1CES
## -0.0408
## IPVstatus1:SexMen:RaceAfrAm
## 20.8611
## IPVstatus1:SexMen:w1CES
## 2.1978
## IPVstatus1:RaceAfrAm:w1CES
## 1.1868
## SexMen:RaceAfrAm:w1CES
## 1.6628
## Age:IPVstatus1:SexMen:RaceAfrAm
## 0.6502
## Age:IPVstatus1:SexMen:w1CES
## 0.3061
## Age:IPVstatus1:RaceAfrAm:w1CES
## 0.1182
## Age:SexMen:RaceAfrAm:w1CES
## 0.2271
## IPVstatus1:SexMen:RaceAfrAm:w1CES
## -2.6166
## Age:IPVstatus1:SexMen:RaceAfrAm:w1CES
## -0.2150
(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
## 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 effects:
## Chi.sq Chi.DF elim.num p.value
## (1 | subclass) 0.70 1 1 0.4015
## (1 | HNDid) 24.42 1 kept 0
##
## Fixed effects:
## Sum Sq Mean Sq NumDF DenDF F.value
## Age:IPVstatus:Sex:Race:w1CES 10.7003 10.7003 1 58.81 0.2133
## IPVstatus:Sex:Race:w1CES 0.1914 0.1914 1 38.69 0.0040
## Age:IPVstatus:Sex:w1CES 53.9718 53.9718 1 83.64 0.2707
## Age:IPVstatus:Sex:Race 0.9329 0.9329 1 96.94 0.2056
## IPVstatus:Sex:Race 1.7437 1.7437 1 38.56 0.0527
## Age:IPVstatus:Race:w1CES 5.7614 5.7614 1 98.44 0.6798
## IPVstatus:Race:w1CES 3.6523 3.6523 1 46.78 0.0051
## IPVstatus:Sex:w1CES 57.7257 57.7257 1 44.89 1.2791
## Age:IPVstatus:Sex 12.0212 12.0212 1 101.96 2.5214
## IPVstatus:Sex 50.8982 50.8982 1 44.61 1.1785
## Age:IPVstatus:Race 116.3128 116.3128 1 102.78 3.1307
## IPVstatus:Race 1.8013 1.8013 1 46.02 0.0818
## Age 1205.2279 1205.2279 1 105.83 7.6064
## IPVstatus 28.1889 28.1889 1 81.41 3.7944
## Sex 4.0537 4.0537 1 78.27 1.1666
## Race 175.4842 175.4842 1 75.90 0.0673
## w1CES 1.0369 1.0369 1 84.58 0.8123
## Age:IPVstatus 127.9319 127.9319 1 105.01 4.8366
## Age:Sex 5.8498 5.8498 1 105.41 0.9383
## Age:Race 30.5380 30.5380 1 104.84 2.0633
## Age:w1CES 0.0098 0.0098 1 105.23 0.4171
## IPVstatus:w1CES 1.0402 1.0402 1 83.45 2.5074
## Sex:Race 0.2560 0.2560 1 80.10 1.2122
## Sex:w1CES 5.9037 5.9037 1 83.77 1.4535
## Race:w1CES 1.8604 1.8604 1 82.65 3.5868
## Age:IPVstatus:w1CES 282.1336 282.1336 1 105.14 4.0643
## Age:Sex:Race 0.9270 0.9270 1 105.89 1.8955
## Age:Sex:w1CES 42.8070 42.8070 1 104.78 0.9814
## Age:Race:w1CES 226.2313 226.2313 1 105.63 6.1362
## Sex:Race:w1CES 3.9559 3.9559 1 84.05 2.4766
## Age:Sex:Race:w1CES 199.0480 199.0480 1 104.47 4.0548
## elim.num Pr(>F)
## Age:IPVstatus:Sex:Race:w1CES 1 0.6459
## IPVstatus:Sex:Race:w1CES 2 0.9502
## Age:IPVstatus:Sex:w1CES 3 0.6042
## Age:IPVstatus:Sex:Race 4 0.6513
## IPVstatus:Sex:Race 5 0.8197
## Age:IPVstatus:Race:w1CES 6 0.4116
## IPVstatus:Race:w1CES 7 0.9435
## IPVstatus:Sex:w1CES 8 0.2641
## Age:IPVstatus:Sex 9 0.1154
## IPVstatus:Sex 10 0.2835
## Age:IPVstatus:Race 11 0.0798
## IPVstatus:Race 12 0.7761
## Age kept 0.0069
## IPVstatus kept 0.0549
## Sex kept 0.2834
## Race kept 0.7961
## w1CES kept 0.3700
## Age:IPVstatus kept 0.0301
## Age:Sex kept 0.3349
## Age:Race kept 0.1539
## Age:w1CES kept 0.5198
## IPVstatus:w1CES kept 0.1171
## Sex:Race kept 0.2742
## Sex:w1CES kept 0.2314
## Race:w1CES kept 0.0617
## Age:IPVstatus:w1CES kept 0.0463
## Age:Sex:Race kept 0.1715
## Age:Sex:w1CES kept 0.3241
## Age:Race:w1CES kept 0.0148
## Sex:Race:w1CES kept 0.1193
## Age:Sex:Race:w1CES kept 0.0466
##
## Least squares means:
## IPVstatus Sex Race Estimate Standard Error DF
## IPVstatus 0 1.0 NA NA 30.70 1.77 47.5
## IPVstatus 1 2.0 NA NA 33.82 2.49 46.5
## Sex Women NA 2.0 NA 32.20 2.08 47.0
## Sex Men NA 1.0 NA 32.33 2.18 47.7
## Race White NA NA 2.0 29.64 2.37 46.2
## Race AfrAm NA NA 1.0 34.88 1.90 49.2
## Sex:Race Women White NA 2.0 2.0 29.03 3.28 45.9
## Sex:Race Men White NA 1.0 2.0 30.25 3.36 46.2
## Sex:Race Women AfrAm NA 2.0 1.0 35.36 2.48 48.2
## Sex:Race Men AfrAm NA 1.0 1.0 34.40 2.70 51.1
## t-value Lower CI Upper CI p-value
## IPVstatus 0 17.35 27.1 34.3 <2e-16
## IPVstatus 1 13.60 28.8 38.8 <2e-16
## Sex Women 15.50 28.0 36.4 <2e-16
## Sex Men 14.86 28.0 36.7 <2e-16
## Race White 12.53 24.9 34.4 <2e-16
## Race AfrAm 18.32 31.1 38.7 <2e-16
## Sex:Race Women White 8.85 22.4 35.6 <2e-16
## Sex:Race Men White 9.02 23.5 37.0 <2e-16
## Sex:Race Women AfrAm 14.28 30.4 40.3 <2e-16
## Sex:Race Men AfrAm 12.72 29.0 39.8 <2e-16
##
## Differences of LSMEANS:
## Estimate Standard Error DF t-value
## IPVstatus 0-1 -3.1 2.995 46.1 -1.04
## Sex Women-Men -0.1 2.906 47.3 -0.04
## Race White-AfrAm -5.2 2.967 47.2 -1.76
## Sex:Race Women White- Men White -1.2 4.650 46.0 -0.26
## Sex:Race Women White- Women AfrAm -6.3 4.063 46.4 -1.56
## Sex:Race Women White- Men AfrAm -5.4 4.221 47.8 -1.27
## Sex:Race Men White- Women AfrAm -5.1 4.084 46.6 -1.25
## Sex:Race Men White- Men AfrAm -4.1 4.268 48.4 -0.97
## Sex:Race Women AfrAm- Men AfrAm 1.0 3.519 50.3 0.27
## Lower CI Upper CI p-value
## IPVstatus 0-1 -9.14 2.914 0.30
## Sex Women-Men -5.97 5.716 0.96
## Race White-AfrAm -11.20 0.735 0.08
## Sex:Race Women White- Men White -10.58 8.139 0.79
## Sex:Race Women White- Women AfrAm -14.50 1.853 0.13
## Sex:Race Women White- Men AfrAm -13.85 3.126 0.21
## Sex:Race Men White- Women AfrAm -13.32 3.114 0.22
## Sex:Race Men White- Men AfrAm -12.72 4.437 0.34
## Sex:Race Women AfrAm- Men AfrAm -6.11 8.029 0.79
##
## Final model:
## lme4::lmer(formula = TrailsAtestSec ~ Age + IPVstatus + Sex +
## Race + w1CES + (1 | HNDid) + Age:IPVstatus + Age:Sex + Age:Race +
## Age:w1CES + IPVstatus:w1CES + Sex:Race + Sex:w1CES + Race:w1CES +
## Age:IPVstatus:w1CES + Age:Sex:Race + Age:Sex:w1CES + Age:Race:w1CES +
## Sex:Race:w1CES + Age:Sex:Race:w1CES, data = IPVandCognitionDataSet2,
## REML = reml, contrasts = l)
Re-run suggested Trails A final Model 2
(mm2 = lmer(TrailsAtestSec ~ Age + IPVstatus + Sex + Race + w1CES + (Age | HNDid) +
(1 | subclass) + Age:IPVstatus + Age:Sex + Age:Race + Age:w1CES + IPVstatus:w1CES +
Sex:Race + Sex:w1CES + Race:w1CES + Age:IPVstatus:w1CES + Age:Sex:Race +
Age:Sex:w1CES + Age:Race:w1CES + Sex:Race:w1CES + Age:Sex:Race:w1CES, 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: TrailsAtestSec ~ Age + IPVstatus + Sex + Race + w1CES + (Age | HNDid) + (1 | subclass) + Age:IPVstatus + Age:Sex + Age:Race + Age:w1CES + IPVstatus:w1CES + Sex:Race + Sex:w1CES + Race:w1CES + Age:IPVstatus:w1CES + Age:Sex:Race + Age:Sex:w1CES + Age:Race:w1CES + Sex:Race:w1CES + Age:Sex:Race:w1CES
## Data: IPVandCognitionDataSet2
## AIC BIC logLik deviance
## 973.1 1044.0 -461.6 923.1
## Random effects:
## Groups Name Std.Dev. Corr
## HNDid (Intercept) 8.035975
## Age 0.066129 1.00
## subclass (Intercept) 0.000559
## Residual 6.976433
## Number of obs: 126, groups: HNDid, 63; subclass, 21
## Fixed Effects:
## (Intercept) Age
## 24.8667 -0.1279
## IPVstatus1 SexMen
## 16.4604 17.0801
## RaceAfrAm w1CES
## 7.1867 0.4169
## Age:IPVstatus1 Age:SexMen
## 1.5634 1.6398
## Age:RaceAfrAm Age:w1CES
## 0.0420 0.0426
## IPVstatus1:w1CES SexMen:RaceAfrAm
## -0.7097 -17.3121
## SexMen:w1CES RaceAfrAm:w1CES
## -1.2214 0.0825
## Age:IPVstatus1:w1CES Age:SexMen:RaceAfrAm
## -0.0793 -1.9371
## Age:SexMen:w1CES Age:RaceAfrAm:w1CES
## -0.1327 0.0169
## SexMen:RaceAfrAm:w1CES Age:SexMen:RaceAfrAm:w1CES
## 1.3315 0.1726
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
## [1] "Asymptotic covariance matrix A is not positive!"
## Linear mixed model fit by maximum likelihood ['merModLmerTest']
## Formula: TrailsAtestSec ~ Age + IPVstatus + Sex + Race + w1CES + (Age | HNDid) + (1 | subclass) + Age:IPVstatus + Age:Sex + Age:Race + Age:w1CES + IPVstatus:w1CES + Sex:Race + Sex:w1CES + Race:w1CES + Age:IPVstatus:w1CES + Age:Sex:Race + Age:Sex:w1CES + Age:Race:w1CES + Sex:Race:w1CES + Age:Sex:Race:w1CES
## Data: IPVandCognitionDataSet2
##
## AIC BIC logLik deviance
## 973.1 1044.0 -461.6 923.1
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## HNDid (Intercept) 6.46e+01 8.035975
## Age 4.37e-03 0.066129 1.00
## subclass (Intercept) 3.12e-07 0.000559
## Residual 4.87e+01 6.976433
## Number of obs: 126, groups: HNDid, 63; subclass, 21
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 24.8667 9.0897 19.6000 2.74 0.013
## Age -0.1279 0.6786 42.8000 -0.19 0.851
## IPVstatus1 16.4604 8.3924 28.3000 1.96 0.060
## SexMen 17.0801 10.6771 19.8000 1.60 0.125
## RaceAfrAm 7.1867 10.3802 28.4000 0.69 0.494
## w1CES 0.4169 0.4710 34.5000 0.89 0.382
## Age:IPVstatus1 1.5634 0.7341 74.4000 2.13 0.037
## Age:SexMen 1.6398 0.8914 53.5000 1.84 0.071
## Age:RaceAfrAm 0.0420 0.8600 72.2000 0.05 0.961
## Age:w1CES 0.0426 0.0368 80.7000 1.16 0.251
## IPVstatus1:w1CES -0.7097 0.4485 28.2000 -1.58 0.125
## SexMen:RaceAfrAm -17.3121 13.6093 29.8000 -1.27 0.213
## SexMen:w1CES -1.2214 0.6231 33.5000 -1.96 0.058
## RaceAfrAm:w1CES 0.0825 0.5361 40.8000 0.15 0.878
## Age:IPVstatus1:w1CES -0.0793 0.0406 72.0000 -1.95 0.055
## Age:SexMen:RaceAfrAm -1.9371 1.2468 88.8000 -1.55 0.124
## Age:SexMen:w1CES -0.1327 0.0589 107.2000 -2.25 0.026
## Age:RaceAfrAm:w1CES 0.0169 0.0467 87.0000 0.36 0.718
## SexMen:RaceAfrAm:w1CES 1.3315 0.7438 37.3000 1.79 0.082
## Age:SexMen:RaceAfrAm:w1CES 0.1726 0.0781 113.0000 2.21 0.029
##
## Correlation of Fixed Effects:
## (Intr) Age IPVst1 SexMen RcAfrA w1CES Ag:IPV1 Ag:SxM
## Age 0.784
## IPVstatus1 -0.291 -0.202
## SexMen -0.821 -0.649 0.138
## RaceAfrAm -0.754 -0.584 -0.142 0.647
## w1CES -0.887 -0.722 0.290 0.734 0.623
## Ag:IPVstts1 -0.148 -0.236 0.788 0.044 -0.207 0.163
## Age:SexMen -0.584 -0.737 0.062 0.694 0.459 0.539 0.055
## Age:RcAfrAm -0.532 -0.665 -0.211 0.466 0.793 0.447 -0.255 0.526
## Age:w1CES -0.706 -0.865 0.209 0.587 0.479 0.829 0.251 0.642
## IPVst1:1CES 0.277 0.209 -0.891 -0.163 0.218 -0.344 -0.713 -0.096
## SxMn:RcAfrA 0.556 0.434 0.168 -0.728 -0.772 -0.456 0.198 -0.501
## SexMn:w1CES 0.661 0.541 -0.189 -0.855 -0.473 -0.748 -0.104 -0.642
## RcAfrA:1CES 0.619 0.495 0.255 -0.541 -0.875 -0.657 0.295 -0.397
## A:IPV1:1CES 0.143 0.243 -0.676 -0.068 0.275 -0.196 -0.868 -0.109
## Ag:SxMn:RAA 0.358 0.446 0.196 -0.460 -0.558 -0.293 0.242 -0.677
## Ag:SxM:1CES 0.439 0.535 -0.113 -0.567 -0.303 -0.517 -0.129 -0.846
## Ag:RAA:1CES 0.442 0.526 0.292 -0.395 -0.689 -0.486 0.329 -0.427
## SM:RAA:1CES -0.445 -0.360 -0.202 0.651 0.613 0.486 -0.224 0.492
## A:SM:RAA:1C -0.264 -0.318 -0.202 0.392 0.394 0.295 -0.234 0.609
## Ag:RAA A:1CES IPV1:1 SxM:RAA SM:1CE RAA:1C A:IPV1: Ag:SM:RAA
## Age
## IPVstatus1
## SexMen
## RaceAfrAm
## w1CES
## Ag:IPVstts1
## Age:SexMen
## Age:RcAfrAm
## Age:w1CES 0.515
## IPVst1:1CES 0.276 -0.267
## SxMn:RcAfrA -0.618 -0.351 -0.223
## SexMn:w1CES -0.342 -0.622 0.236 0.587
## RcAfrA:1CES -0.710 -0.530 -0.348 0.684 0.501
## A:IPV1:1CES 0.314 -0.329 0.778 -0.252 0.134 -0.383
## Ag:SxMn:RAA -0.703 -0.347 -0.241 0.727 0.394 0.505 -0.254
## Ag:SxM:1CES -0.327 -0.619 0.155 0.379 0.724 0.336 0.185 0.540
## Ag:RAA:1CES -0.873 -0.567 -0.368 0.544 0.373 0.789 -0.390 0.621
## SM:RAA:1CES 0.496 0.397 0.223 -0.853 -0.741 -0.698 0.247 -0.647
## A:SM:RAA:1C 0.505 0.365 0.208 -0.565 -0.481 -0.447 0.186 -0.852
## A:SM:1 A:RAA: SM:RAA:
## Age
## IPVstatus1
## SexMen
## RaceAfrAm
## w1CES
## Ag:IPVstts1
## Age:SexMen
## Age:RcAfrAm
## Age:w1CES
## IPVst1:1CES
## SxMn:RcAfrA
## SexMn:w1CES
## RcAfrA:1CES
## A:IPV1:1CES
## Ag:SxMn:RAA
## Ag:SxM:1CES
## Ag:RAA:1CES 0.360
## SM:RAA:1CES -0.534 -0.550
## A:SM:RAA:1C -0.698 -0.581 0.688
plot(st)
plot(mm2)
Trails B Regression Model 2 (with CES)
load("~/Desktop/Megan/Research/IPV and Cognition Paper/IPV R Output/IPVandCognitionDataSet2.rda")
library(lme4)
library(lmerTest)
(mm2 = lmer(TrailsBtestSec ~ (Age + IPVstatus + Sex + Race + w1CES)^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: TrailsBtestSec ~ (Age + IPVstatus + Sex + Race + w1CES)^5 + (Age | HNDid) + (1 | subclass)
## Data: IPVandCognitionDataSet2
## AIC BIC logLik deviance
## 1590.6 1695.6 -758.3 1516.6
## Random effects:
## Groups Name Std.Dev. Corr
## HNDid (Intercept) 1.18e+02
## Age 4.13e+00 1.00
## subclass (Intercept) 7.20e-04
## Residual 6.99e+01
## Number of obs: 126, groups: HNDid, 63; subclass, 21
## Fixed Effects:
## (Intercept)
## 9.31e+01
## Age
## 2.15e+00
## IPVstatus1
## 2.98e+01
## SexMen
## 1.91e+01
## RaceAfrAm
## -3.11e+01
## w1CES
## -8.29e-01
## Age:IPVstatus1
## 3.33e+00
## Age:SexMen
## 6.34e+00
## Age:RaceAfrAm
## -3.18e+00
## Age:w1CES
## -4.99e-02
## IPVstatus1:SexMen
## 7.39e+02
## IPVstatus1:RaceAfrAm
## -1.46e+00
## IPVstatus1:w1CES
## -1.89e+00
## SexMen:RaceAfrAm
## -1.11e+01
## SexMen:w1CES
## 3.90e-02
## RaceAfrAm:w1CES
## 1.19e+01
## Age:IPVstatus1:SexMen
## 2.94e+01
## Age:IPVstatus1:RaceAfrAm
## -5.49e-01
## Age:IPVstatus1:w1CES
## -1.99e-01
## Age:SexMen:RaceAfrAm
## -1.09e+01
## Age:SexMen:w1CES
## -3.11e-01
## Age:RaceAfrAm:w1CES
## 5.04e-01
## IPVstatus1:SexMen:RaceAfrAm
## -1.86e+03
## IPVstatus1:SexMen:w1CES
## -4.16e+01
## IPVstatus1:RaceAfrAm:w1CES
## -9.40e+00
## SexMen:RaceAfrAm:w1CES
## 1.06e+00
## Age:IPVstatus1:SexMen:RaceAfrAm
## -1.20e+02
## Age:IPVstatus1:SexMen:w1CES
## -1.45e+00
## Age:IPVstatus1:RaceAfrAm:w1CES
## -2.09e-01
## Age:SexMen:RaceAfrAm:w1CES
## 1.36e+00
## IPVstatus1:SexMen:RaceAfrAm:w1CES
## 1.06e+02
## Age:IPVstatus1:SexMen:RaceAfrAm:w1CES
## 4.97e+00
(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
## 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
## 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
## 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
## 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
## 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
## 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
## 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
## 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
## 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
## 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
## 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
## (1 | subclass) 0.00 1 1 1.0000
## (Age | HNDid) 3.45 1 kept 0.0634
##
## Fixed effects:
## Sum Sq Mean Sq NumDF DenDF F.value
## Age:IPVstatus:Sex:Race:w1CES 1998.40 1998.40 1 49.34 0.4120
## Age:IPVstatus:Race:w1CES 1510.63 1510.63 1 55.46 0.0058
## Age:IPVstatus:Sex:w1CES 5866.58 5866.58 1 65.60 0.6973
## Age:IPVstatus:w1CES 4788.66 4788.66 1 56.77 0.2132
## Age:IPVstatus:Sex:Race 4234.38 4234.38 1 44.07 2.4167
## Age:IPVstatus:Sex 279.08 279.08 1 54.00 0.2931
## Age:Sex:Race:w1CES 6607.93 6607.93 1 59.71 1.9093
## Age:Sex:Race 1845.00 1845.00 1 47.60 0.0989
## Age:Sex:w1CES 2548.47 2548.47 1 65.23 1.8175
## Age:Sex 3864.86 3864.86 1 52.68 0.4720
## Age:IPVstatus:Race 10802.23 10802.23 1 53.15 2.4420
## Age:IPVstatus 892.52 892.52 1 58.23 0.7180
## Age:Race:w1CES 12774.17 12774.17 1 62.11 2.8388
## Age:Race 40.77 40.77 1 48.89 0.0624
## Age:w1CES 1502.17 1502.17 1 66.78 0.6328
## IPVstatus:Sex:Race:w1CES 25684.26 25684.26 1 41.35 3.4026
## Sex:Race:w1CES 1760.48 1760.48 1 43.73 0.0056
## IPVstatus:Sex:w1CES 3741.90 3741.90 1 41.85 0.1719
## Sex:w1CES 1188.03 1188.03 1 45.98 0.0323
## IPVstatus:Race:w1CES 12.59 12.59 1 47.33 0.1995
## IPVstatus:w1CES 1350.12 1350.12 1 49.15 0.6785
## Race:w1CES 3622.05 3622.05 1 48.07 0.5723
## w1CES 10212.22 10212.22 1 49.97 0.8420
## IPVstatus:Sex:Race 7417.24 7417.24 1 39.71 1.5848
## IPVstatus:Race 1163.12 1163.12 1 47.38 0.0220
## Sex:Race 228.89 228.89 1 48.43 0.2489
## Race 23386.60 23386.60 1 49.23 3.5353
## Age 21937.82 21937.82 1 53.75 4.8981
## IPVstatus 3114.98 3114.98 1 55.75 1.8083
## Sex 6494.58 6494.58 1 55.66 4.6249
## IPVstatus:Sex 45100.05 45100.05 1 56.35 9.4992
## elim.num Pr(>F)
## Age:IPVstatus:Sex:Race:w1CES 1 0.5239
## Age:IPVstatus:Race:w1CES 2 0.9393
## Age:IPVstatus:Sex:w1CES 3 0.4067
## Age:IPVstatus:w1CES 4 0.6460
## Age:IPVstatus:Sex:Race 5 0.1272
## Age:IPVstatus:Sex 6 0.5904
## Age:Sex:Race:w1CES 7 0.1722
## Age:Sex:Race 8 0.7545
## Age:Sex:w1CES 9 0.1823
## Age:Sex 10 0.4951
## Age:IPVstatus:Race 11 0.1241
## Age:IPVstatus 12 0.4003
## Age:Race:w1CES 13 0.0970
## Age:Race 14 0.8037
## Age:w1CES 15 0.4291
## IPVstatus:Sex:Race:w1CES 16 0.0723
## Sex:Race:w1CES 17 0.9409
## IPVstatus:Sex:w1CES 18 0.6805
## Sex:w1CES 19 0.8581
## IPVstatus:Race:w1CES 20 0.6572
## IPVstatus:w1CES 21 0.4141
## Race:w1CES 22 0.4531
## w1CES 23 0.3632
## IPVstatus:Sex:Race 24 0.2154
## IPVstatus:Race 25 0.8827
## Sex:Race 26 0.6201
## Race 27 0.0660
## Age kept 0.0312
## IPVstatus kept 0.1842
## Sex kept 0.0359
## IPVstatus:Sex kept 0.0032
##
## Least squares means:
## IPVstatus Sex Estimate Standard Error DF t-value
## IPVstatus 0 1.0 NA 123.1 21.8 60.1 5.64
## IPVstatus 1 2.0 NA 171.8 30.5 60.3 5.63
## Sex Women NA 2.0 108.5 24.8 60.6 4.37
## Sex Men NA 1.0 186.4 28.1 60.5 6.63
## IPVstatus:Sex 0 Women 1.0 2.0 140.0 29.1 60.2 4.81
## IPVstatus:Sex 1 Women 2.0 2.0 77.0 38.9 57.4 1.98
## IPVstatus:Sex 0 Men 1.0 1.0 106.1 30.7 59.4 3.46
## IPVstatus:Sex 1 Men 2.0 1.0 266.6 46.3 58.3 5.76
## Lower CI Upper CI p-value
## IPVstatus 0 79.408 167 <2e-16
## IPVstatus 1 110.776 233 <2e-16
## Sex Women 58.803 158 0.0001
## Sex Men 130.182 243 <2e-16
## IPVstatus:Sex 0 Women 81.767 198 <2e-16
## IPVstatus:Sex 1 Women -0.827 155 0.0524
## IPVstatus:Sex 0 Men 44.752 168 0.0010
## IPVstatus:Sex 1 Men 173.985 359 <2e-16
##
## Differences of LSMEANS:
## Estimate Standard Error DF t-value
## IPVstatus 0-1 -48.7 36.2 55.7 -1.34
## Sex Women-Men -77.9 36.2 55.7 -2.15
## IPVstatus:Sex 0 Women- 1 Women 63.0 47.4 54.5 1.33
## IPVstatus:Sex 0 Women- 0 Men 33.9 40.9 53.5 0.83
## IPVstatus:Sex 0 Women- 1 Men -126.6 53.5 53.1 -2.37
## IPVstatus:Sex 1 Women- 0 Men -29.2 48.8 56.1 -0.60
## IPVstatus:Sex 1 Women- 1 Men -189.6 59.8 57.0 -3.17
## IPVstatus:Sex 0 Men- 1 Men -160.5 54.9 56.8 -2.93
## Lower CI Upper CI p-value
## IPVstatus 0-1 -121.3 23.87 0.184
## Sex Women-Men -150.5 -5.33 0.036
## IPVstatus:Sex 0 Women- 1 Women -32.0 158.03 0.189
## IPVstatus:Sex 0 Women- 0 Men -48.1 115.86 0.411
## IPVstatus:Sex 0 Women- 1 Men -234.0 -19.27 0.022
## IPVstatus:Sex 1 Women- 0 Men -127.0 68.65 0.553
## IPVstatus:Sex 1 Women- 1 Men -309.5 -69.82 0.003
## IPVstatus:Sex 0 Men- 1 Men -270.3 -50.63 0.005
##
## Final model:
## lme4::lmer(formula = TrailsBtestSec ~ Age + IPVstatus + Sex +
## (Age | HNDid) + IPVstatus:Sex, data = IPVandCognitionDataSet2,
## REML = reml, contrasts = l)
Re-run the suggested final Model 2
(mm2 = lmer(TrailsBtestSec ~ Age + IPVstatus + Sex + (Age | HNDid) + (1 | subclass) +
IPVstatus:Sex, 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: TrailsBtestSec ~ Age + IPVstatus + Sex + (Age | HNDid) + (1 | subclass) + IPVstatus:Sex
## Data: IPVandCognitionDataSet2
## AIC BIC logLik deviance
## 1571.1 1599.5 -775.6 1551.1
## Random effects:
## Groups Name Std.Dev. Corr
## HNDid (Intercept) 1.71e+02
## Age 7.93e+00 0.84
## subclass (Intercept) 7.34e-05
## Residual 6.41e+01
## Number of obs: 126, groups: HNDid, 63; subclass, 21
## Fixed Effects:
## (Intercept) Age IPVstatus1
## 174.13 4.71 -62.95
## SexMen IPVstatus1:SexMen
## -33.65 225.03
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 maximum likelihood ['merModLmerTest']
## Formula: TrailsBtestSec ~ Age + IPVstatus + Sex + (Age | HNDid) + (1 | subclass) + IPVstatus:Sex
## Data: IPVandCognitionDataSet2
##
## AIC BIC logLik deviance
## 1571.1 1599.5 -775.6 1551.1
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## HNDid (Intercept) 2.92e+04 1.71e+02
## Age 6.29e+01 7.93e+00 0.84
## subclass (Intercept) 5.39e-09 7.34e-05
## Residual 4.11e+03 6.41e+01
## Number of obs: 126, groups: HNDid, 63; subclass, 21
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 174.13 36.34 74.10 4.79 8.3e-06
## Age 4.71 2.10 53.20 2.24 0.029
## IPVstatus1 -62.95 45.65 58.80 -1.38 0.173
## SexMen -33.65 39.37 57.60 -0.85 0.396
## IPVstatus1:SexMen 225.03 69.84 60.70 3.22 0.002
##
## Correlation of Fixed Effects:
## (Intr) Age IPVst1 SexMen
## Age 0.690
## IPVstatus1 -0.481 -0.093
## SexMen -0.552 -0.100 0.394
## IPVstts1:SM 0.319 0.068 -0.654 -0.565
plot(st)
plot(mm2)
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 + w1CES)^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 + w1CES)^5 + (Age | HNDid) + (1 | subclass)
## Data: IPVandCognitionDataSet2
## AIC BIC logLik deviance
## 768.8 873.7 -347.4 694.8
## Random effects:
## Groups Name Std.Dev. Corr
## HNDid (Intercept) 0.0000
## Age 0.0886 NaN
## subclass (Intercept) 0.3488
## Residual 3.7055
## Number of obs: 126, groups: HNDid, 63; subclass, 21
## Fixed Effects:
## (Intercept)
## 21.39256
## Age
## -0.11507
## IPVstatus1
## -3.05351
## SexMen
## -0.56895
## RaceAfrAm
## -7.64522
## w1CES
## -0.17010
## Age:IPVstatus1
## 0.67930
## Age:SexMen
## -1.22965
## Age:RaceAfrAm
## -0.58368
## Age:w1CES
## -0.01023
## IPVstatus1:SexMen
## -54.38546
## IPVstatus1:RaceAfrAm
## 9.48993
## IPVstatus1:w1CES
## 0.46494
## SexMen:RaceAfrAm
## 4.78263
## SexMen:w1CES
## 0.56271
## RaceAfrAm:w1CES
## 0.18199
## Age:IPVstatus1:SexMen
## -2.64774
## Age:IPVstatus1:RaceAfrAm
## 0.36136
## Age:IPVstatus1:w1CES
## 0.00175
## Age:SexMen:RaceAfrAm
## 1.27970
## Age:SexMen:w1CES
## 0.10652
## Age:RaceAfrAm:w1CES
## 0.03098
## IPVstatus1:SexMen:RaceAfrAm
## 76.40682
## IPVstatus1:SexMen:w1CES
## 4.70719
## IPVstatus1:RaceAfrAm:w1CES
## -0.49996
## SexMen:RaceAfrAm:w1CES
## -0.54141
## Age:IPVstatus1:SexMen:RaceAfrAm
## 3.53091
## Age:IPVstatus1:SexMen:w1CES
## 0.21451
## Age:IPVstatus1:RaceAfrAm:w1CES
## -0.03814
## Age:SexMen:RaceAfrAm:w1CES
## -0.09943
## IPVstatus1:SexMen:RaceAfrAm:w1CES
## -6.22180
## Age:IPVstatus1:SexMen:RaceAfrAm:w1CES
## -0.30114
(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
## 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 effects:
## Chi.sq Chi.DF elim.num p.value
## (1 | subclass) 0.00 1 1 1.0000
## (1 | HNDid) 9.39 1 kept 0.0022
##
## Fixed effects:
## Sum Sq Mean Sq NumDF DenDF F.value
## Age:IPVstatus:Sex:Race:w1CES 10.1930 10.1930 1 56.36 0.8777
## Age:IPVstatus:Sex:w1CES 9.3534 9.3534 1 88.88 0.0001
## Age:IPVstatus:Sex:Race 6.4755 6.4755 1 95.17 0.0421
## Age:IPVstatus:Sex 16.1212 16.1212 1 95.21 0.1925
## Age:IPVstatus:Race:w1CES 1.0108 1.0108 1 93.86 1.7793
## Age:IPVstatus:Race 5.3729 5.3729 1 89.19 0.0000
## Age:IPVstatus:w1CES 58.0219 58.0219 1 95.94 0.3316
## Age:IPVstatus 9.2229 9.2229 1 90.17 2.9346
## Age 126.1224 126.1224 1 93.35 7.9049
## IPVstatus 0.0059 0.0059 1 38.15 6.6201
## Sex 184.8543 184.8543 1 50.18 0.0648
## Race 172.9128 172.9128 1 50.18 9.8758
## w1CES 0.0002 0.0002 1 50.35 9.0184
## Age:Sex 6.8070 6.8070 1 93.35 2.0552
## Age:Race 12.3819 12.3819 1 93.35 3.9244
## Age:w1CES 5.5118 5.5118 1 100.84 3.6234
## IPVstatus:Sex 2.6928 2.6928 1 38.15 0.9422
## IPVstatus:Race 0.0117 0.0117 1 38.15 15.9184
## IPVstatus:w1CES 62.1940 62.1940 1 38.52 12.2991
## Sex:Race 18.8171 18.8171 1 50.18 8.8808
## Sex:w1CES 0.9409 0.9409 1 50.35 6.0840
## Race:w1CES 36.0607 36.0607 1 50.35 27.1415
## Age:Sex:Race 3.5224 3.5224 1 93.35 3.4099
## Age:Sex:w1CES 32.3304 32.3304 1 100.84 4.0940
## Age:Race:w1CES 23.5005 23.5005 1 100.84 10.1053
## IPVstatus:Sex:Race 16.7910 16.7910 1 38.15 10.5187
## IPVstatus:Sex:w1CES 0.6396 0.6396 1 38.52 3.6727
## IPVstatus:Race:w1CES 31.9109 31.9109 1 38.52 19.3066
## Sex:Race:w1CES 3.1401 3.1401 1 50.35 17.8702
## Age:Sex:Race:w1CES 65.6894 65.6894 1 100.84 6.3951
## IPVstatus:Sex:Race:w1CES 202.7757 202.7757 1 38.52 17.2418
## elim.num Pr(>F)
## Age:IPVstatus:Sex:Race:w1CES 1 0.3528
## Age:IPVstatus:Sex:w1CES 2 0.9924
## Age:IPVstatus:Sex:Race 3 0.8378
## Age:IPVstatus:Sex 4 0.6618
## Age:IPVstatus:Race:w1CES 5 0.1855
## Age:IPVstatus:Race 6 0.9987
## Age:IPVstatus:w1CES 7 0.5661
## Age:IPVstatus 8 0.0901
## Age kept 0.0060
## IPVstatus kept 0.0141
## Sex kept 0.8001
## Race kept 0.0028
## w1CES kept 0.0042
## Age:Sex kept 0.1550
## Age:Race kept 0.0505
## Age:w1CES kept 0.0598
## IPVstatus:Sex kept 0.3378
## IPVstatus:Race kept 0.0003
## IPVstatus:w1CES kept 0.0012
## Sex:Race kept 0.0044
## Sex:w1CES kept 0.0171
## Race:w1CES kept 0e+00
## Age:Sex:Race kept 0.0680
## Age:Sex:w1CES kept 0.0457
## Age:Race:w1CES kept 0.0020
## IPVstatus:Sex:Race kept 0.0025
## IPVstatus:Sex:w1CES kept 0.0628
## IPVstatus:Race:w1CES kept 1e-04
## Sex:Race:w1CES kept 1e-04
## Age:Sex:Race:w1CES kept 0.0130
## IPVstatus:Sex:Race:w1CES kept 0.0002
##
## Least squares means:
## IPVstatus Sex Race Estimate
## IPVstatus 0 1.0 NA NA 21.199
## IPVstatus 1 2.0 NA NA 24.242
## Sex Women NA 2.0 NA 18.830
## Sex Men NA 1.0 NA 26.612
## Race White NA NA 2.0 25.360
## Race AfrAm NA NA 1.0 20.081
## IPVstatus:Sex 0 Women 1.0 2.0 NA 18.935
## IPVstatus:Sex 1 Women 2.0 2.0 NA 18.725
## IPVstatus:Sex 0 Men 1.0 1.0 NA 23.464
## IPVstatus:Sex 1 Men 2.0 1.0 NA 29.759
## IPVstatus:Race 0 White 1.0 NA 2.0 23.331
## IPVstatus:Race 1 White 2.0 NA 2.0 27.389
## IPVstatus:Race 0 AfrAm 1.0 NA 1.0 19.067
## IPVstatus:Race 1 AfrAm 2.0 NA 1.0 21.095
## Sex:Race Women White NA 2.0 2.0 19.957
## Sex:Race Men White NA 1.0 2.0 30.764
## Sex:Race Women AfrAm NA 2.0 1.0 17.703
## Sex:Race Men AfrAm NA 1.0 1.0 22.459
## IPVstatus:Sex:Race 0 Women White 1.0 2.0 2.0 20.996
## IPVstatus:Sex:Race 1 Women White 2.0 2.0 2.0 18.917
## IPVstatus:Sex:Race 0 Men White 1.0 1.0 2.0 25.667
## IPVstatus:Sex:Race 1 Men White 2.0 1.0 2.0 35.861
## IPVstatus:Sex:Race 0 Women AfrAm 1.0 2.0 1.0 16.873
## IPVstatus:Sex:Race 1 Women AfrAm 2.0 2.0 1.0 18.533
## IPVstatus:Sex:Race 0 Men AfrAm 1.0 1.0 1.0 21.261
## IPVstatus:Sex:Race 1 Men AfrAm 2.0 1.0 1.0 23.658
## Standard Error DF t-value Lower CI
## IPVstatus 0 0.735 39.8 28.86 19.7
## IPVstatus 1 1.229 38.3 19.73 21.8
## Sex Women 0.850 39.0 22.14 17.1
## Sex Men 1.159 39.2 22.96 24.3
## Race White 1.111 38.0 22.83 23.1
## Race AfrAm 0.912 40.8 22.01 18.2
## IPVstatus:Sex 0 Women 1.037 38.0 18.26 16.8
## IPVstatus:Sex 1 Women 1.338 39.1 14.00 16.0
## IPVstatus:Sex 0 Men 1.041 41.6 22.54 21.4
## IPVstatus:Sex 1 Men 2.061 38.0 14.44 25.6
## IPVstatus:Race 0 White 1.151 38.3 20.27 21.0
## IPVstatus:Race 1 White 1.899 37.9 14.42 23.5
## IPVstatus:Race 0 AfrAm 0.913 42.2 20.87 17.2
## IPVstatus:Race 1 AfrAm 1.559 39.0 13.53 17.9
## Sex:Race Women White 1.337 38.2 14.92 17.2
## Sex:Race Men White 1.774 37.9 17.34 27.2
## Sex:Race Women AfrAm 1.051 40.4 16.85 15.6
## Sex:Race Men AfrAm 1.492 41.1 15.05 19.4
## IPVstatus:Sex:Race 0 Women White 1.710 38.0 12.28 17.5
## IPVstatus:Sex:Race 1 Women White 2.039 38.0 9.28 14.8
## IPVstatus:Sex:Race 0 Men White 1.540 38.8 16.66 22.6
## IPVstatus:Sex:Race 1 Men White 3.204 37.9 11.19 29.4
## IPVstatus:Sex:Race 0 Women AfrAm 1.174 38.0 14.38 14.5
## IPVstatus:Sex:Race 1 Women AfrAm 1.732 40.9 10.70 15.0
## IPVstatus:Sex:Race 0 Men AfrAm 1.400 45.5 15.19 18.4
## IPVstatus:Sex:Race 1 Men AfrAm 2.593 38.2 9.12 18.4
## Upper CI p-value
## IPVstatus 0 22.7 <2e-16
## IPVstatus 1 26.7 <2e-16
## Sex Women 20.5 <2e-16
## Sex Men 29.0 <2e-16
## Race White 27.6 <2e-16
## Race AfrAm 21.9 <2e-16
## IPVstatus:Sex 0 Women 21.0 <2e-16
## IPVstatus:Sex 1 Women 21.4 <2e-16
## IPVstatus:Sex 0 Men 25.6 <2e-16
## IPVstatus:Sex 1 Men 33.9 <2e-16
## IPVstatus:Race 0 White 25.7 <2e-16
## IPVstatus:Race 1 White 31.2 <2e-16
## IPVstatus:Race 0 AfrAm 20.9 <2e-16
## IPVstatus:Race 1 AfrAm 24.2 <2e-16
## Sex:Race Women White 22.7 <2e-16
## Sex:Race Men White 34.4 <2e-16
## Sex:Race Women AfrAm 19.8 <2e-16
## Sex:Race Men AfrAm 25.5 <2e-16
## IPVstatus:Sex:Race 0 Women White 24.5 <2e-16
## IPVstatus:Sex:Race 1 Women White 23.0 <2e-16
## IPVstatus:Sex:Race 0 Men White 28.8 <2e-16
## IPVstatus:Sex:Race 1 Men White 42.3 <2e-16
## IPVstatus:Sex:Race 0 Women AfrAm 19.2 <2e-16
## IPVstatus:Sex:Race 1 Women AfrAm 22.0 <2e-16
## IPVstatus:Sex:Race 0 Men AfrAm 24.1 <2e-16
## IPVstatus:Sex:Race 1 Men AfrAm 28.9 <2e-16
##
## Differences of LSMEANS:
## Estimate Standard Error DF t-value
## IPVstatus 0-1 -3.0 1.43 38.3 -2.13
## Sex Women-Men -7.8 1.44 39.1 -5.41
## Race White-AfrAm 5.3 1.44 39.1 3.67
## IPVstatus:Sex 0 Women- 1 Women 0.2 1.69 38.4 0.12
## IPVstatus:Sex 0 Women- 0 Men -4.5 1.47 39.8 -3.08
## IPVstatus:Sex 0 Women- 1 Men -10.8 2.31 38.0 -4.69
## IPVstatus:Sex 1 Women- 0 Men -4.7 1.70 40.1 -2.80
## IPVstatus:Sex 1 Women- 1 Men -11.0 2.46 38.3 -4.49
## IPVstatus:Sex 0 Men- 1 Men -6.3 2.30 38.3 -2.74
## IPVstatus:Race 0 White- 1 White -4.1 2.22 38.0 -1.83
## IPVstatus:Race 0 White- 0 AfrAm 4.3 1.47 39.8 2.90
## IPVstatus:Race 0 White- 1 AfrAm 2.2 1.94 38.8 1.15
## IPVstatus:Race 1 White- 0 AfrAm 8.3 2.11 38.7 3.95
## IPVstatus:Race 1 White- 1 AfrAm 6.3 2.46 38.3 2.56
## IPVstatus:Race 0 AfrAm- 1 AfrAm -2.0 1.79 38.7 -1.13
## Sex:Race Women White- Men White -10.8 2.22 38.0 -4.86
## Sex:Race Women White- Women AfrAm 2.3 1.70 39.0 1.33
## Sex:Race Women White- Men AfrAm -2.5 2.00 39.7 -1.25
## Sex:Race Men White- Women AfrAm 13.1 2.06 38.5 6.33
## Sex:Race Men White- Men AfrAm 8.3 2.32 39.2 3.58
## Sex:Race Women AfrAm- Men AfrAm -4.8 1.82 40.8 -2.61
## Lower CI Upper CI p-value
## IPVstatus 0-1 -5.93 -0.158 0.039
## Sex Women-Men -10.69 -4.874 <2e-16
## Race White-AfrAm 2.37 8.187 7e-04
## IPVstatus:Sex 0 Women- 1 Women -3.20 3.619 0.902
## IPVstatus:Sex 0 Women- 0 Men -7.50 -1.559 0.004
## IPVstatus:Sex 0 Women- 1 Men -15.50 -6.154 <2e-16
## IPVstatus:Sex 1 Women- 0 Men -8.16 -1.314 0.008
## IPVstatus:Sex 1 Women- 1 Men -16.01 -6.061 1e-04
## IPVstatus:Sex 0 Men- 1 Men -10.95 -1.641 0.009
## IPVstatus:Race 0 White- 1 White -8.55 0.436 0.075
## IPVstatus:Race 0 White- 0 AfrAm 1.29 7.234 0.006
## IPVstatus:Race 0 White- 1 AfrAm -1.68 6.157 0.256
## IPVstatus:Race 1 White- 0 AfrAm 4.06 12.586 3e-04
## IPVstatus:Race 1 White- 1 AfrAm 1.32 11.267 0.015
## IPVstatus:Race 0 AfrAm- 1 AfrAm -5.65 1.591 0.264
## Sex:Race Women White- Men White -15.30 -6.310 <2e-16
## Sex:Race Women White- Women AfrAm -1.19 5.694 0.193
## Sex:Race Women White- Men AfrAm -6.55 1.548 0.219
## Sex:Race Men White- Women AfrAm 8.89 17.233 <2e-16
## Sex:Race Men White- Men AfrAm 3.62 12.993 9e-04
## Sex:Race Women AfrAm- Men AfrAm -8.44 -1.070 0.013
##
## Final model:
## lme4::lmer(formula = FluencyWord ~ Age + IPVstatus + Sex + Race +
## w1CES + (1 | HNDid) + Age:Sex + Age:Race + Age:w1CES + IPVstatus:Sex +
## IPVstatus:Race + IPVstatus:w1CES + Sex:Race + Sex:w1CES +
## Race:w1CES + Age:Sex:Race + Age:Sex:w1CES + Age:Race:w1CES +
## IPVstatus:Sex:Race + IPVstatus:Sex:w1CES + IPVstatus:Race:w1CES +
## Sex:Race:w1CES + Age:Sex:Race:w1CES + IPVstatus:Sex:Race:w1CES,
## data = IPVandCognitionDataSet2, REML = reml, contrasts = l)
Re-run suggested final Model 2
(mm2 = lmer(FluencyWord ~ Age + IPVstatus + Sex + Race + w1CES + (Age | HNDid) +
(1 | subclass) + Age:Sex + Age:Race + Age:w1CES + IPVstatus:Sex + IPVstatus:Race +
IPVstatus:w1CES + Sex:Race + Sex:w1CES + Race:w1CES + Age:Sex:Race + Age:Sex:w1CES +
Age:Race:w1CES + IPVstatus:Sex:Race + IPVstatus:Sex:w1CES + IPVstatus:Race:w1CES +
Sex:Race:w1CES + Age:Sex:Race:w1CES + IPVstatus:Sex:Race:w1CES, 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 + Race + w1CES + (Age | HNDid) + (1 | subclass) + Age:Sex + Age:Race + Age:w1CES + IPVstatus:Sex + IPVstatus:Race + IPVstatus:w1CES + Sex:Race + Sex:w1CES + Race:w1CES + Age:Sex:Race + Age:Sex:w1CES + Age:Race:w1CES + IPVstatus:Sex:Race + IPVstatus:Sex:w1CES + IPVstatus:Race:w1CES + Sex:Race:w1CES + Age:Sex:Race:w1CES + IPVstatus:Sex:Race:w1CES
## Data: IPVandCognitionDataSet2
## REML criterion at convergence: 731.7
## Random effects:
## Groups Name Std.Dev. Corr
## HNDid (Intercept) 3.34e+00
## Age 1.84e-02 -1.00
## subclass (Intercept) 7.29e-06
## Residual 3.31e+00
## Number of obs: 126, groups: HNDid, 63; subclass, 21
## Fixed Effects:
## (Intercept) Age
## 20.27515 -0.19581
## IPVstatus1 SexMen
## -8.46098 1.20011
## RaceAfrAm w1CES
## -2.25879 -0.02025
## Age:SexMen Age:RaceAfrAm
## -0.96200 0.01637
## Age:w1CES IPVstatus1:SexMen
## 0.00408 -30.67290
## IPVstatus1:RaceAfrAm IPVstatus1:w1CES
## 5.50026 0.43046
## SexMen:RaceAfrAm SexMen:w1CES
## 2.19236 0.37659
## RaceAfrAm:w1CES Age:SexMen:RaceAfrAm
## -0.19277 1.07521
## Age:SexMen:w1CES Age:RaceAfrAm:w1CES
## 0.08352 -0.01086
## IPVstatus1:SexMen:RaceAfrAm IPVstatus1:SexMen:w1CES
## 47.33755 2.83205
## IPVstatus1:RaceAfrAm:w1CES SexMen:RaceAfrAm:w1CES
## -0.12017 -0.32067
## Age:SexMen:RaceAfrAm:w1CES IPVstatus1:SexMen:RaceAfrAm:w1CES
## -0.09256 -3.88586
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 + IPVstatus + Sex + Race + w1CES + (Age | HNDid) + (1 | subclass) + Age:Sex + Age:Race + Age:w1CES + IPVstatus:Sex + IPVstatus:Race + IPVstatus:w1CES + Sex:Race + Sex:w1CES + Race:w1CES + Age:Sex:Race + Age:Sex:w1CES + Age:Race:w1CES + IPVstatus:Sex:Race + IPVstatus:Sex:w1CES + IPVstatus:Race:w1CES + Sex:Race:w1CES + Age:Sex:Race:w1CES + IPVstatus:Sex:Race:w1CES
## Data: IPVandCognitionDataSet2
##
## REML criterion at convergence: 731.7
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## HNDid (Intercept) 1.11e+01 3.34e+00
## Age 3.39e-04 1.84e-02 -1.00
## subclass (Intercept) 5.31e-11 7.29e-06
## Residual 1.10e+01 3.31e+00
## Number of obs: 126, groups: HNDid, 63; subclass, 21
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 20.27515 4.25518 8.20000 4.76
## Age -0.19581 0.29278 20.60000 -0.67
## IPVstatus1 -8.46098 4.84178 37.80000 -1.75
## SexMen 1.20011 5.04307 8.20000 0.24
## RaceAfrAm -2.25879 5.14478 11.70000 -0.44
## w1CES -0.02025 0.21928 16.90000 -0.09
## Age:SexMen -0.96200 0.39634 25.30000 -2.43
## Age:RaceAfrAm 0.01637 0.37313 31.80000 0.04
## Age:w1CES 0.00408 0.01581 47.40000 0.26
## IPVstatus1:SexMen -30.67290 10.83452 30.80000 -2.83
## IPVstatus1:RaceAfrAm 5.50026 5.98746 37.90000 0.92
## IPVstatus1:w1CES 0.43046 0.21308 37.20000 2.02
## SexMen:RaceAfrAm 2.19236 6.45900 12.40000 0.34
## SexMen:w1CES 0.37659 0.28535 15.00000 1.32
## RaceAfrAm:w1CES -0.19277 0.28595 21.00000 -0.67
## Age:SexMen:RaceAfrAm 1.07521 0.55632 52.40000 1.93
## Age:SexMen:w1CES 0.08352 0.02678 72.30000 3.12
## Age:RaceAfrAm:w1CES -0.01086 0.02027 53.10000 -0.54
## IPVstatus1:SexMen:RaceAfrAm 47.33755 14.51562 34.20000 3.26
## IPVstatus1:SexMen:w1CES 2.83205 0.78212 34.20000 3.62
## IPVstatus1:RaceAfrAm:w1CES -0.12017 0.29658 36.60000 -0.41
## SexMen:RaceAfrAm:w1CES -0.32067 0.37123 18.00000 -0.86
## Age:SexMen:RaceAfrAm:w1CES -0.09256 0.03580 88.00000 -2.59
## IPVstatus1:SexMen:RaceAfrAm:w1CES -3.88586 0.93090 34.50000 -4.17
## Pr(>|t|)
## (Intercept) 0.00132
## Age 0.51105
## IPVstatus1 0.08867
## SexMen 0.81775
## RaceAfrAm 0.66865
## w1CES 0.92751
## Age:SexMen 0.02266
## Age:RaceAfrAm 0.96528
## Age:w1CES 0.79739
## IPVstatus1:SexMen 0.00811
## IPVstatus1:RaceAfrAm 0.36410
## IPVstatus1:w1CES 0.05061
## SexMen:RaceAfrAm 0.73997
## SexMen:w1CES 0.20667
## RaceAfrAm:w1CES 0.50755
## Age:SexMen:RaceAfrAm 0.05869
## Age:SexMen:w1CES 0.00261
## Age:RaceAfrAm:w1CES 0.59415
## IPVstatus1:SexMen:RaceAfrAm 0.00252
## IPVstatus1:SexMen:w1CES 0.00094
## IPVstatus1:RaceAfrAm:w1CES 0.68769
## SexMen:RaceAfrAm:w1CES 0.39905
## Age:SexMen:RaceAfrAm:w1CES 0.01136
## IPVstatus1:SexMen:RaceAfrAm:w1CES 0.00019
##
## Correlation matrix not shown by default, as p = 24 > 20.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need it
plot(st)
plot(mm2)
Clock Total Regression Model 2 (with CES)
load("~/Desktop/Megan/Research/IPV and Cognition Paper/IPV R Output/IPVandCognitionDataSet2.rda")
library(lme4)
library(lmerTest)
(mm2 = lmer(ClockTotal ~ (Age + IPVstatus + Sex + Race + w1CES)^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: ClockTotal ~ (Age + IPVstatus + Sex + Race + w1CES)^5 + (Age | HNDid) + (1 | subclass)
## Data: IPVandCognitionDataSet2
## AIC BIC logLik deviance
## 413.4 518.3 -169.7 339.4
## Random effects:
## Groups Name Std.Dev. Corr
## HNDid (Intercept) 0.1910
## Age 0.0154 1.00
## subclass (Intercept) 0.5980
## Residual 0.8173
## Number of obs: 126, groups: HNDid, 63; subclass, 21
## Fixed Effects:
## (Intercept)
## 7.014350
## Age
## -0.062561
## IPVstatus1
## -1.768974
## SexMen
## 1.127260
## RaceAfrAm
## 1.592682
## w1CES
## 0.052052
## Age:IPVstatus1
## -0.270452
## Age:SexMen
## -0.126521
## Age:RaceAfrAm
## 0.068793
## Age:w1CES
## 0.000214
## IPVstatus1:SexMen
## 0.199394
## IPVstatus1:RaceAfrAm
## 0.429321
## IPVstatus1:w1CES
## 0.111782
## SexMen:RaceAfrAm
## 0.111290
## SexMen:w1CES
## -0.007955
## RaceAfrAm:w1CES
## -0.022658
## Age:IPVstatus1:SexMen
## 0.140202
## Age:IPVstatus1:RaceAfrAm
## 0.107495
## Age:IPVstatus1:w1CES
## 0.011937
## Age:SexMen:RaceAfrAm
## 0.190969
## Age:SexMen:w1CES
## 0.012304
## Age:RaceAfrAm:w1CES
## 0.003555
## IPVstatus1:SexMen:RaceAfrAm
## 4.919451
## IPVstatus1:SexMen:w1CES
## 0.177508
## IPVstatus1:RaceAfrAm:w1CES
## -0.085683
## SexMen:RaceAfrAm:w1CES
## -0.054737
## Age:IPVstatus1:SexMen:RaceAfrAm
## 0.654864
## Age:IPVstatus1:SexMen:w1CES
## 0.013937
## Age:IPVstatus1:RaceAfrAm:w1CES
## -0.011466
## Age:SexMen:RaceAfrAm:w1CES
## -0.020933
## IPVstatus1:SexMen:RaceAfrAm:w1CES
## -0.441035
## Age:IPVstatus1:SexMen:RaceAfrAm:w1CES
## -0.049345
(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
## 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 effects:
## Chi.sq Chi.DF elim.num p.value
## (1 | HNDid) 0.00 1 1 1e+00
## (1 | subclass) 12.45 1 kept 4e-04
##
## Fixed effects:
## Sum Sq Mean Sq NumDF DenDF F.value elim.num
## Age:IPVstatus:Sex:Race:w1CES 0.4809 0.4809 1 78.17 0.5269 1
## IPVstatus:Sex:Race:w1CES 0.0205 0.0205 1 94.60 0.0231 2
## Age:IPVstatus:Sex:Race 0.3931 0.3931 1 94.08 0.2929 3
## IPVstatus:Sex:Race 0.4734 0.4734 1 95.92 0.7602 4
## Age:IPVstatus:Race:w1CES 0.1216 0.1216 1 93.30 2.1509 5
## IPVstatus:Race:w1CES 0.0035 0.0035 1 97.79 0.0540 6
## Age:IPVstatus:Race 6.1600 6.1600 1 97.36 0.1499 7
## IPVstatus:Race 1.9178 1.9178 1 95.02 0.9675 8
## Age:Sex:Race:w1CES 3.9288 3.9288 1 100.98 1.9548 9
## Age:Sex:Race 0.9747 0.9747 1 102.36 0.4804 10
## Sex:Race:w1CES 1.2271 1.2271 1 100.55 1.5596 11
## Sex:Race 0.0359 0.0359 1 104.05 0.1769 12
## Age:Race:w1CES 0.5909 0.5909 1 104.68 1.8614 13
## Race:w1CES 0.7407 0.7407 1 104.78 1.8796 14
## Age 0.0098 0.0098 1 106.41 0.6428 kept
## IPVstatus 1.5781 1.5781 1 107.98 2.6179 kept
## Sex 1.6300 1.6300 1 106.19 13.6949 kept
## Race 0.0009 0.0009 1 105.92 7.1633 kept
## w1CES 0.4379 0.4379 1 104.33 2.1000 kept
## Age:IPVstatus 0.0025 0.0025 1 107.73 3.4635 kept
## Age:Sex 2.4846 2.4846 1 105.63 8.4155 kept
## Age:Race 7.6056 7.6056 1 107.81 11.3662 kept
## Age:w1CES 0.9510 0.9510 1 105.44 2.0961 kept
## IPVstatus:Sex 2.1819 2.1819 1 105.43 6.2946 kept
## IPVstatus:w1CES 0.9138 0.9138 1 107.99 5.4549 kept
## Sex:w1CES 6.2879 6.2879 1 107.89 12.7315 kept
## Age:IPVstatus:Sex 2.7646 2.7646 1 105.19 11.3318 kept
## Age:IPVstatus:w1CES 0.0199 0.0199 1 106.06 7.8374 kept
## Age:Sex:w1CES 0.0968 0.0968 1 107.98 6.0746 kept
## IPVstatus:Sex:w1CES 0.0337 0.0337 1 107.19 6.6921 kept
## Age:IPVstatus:Sex:w1CES 4.3739 4.3739 1 105.31 8.8146 kept
## Pr(>F)
## Age:IPVstatus:Sex:Race:w1CES 0.4701
## IPVstatus:Sex:Race:w1CES 0.8794
## Age:IPVstatus:Sex:Race 0.5897
## IPVstatus:Sex:Race 0.3854
## Age:IPVstatus:Race:w1CES 0.1459
## IPVstatus:Race:w1CES 0.8167
## Age:IPVstatus:Race 0.6995
## IPVstatus:Race 0.3278
## Age:Sex:Race:w1CES 0.1651
## Age:Sex:Race 0.4898
## Sex:Race:w1CES 0.2146
## Sex:Race 0.6749
## Age:Race:w1CES 0.1754
## Race:w1CES 0.1733
## Age 0.4245
## IPVstatus 0.1086
## Sex 0.0003
## Race 0.0086
## w1CES 0.1503
## Age:IPVstatus 0.0655
## Age:Sex 0.0045
## Age:Race 0.0010
## Age:w1CES 0.1506
## IPVstatus:Sex 0.0136
## IPVstatus:w1CES 0.0214
## Sex:w1CES 0.0005
## Age:IPVstatus:Sex 0.0011
## Age:IPVstatus:w1CES 0.0061
## Age:Sex:w1CES 0.0153
## IPVstatus:Sex:w1CES 0.0110
## Age:IPVstatus:Sex:w1CES 0.0037
##
## Least squares means:
## IPVstatus Sex Race Estimate Standard Error DF
## IPVstatus 0 1.0 NA NA 8.773 0.186 22.3
## IPVstatus 1 2.0 NA NA 8.946 0.225 42.5
## Sex Women NA 2.0 NA 8.712 0.207 30.4
## Sex Men NA 1.0 NA 9.007 0.224 36.2
## Race White NA NA 2.0 8.824 0.226 36.2
## Race AfrAm NA NA 1.0 8.895 0.200 27.6
## IPVstatus:Sex 0 Women 1.0 2.0 NA 8.582 0.217 36.1
## IPVstatus:Sex 1 Women 2.0 2.0 NA 8.842 0.280 69.7
## IPVstatus:Sex 0 Men 1.0 1.0 NA 8.964 0.234 44.0
## IPVstatus:Sex 1 Men 2.0 1.0 NA 9.050 0.309 78.0
## t-value Lower CI Upper CI p-value
## IPVstatus 0 47.3 8.39 9.16 <2e-16
## IPVstatus 1 39.8 8.49 9.40 <2e-16
## Sex Women 42.0 8.29 9.13 <2e-16
## Sex Men 40.1 8.55 9.46 <2e-16
## Race White 39.0 8.36 9.28 <2e-16
## Race AfrAm 44.5 8.49 9.30 <2e-16
## IPVstatus:Sex 0 Women 39.5 8.14 9.02 <2e-16
## IPVstatus:Sex 1 Women 31.5 8.28 9.40 <2e-16
## IPVstatus:Sex 0 Men 38.3 8.49 9.43 <2e-16
## IPVstatus:Sex 1 Men 29.3 8.43 9.67 <2e-16
##
## Differences of LSMEANS:
## Estimate Standard Error DF t-value
## IPVstatus 0-1 -0.2 0.2044 93.2 -0.85
## Sex Women-Men -0.3 0.2416 107.8 -1.22
## Race White-AfrAm -0.1 0.2331 107.3 -0.30
## IPVstatus:Sex 0 Women- 1 Women -0.3 0.2828 98.5 -0.92
## IPVstatus:Sex 0 Women- 0 Men -0.4 0.2567 107.4 -1.49
## IPVstatus:Sex 0 Women- 1 Men -0.5 0.3229 106.0 -1.45
## IPVstatus:Sex 1 Women- 0 Men -0.1 0.3098 105.5 -0.39
## IPVstatus:Sex 1 Women- 1 Men -0.2 0.3824 106.8 -0.54
## IPVstatus:Sex 0 Men- 1 Men -0.1 0.3146 97.2 -0.28
## Lower CI Upper CI p-value
## IPVstatus 0-1 -0.580 0.232 0.4
## Sex Women-Men -0.774 0.184 0.2
## Race White-AfrAm -0.533 0.391 0.8
## IPVstatus:Sex 0 Women- 1 Women -0.822 0.300 0.4
## IPVstatus:Sex 0 Women- 0 Men -0.891 0.127 0.1
## IPVstatus:Sex 0 Women- 1 Men -1.109 0.172 0.1
## IPVstatus:Sex 1 Women- 0 Men -0.736 0.493 0.7
## IPVstatus:Sex 1 Women- 1 Men -0.966 0.550 0.6
## IPVstatus:Sex 0 Men- 1 Men -0.711 0.538 0.8
##
## Final model:
## lme4::lmer(formula = ClockTotal ~ Age + IPVstatus + Sex + Race +
## w1CES + (1 | subclass) + Age:IPVstatus + Age:Sex + Age:Race +
## Age:w1CES + IPVstatus:Sex + IPVstatus:w1CES + Sex:w1CES +
## Age:IPVstatus:Sex + Age:IPVstatus:w1CES + Age:Sex:w1CES +
## IPVstatus:Sex:w1CES + Age:IPVstatus:Sex:w1CES, data = IPVandCognitionDataSet2,
## REML = reml, contrasts = l)
Re-run suggested final Model 2
(mm2 = lmer(ClockTotal ~ Age + IPVstatus + Sex + Race + w1CES + (Age | HNDid) +
(1 | subclass) + Age:IPVstatus + Age:Sex + Age:Race + Age:w1CES + IPVstatus:Sex +
IPVstatus:w1CES + Sex:w1CES + Age:IPVstatus:Sex + Age:IPVstatus:w1CES +
Age:Sex:w1CES + IPVstatus:Sex:w1CES + Age:IPVstatus:Sex:w1CES, data = IPVandCognitionDataSet2))
## Warning: number of observations <= rank(Z); variance-covariance matrix
## will be unidentifiable
## Linear mixed model fit by REML ['merModLmerTest']
## Formula: ClockTotal ~ Age + IPVstatus + Sex + Race + w1CES + (Age | HNDid) + (1 | subclass) + Age:IPVstatus + Age:Sex + Age:Race + Age:w1CES + IPVstatus:Sex + IPVstatus:w1CES + Sex:w1CES + Age:IPVstatus:Sex + Age:IPVstatus:w1CES + Age:Sex:w1CES + IPVstatus:Sex:w1CES + Age:IPVstatus:Sex:w1CES
## Data: IPVandCognitionDataSet2
## REML criterion at convergence: 442.2
## Random effects:
## Groups Name Std.Dev. Corr
## HNDid (Intercept) 0.6578
## Age 0.0455 1.00
## subclass (Intercept) 0.5844
## Residual 0.8913
## Number of obs: 126, groups: HNDid, 63; subclass, 21
## Fixed Effects:
## (Intercept) Age
## 7.79096 -0.05555
## IPVstatus1 SexMen
## -0.79715 1.06472
## RaceAfrAm w1CES
## 0.66852 0.03056
## Age:IPVstatus1 Age:SexMen
## -0.14200 -0.02797
## Age:RaceAfrAm Age:w1CES
## 0.08704 0.00102
## IPVstatus1:SexMen IPVstatus1:w1CES
## 3.54170 0.01726
## SexMen:w1CES Age:IPVstatus1:SexMen
## -0.04111 0.51598
## Age:IPVstatus1:w1CES Age:SexMen:w1CES
## 0.00199 0.00168
## IPVstatus1:SexMen:w1CES Age:IPVstatus1:SexMen:w1CES
## -0.20174 -0.02720
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: ClockTotal ~ Age + IPVstatus + Sex + Race + w1CES + (Age | HNDid) + (1 | subclass) + Age:IPVstatus + Age:Sex + Age:Race + Age:w1CES + IPVstatus:Sex + IPVstatus:w1CES + Sex:w1CES + Age:IPVstatus:Sex + Age:IPVstatus:w1CES + Age:Sex:w1CES + IPVstatus:Sex:w1CES + Age:IPVstatus:Sex:w1CES
## Data: IPVandCognitionDataSet2
##
## REML criterion at convergence: 442.2
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## HNDid (Intercept) 0.43275 0.6578
## Age 0.00207 0.0455 1.00
## subclass (Intercept) 0.34158 0.5844
## Residual 0.79447 0.8913
## Number of obs: 126, groups: HNDid, 63; subclass, 21
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 7.79096 0.79789 33.60000 9.76 2.4e-11
## Age -0.05555 0.06624 46.00000 -0.84 0.406
## IPVstatus1 -0.79715 1.15124 48.00000 -0.69 0.492
## SexMen 1.06472 0.89270 33.80000 1.19 0.241
## RaceAfrAm 0.66852 0.41253 25.60000 1.62 0.117
## w1CES 0.03056 0.04303 41.40000 0.71 0.482
## Age:IPVstatus1 -0.14200 0.10612 76.60000 -1.34 0.185
## Age:SexMen -0.02797 0.08440 59.00000 -0.33 0.741
## Age:RaceAfrAm 0.08704 0.03813 45.10000 2.28 0.027
## Age:w1CES 0.00102 0.00375 60.80000 0.27 0.787
## IPVstatus1:SexMen 3.54170 2.06555 24.70000 1.71 0.099
## IPVstatus1:w1CES 0.01726 0.05301 34.80000 0.33 0.747
## SexMen:w1CES -0.04111 0.05422 45.00000 -0.76 0.452
## Age:IPVstatus1:SexMen 0.51598 0.19708 47.40000 2.62 0.012
## Age:IPVstatus1:w1CES 0.00199 0.00479 50.40000 0.41 0.680
## Age:SexMen:w1CES 0.00168 0.00565 88.30000 0.30 0.767
## IPVstatus1:SexMen:w1CES -0.20174 0.11344 27.20000 -1.78 0.087
## Age:IPVstatus1:SexMen:w1CES -0.02720 0.01203 55.80000 -2.26 0.028
##
## Correlation of Fixed Effects:
## (Intr) Age IPVst1 SexMen RcAfrA w1CES Ag:IPV1 Ag:SxM
## Age 0.862
## IPVstatus1 -0.577 -0.556
## SexMen -0.789 -0.712 0.477
## RaceAfrAm -0.467 -0.379 0.161 0.216
## w1CES -0.838 -0.766 0.536 0.723 0.172
## Ag:IPVstts1 -0.469 -0.567 0.898 0.394 0.123 0.451
## Age:SexMen -0.663 -0.746 0.414 0.860 0.166 0.624 0.435
## Age:RcAfrAm -0.294 -0.378 0.113 0.122 0.803 0.066 0.095 0.101
## Age:w1CES -0.730 -0.842 0.505 0.649 0.110 0.888 0.535 0.693
## IPVstts1:SM 0.225 0.223 -0.515 -0.386 0.120 -0.269 -0.471 -0.358
## IPVst1:1CES 0.702 0.667 -0.844 -0.583 -0.199 -0.812 -0.734 -0.503
## SexMn:w1CES 0.723 0.659 -0.440 -0.858 -0.180 -0.838 -0.367 -0.766
## Ag:IPVs1:SM 0.199 0.225 -0.452 -0.337 0.125 -0.246 -0.517 -0.423
## A:IPV1:1CES 0.607 0.686 -0.747 -0.509 -0.168 -0.708 -0.832 -0.534
## Ag:SxM:1CES 0.568 0.639 -0.360 -0.700 -0.117 -0.652 -0.383 -0.870
## IPV1:SM:1CE -0.203 -0.191 0.336 0.361 -0.184 0.339 0.299 0.339
## A:IPV1:SM:1 -0.166 -0.162 0.256 0.293 -0.183 0.278 0.297 0.377
## Ag:RAA A:1CES IPVs1:SM IPV1:1 SM:1CE Ag:IPV1:SM A:IPV1:1
## Age
## IPVstatus1
## SexMen
## RaceAfrAm
## w1CES
## Ag:IPVstts1
## Age:SexMen
## Age:RcAfrAm
## Age:w1CES 0.045
## IPVstts1:SM 0.143 -0.272
## IPVst1:1CES -0.128 -0.735 0.423
## SexMn:w1CES -0.076 -0.745 0.349 0.672
## Ag:IPVs1:SM 0.215 -0.291 0.817 0.366 0.323
## A:IPV1:1CES -0.085 -0.791 0.385 0.864 0.585 0.429
## Ag:SxM:1CES -0.030 -0.733 0.305 0.526 0.823 0.382 0.566
## IPV1:SM:1CE -0.225 0.324 -0.913 -0.407 -0.447 -0.776 -0.361
## A:IPV1:SM:1 -0.310 0.310 -0.697 -0.307 -0.377 -0.909 -0.371
## A:SM:1 IPV1:SM:
## Age
## IPVstatus1
## SexMen
## RaceAfrAm
## w1CES
## Ag:IPVstts1
## Age:SexMen
## Age:RcAfrAm
## Age:w1CES
## IPVstts1:SM
## IPVst1:1CES
## SexMn:w1CES
## Ag:IPVs1:SM
## A:IPV1:1CES
## Ag:SxM:1CES
## IPV1:SM:1CE -0.381
## A:IPV1:SM:1 -0.452 0.804
plot(st)
plot(mm2)
