Trails A 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: TrailsAtestSec ~ (Age + IPVstatus + Sex + Race)^4 + (Age | HNDid) + (1 | subclass)
## Data: IPVandCognitionDataSet2
## AIC BIC logLik deviance
## 1381.2 1440.8 -669.6 1339.2
## Random effects:
## Groups Name Std.Dev. Corr
## HNDid (Intercept) 0.00e+00
## Age 1.77e+00 NaN
## subclass (Intercept) 2.55e-06
## Residual 4.63e+01
## Number of obs: 126, groups: HNDid, 63; subclass, 21
## Fixed Effects:
## (Intercept) Age
## 30.090 0.254
## IPVstatus1 SexMen
## 0.212 -9.708
## RaceAfrAm Age:IPVstatus1
## 11.479 0.118
## Age:SexMen Age:RaceAfrAm
## -0.288 0.353
## IPVstatus1:SexMen IPVstatus1:RaceAfrAm
## 22.979 3.001
## SexMen:RaceAfrAm Age:IPVstatus1:SexMen
## -0.260 0.978
## Age:IPVstatus1:RaceAfrAm Age:SexMen:RaceAfrAm
## 0.785 -1.832
## IPVstatus1:SexMen:RaceAfrAm Age:IPVstatus1:SexMen:RaceAfrAm
## -9.684 1.218
## 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: no non-missing arguments to max; returning -Inf
## Warning: no non-missing arguments to max; returning -Inf
##
## Random effects:
## Chi.sq Chi.DF elim.num p.value
## (1 | subclass) 0.00 1 1 1.0000
## (Age | HNDid) 0.02 1 2 0.8760
## (1 | HNDid) 0.00 1 3 1.0000
## (Age + 0 | HNDid) 6.56 1 kept 0.0105
##
## Fixed effects:
## Sum Sq Mean Sq NumDF DenDF F.value elim.num
## Age:IPVstatus:Sex:Race 45.4128 45.4128 1 95.48 0.0190 1
## Age:Sex:Race 292.1723 292.1723 1 96.51 0.0934 2
## Age:IPVstatus:Race 94.5952 94.5952 1 89.48 0.0476 3
## Age:Race 0.0126 0.0126 1 86.63 0.0277 4
## IPVstatus:Sex:Race 331.1841 331.1841 1 103.10 0.1492 5
## Sex:Race 171.3033 171.3033 1 103.37 0.0658 6
## IPVstatus:Race 548.6032 548.6032 1 105.77 0.2041 7
## Age:IPVstatus:Sex 648.8727 648.8727 1 99.02 0.3456 8
## IPVstatus:Sex 32.3929 32.3929 1 109.78 0.0145 9
## Age:Sex 887.6075 887.6075 1 98.71 0.4218 10
## Sex 976.6424 976.6424 1 111.33 0.1209 11
## Age:IPVstatus 1450.1250 1450.1250 1 101.13 0.8106 12
## IPVstatus 188.0815 188.0815 1 116.18 0.0094 13
## Age 558.7452 558.7452 1 94.69 0.1981 14
## Race 2177.2088 2177.2088 1 112.45 1.3215 15
## Pr(>F)
## Age:IPVstatus:Sex:Race 0.8908
## Age:Sex:Race 0.7606
## Age:IPVstatus:Race 0.8277
## Age:Race 0.8681
## IPVstatus:Sex:Race 0.7001
## Sex:Race 0.7980
## IPVstatus:Race 0.6524
## Age:IPVstatus:Sex 0.5580
## IPVstatus:Sex 0.9044
## Age:Sex 0.5175
## Sex 0.7287
## Age:IPVstatus 0.3701
## IPVstatus 0.9229
## Age 0.6572
## Race 0.2528
##
## Least squares means:
## Estimate Standard Error DF t-value Lower CI Upper CI p-value
##
## Differences of LSMEANS:
## Estimate Standard Error DF t-value Lower CI Upper CI p-value
##
## Final model:
## lme4::lmer(formula = TrailsAtestSec ~ (Age + 0 | HNDid), data = IPVandCognitionDataSet2,
## REML = reml, contrasts = l)
Will not allow me to run Trails A final Model 1
Trails A Regression Model 2 (with CES)
load("~/Desktop/Megan/Research/IPV and Cognition Paper/IPV R Output/IPVandCognitionDataSet2.rda")
library(lme4)
library(lmerTest)
(mm2 = lmer(TrailsAtestSec ~ (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: TrailsAtestSec ~ (Age + IPVstatus + Sex + Race + CES1)^5 + (Age | HNDid) + (1 | subclass)
## Data: IPVandCognitionDataSet2
## AIC BIC logLik deviance
## 1407.6 1512.6 -666.8 1333.6
## Random effects:
## Groups Name Std.Dev. Corr
## HNDid (Intercept) 8.72e-01
## Age 1.21e+00 -1.00
## subclass (Intercept) 4.31e-04
## Residual 4.65e+01
## Number of obs: 126, groups: HNDid, 63; subclass, 21
## Fixed Effects:
## (Intercept)
## 30.3798
## Age
## 0.0755
## IPVstatus1
## 14.6202
## SexMen
## -9.8277
## RaceAfrAm
## 8.3326
## CES11
## 4.6202
## Age:IPVstatus1
## 1.9245
## Age:SexMen
## -0.0698
## Age:RaceAfrAm
## 0.4424
## Age:CES11
## 0.9245
## IPVstatus1:SexMen
## 10.1025
## IPVstatus1:RaceAfrAm
## -4.4392
## IPVstatus1:CES11
## -20.9920
## SexMen:RaceAfrAm
## 7.7783
## SexMen:CES11
## -6.9723
## RaceAfrAm:CES11
## 1.8188
## Age:IPVstatus1:SexMen
## -0.9835
## Age:IPVstatus1:RaceAfrAm
## -0.6883
## Age:IPVstatus1:CES11
## -2.7583
## Age:SexMen:RaceAfrAm
## -0.1476
## Age:SexMen:CES11
## -1.7302
## Age:RaceAfrAm:CES11
## -0.9260
## IPVstatus1:SexMen:RaceAfrAm
## -18.9465
## IPVstatus1:SexMen:CES11
## 17.0692
## IPVstatus1:RaceAfrAm:CES11
## 8.5263
## SexMen:RaceAfrAm:CES11
## -19.6817
## Age:IPVstatus1:SexMen:RaceAfrAm
## -0.5533
## Age:IPVstatus1:SexMen:CES11
## 3.6173
## Age:IPVstatus1:RaceAfrAm:CES11
## 2.7190
## Age:SexMen:RaceAfrAm:CES11
## -3.8414
## IPVstatus1:SexMen:RaceAfrAm:CES11
## 27.2041
## Age:IPVstatus1:SexMen:RaceAfrAm:CES11
## 4.6643
(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: no non-missing arguments to max; returning -Inf
## Warning: no non-missing arguments to max; returning -Inf
##
## Random effects:
## Chi.sq Chi.DF elim.num p.value
## (Age | HNDid) 0.00 1 1 1.0000
## (1 | subclass) 0.00 1 2 1.0000
## (1 | HNDid) 0.00 1 3 1.0000
## (Age + 0 | HNDid) 3.12 1 kept 0.0773
##
## Fixed effects:
## Sum Sq Mean Sq NumDF DenDF F.value elim.num
## Age:IPVstatus:Sex:Race:CES1 24.10 24.10 1 86.66 0.0087 1
## Age:IPVstatus:Sex:Race 212.06 212.06 1 88.71 0.0016 2
## IPVstatus:Sex:Race:CES1 20.35 20.35 1 93.92 0.0059 3
## Age:Sex:Race:CES1 20.34 20.34 1 92.01 0.0077 4
## Sex:Race:CES1 172.07 172.07 1 95.71 0.0044 5
## Age:Sex:Race 312.61 312.61 1 98.82 0.0059 6
## IPVstatus:Sex:Race 30.33 30.33 1 70.92 0.0485 7
## Sex:Race 554.41 554.41 1 67.11 0.0224 8
## Age:IPVstatus:Race:CES1 136.72 136.72 1 79.48 0.0649 9
## Age:Race:CES1 65.13 65.13 1 72.92 0.0001 10
## Age:IPVstatus:Race 153.27 153.27 1 92.20 0.0390 11
## Age:Race 67.95 67.95 1 85.28 0.0172 12
## IPVstatus:Race:CES1 532.02 532.02 1 82.14 0.0517 13
## Race:CES1 350.24 350.24 1 85.74 0.0160 14
## IPVstatus:Race 308.94 308.94 1 89.63 0.0552 15
## Race 2303.12 2303.12 1 89.08 0.4058 16
## Age:IPVstatus:Sex:CES1 613.76 613.76 1 96.05 1.3460 17
## IPVstatus:Sex:CES1 1005.64 1005.64 1 93.18 0.1389 18
## Age:IPVstatus:Sex 435.86 435.86 1 92.72 0.7846 19
## IPVstatus:Sex 42.56 42.56 1 96.54 0.0200 20
## Age:IPVstatus:CES1 455.29 455.29 1 95.36 0.4354 21
## Age:Sex:CES1 1656.23 1656.23 1 87.91 0.4996 22
## Age:CES1 1296.42 1296.42 1 80.38 0.3067 23
## Sex:CES1 621.64 621.64 1 108.60 0.4082 24
## Age:Sex 1145.18 1145.18 1 95.31 0.6167 25
## Sex 1072.52 1072.52 1 108.28 0.1665 26
## IPVstatus:CES1 2191.59 2191.59 1 111.36 0.8732 27
## Age:IPVstatus 1357.76 1357.76 1 101.59 0.7653 28
## Age 472.15 472.15 1 94.87 0.1456 29
## IPVstatus 250.78 250.78 1 113.96 0.3159 30
## CES1 1696.13 1696.13 1 116.79 0.8347 31
## Pr(>F)
## Age:IPVstatus:Sex:Race:CES1 0.9259
## Age:IPVstatus:Sex:Race 0.9684
## IPVstatus:Sex:Race:CES1 0.9391
## Age:Sex:Race:CES1 0.9303
## Sex:Race:CES1 0.9474
## Age:Sex:Race 0.9388
## IPVstatus:Sex:Race 0.8264
## Sex:Race 0.8815
## Age:IPVstatus:Race:CES1 0.7996
## Age:Race:CES1 0.9933
## Age:IPVstatus:Race 0.8439
## Age:Race 0.8959
## IPVstatus:Race:CES1 0.8207
## Race:CES1 0.8996
## IPVstatus:Race 0.8148
## Race 0.5257
## Age:IPVstatus:Sex:CES1 0.2489
## IPVstatus:Sex:CES1 0.7102
## Age:IPVstatus:Sex 0.3780
## IPVstatus:Sex 0.8879
## Age:IPVstatus:CES1 0.5109
## Age:Sex:CES1 0.4816
## Age:CES1 0.5813
## Sex:CES1 0.5242
## Age:Sex 0.4342
## Sex 0.6840
## IPVstatus:CES1 0.3521
## Age:IPVstatus 0.3837
## Age 0.7037
## IPVstatus 0.5752
## CES1 0.3628
##
## Least squares means:
## Estimate Standard Error DF t-value Lower CI Upper CI p-value
##
## Differences of LSMEANS:
## Estimate Standard Error DF t-value Lower CI Upper CI p-value
##
## Final model:
## lme4::lmer(formula = TrailsAtestSec ~ (Age + 0 | HNDid), data = IPVandCognitionDataSet2,
## REML = reml, contrasts = l)
Will not allow me to run Trails A suggested final Model 2
Trails B Regression Model 1
load("~/Desktop/Megan/Research/IPV and Cognition Paper/IPV R Output/IPVandCognitionDataSet2.rda")
library(lme4)
library(lmerTest)
(mm1 = lmer(TrailsBtestSec ~ (Age + IPVstatus + Sex + Race)^4 + (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)^4 + (Age | HNDid) + (1 | subclass)
## Data: IPVandCognitionDataSet2
## AIC BIC logLik deviance
## 1591.1 1650.6 -774.5 1549.1
## Random effects:
## Groups Name Std.Dev. Corr
## HNDid (Intercept) 173.8
## Age 12.1 0.71
## subclass (Intercept) 33.1
## Residual 48.9
## Number of obs: 126, groups: HNDid, 63; subclass, 21
## Fixed Effects:
## (Intercept) Age
## 81.45 1.76
## IPVstatus1 SexMen
## -2.07 -51.03
## RaceAfrAm Age:IPVstatus1
## 177.08 -0.15
## Age:SexMen Age:RaceAfrAm
## -3.36 2.54
## IPVstatus1:SexMen IPVstatus1:RaceAfrAm
## 269.20 -172.07
## SexMen:RaceAfrAm Age:IPVstatus1:SexMen
## -73.06 14.32
## Age:IPVstatus1:RaceAfrAm Age:SexMen:RaceAfrAm
## -2.62 1.41
## IPVstatus1:SexMen:RaceAfrAm Age:IPVstatus1:SexMen:RaceAfrAm
## 79.24 -8.19
(st = step(mm1))
## 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
##
## Random effects:
## Chi.sq Chi.DF elim.num p.value
## (1 | subclass) 0.07 1 1 0.7963
## (Age | HNDid) 6.21 1 kept 0.0127
##
## Fixed effects:
## Sum Sq Mean Sq NumDF DenDF F.value elim.num
## Age:IPVstatus:Sex:Race 1.030e+02 1.030e+02 1 58.20 0.0470 1
## Age:Sex:Race 3.370e-02 3.370e-02 1 57.93 0.0004 2
## Age:IPVstatus:Race 4.108e+02 4.108e+02 1 61.14 0.1774 3
## Age:Race 1.578e+02 1.578e+02 1 60.49 0.0755 4
## Age:IPVstatus:Sex 1.317e+03 1.317e+03 1 64.08 0.5903 5
## Age:Sex 7.921e+01 7.921e+01 1 64.75 0.0276 6
## Age:IPVstatus 7.289e+01 7.289e+01 1 67.14 0.1176 7
## IPVstatus:Sex:Race 1.321e+03 1.321e+03 1 47.99 0.6635 8
## Sex:Race 4.709e+02 4.709e+02 1 49.66 0.2014 9
## IPVstatus:Race 2.630e+03 2.630e+03 1 50.79 1.1116 10
## Age 5.336e+03 5.336e+03 1 66.89 2.1616 11
## Race 5.713e+03 5.713e+03 1 51.61 3.4140 12
## IPVstatus 2.898e+01 2.898e+01 1 54.99 0.0735 kept
## Sex 1.638e+02 1.638e+02 1 54.99 1.4959 kept
## IPVstatus:Sex 1.711e+04 1.711e+04 1 54.99 7.8470 kept
## Pr(>F)
## Age:IPVstatus:Sex:Race 0.8291
## Age:Sex:Race 0.9838
## Age:IPVstatus:Race 0.6751
## Age:Race 0.7844
## Age:IPVstatus:Sex 0.4451
## Age:Sex 0.8687
## Age:IPVstatus 0.7328
## IPVstatus:Sex:Race 0.4193
## Sex:Race 0.6555
## IPVstatus:Race 0.2967
## Age 0.1462
## Race 0.0704
## IPVstatus 0.7873
## Sex 0.2265
## IPVstatus:Sex 0.0070
##
## Least squares means:
## IPVstatus Sex Estimate Standard Error DF t-value
## IPVstatus 0 1.0 NA 138.3 24.0 53.8 5.76
## IPVstatus 1 2.0 NA 149.7 34.6 55.5 4.33
## Sex Women NA 2.0 118.2 28.2 53.1 4.19
## Sex Men NA 1.0 169.7 31.2 56.5 5.43
## IPVstatus:Sex 0 Women 1.0 2.0 171.5 35.1 58.4 4.88
## IPVstatus:Sex 1 Women 2.0 2.0 65.0 44.1 50.1 1.47
## IPVstatus:Sex 0 Men 1.0 1.0 105.0 32.8 48.5 3.21
## IPVstatus:Sex 1 Men 2.0 1.0 234.4 53.2 59.6 4.40
## Lower CI Upper CI p-value
## IPVstatus 0 90.1 186 <2e-16
## IPVstatus 1 80.4 219 0.0001
## Sex Women 61.7 175 0.0001
## Sex Men 107.1 232 <2e-16
## IPVstatus:Sex 0 Women 101.1 242 <2e-16
## IPVstatus:Sex 1 Women -23.6 154 0.1468
## IPVstatus:Sex 0 Men 39.2 171 0.0024
## IPVstatus:Sex 1 Men 127.9 341 <2e-16
##
## Differences of LSMEANS:
## Estimate Standard Error DF t-value
## IPVstatus 0-1 -11.4 42.1 55.0 -0.27
## Sex Women-Men -51.5 42.1 55.0 -1.22
## IPVstatus:Sex 0 Women- 1 Women 106.5 56.4 53.1 1.89
## IPVstatus:Sex 0 Women- 0 Men 66.4 48.0 53.8 1.38
## IPVstatus:Sex 0 Women- 1 Men -62.9 63.8 59.2 -0.99
## IPVstatus:Sex 1 Women- 0 Men -40.1 54.9 49.8 -0.73
## IPVstatus:Sex 1 Women- 1 Men -169.4 69.1 55.5 -2.45
## IPVstatus:Sex 0 Men- 1 Men -129.3 62.5 56.5 -2.07
## Lower CI Upper CI p-value
## IPVstatus 0-1 -95.8 72.94 0.79
## Sex Women-Men -135.8 32.87 0.23
## IPVstatus:Sex 0 Women- 1 Women -6.6 219.59 0.06
## IPVstatus:Sex 0 Women- 0 Men -29.9 162.76 0.17
## IPVstatus:Sex 0 Women- 1 Men -190.5 64.75 0.33
## IPVstatus:Sex 1 Women- 0 Men -150.4 70.27 0.47
## IPVstatus:Sex 1 Women- 1 Men -307.9 -30.88 0.02
## IPVstatus:Sex 0 Men- 1 Men -254.5 -4.13 0.04
##
## Final model:
## lme4::lmer(formula = TrailsBtestSec ~ IPVstatus + Sex + (Age |
## HNDid) + IPVstatus:Sex, data = IPVandCognitionDataSet2, REML = reml,
## contrasts = l)
Re-run Trails B suggested final Model 1
(mm1 = lmer(TrailsBtestSec ~ 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 ~ IPVstatus + Sex + (Age | HNDid) + (1 | subclass) + IPVstatus:Sex
## Data: IPVandCognitionDataSet2
## AIC BIC logLik deviance
## 1576.1 1601.6 -779.1 1558.1
## Random effects:
## Groups Name Std.Dev. Corr
## HNDid (Intercept) 1.93e+02
## Age 1.27e+01 0.72
## subclass (Intercept) 7.80e-04
## Residual 4.86e+01
## Number of obs: 126, groups: HNDid, 63; subclass, 21
## Fixed Effects:
## (Intercept) IPVstatus1 SexMen
## 172.1 -107.2 -66.8
## IPVstatus1:SexMen
## 237.0
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 maximum likelihood ['merModLmerTest']
## Formula: TrailsBtestSec ~ IPVstatus + Sex + (Age | HNDid) + (1 | subclass) + IPVstatus:Sex
## Data: IPVandCognitionDataSet2
##
## AIC BIC logLik deviance
## 1576.1 1601.6 -779.1 1558.1
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## HNDid (Intercept) 3.72e+04 1.93e+02
## Age 1.63e+02 1.27e+01 0.72
## subclass (Intercept) 6.09e-07 7.80e-04
## Residual 2.36e+03 4.86e+01
## Number of obs: 126, groups: HNDid, 63; subclass, 21
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 172.1 34.0 62.6 5.07 3.8e-06
## IPVstatus1 -107.2 54.4 57.0 -1.97 0.054
## SexMen -66.8 46.4 57.8 -1.44 0.155
## IPVstatus1:SexMen 237.0 81.3 59.1 2.92 0.005
##
## Correlation of Fixed Effects:
## (Intr) IPVst1 SexMen
## IPVstatus1 -0.624
## SexMen -0.733 0.457
## IPVstts1:SM 0.418 -0.669 -0.570
plot(st)
plot(mm1)
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 + 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: TrailsBtestSec ~ (Age + IPVstatus + Sex + Race + CES1)^5 + (Age | HNDid) + (1 | subclass)
## Data: IPVandCognitionDataSet2
## AIC BIC logLik deviance
## 1603.1 1708.1 -764.6 1529.1
## Random effects:
## Groups Name Std.Dev. Corr
## HNDid (Intercept) 1.63e+02
## Age 9.42e+00 0.79
## subclass (Intercept) 2.39e-04
## Residual 5.34e+01
## Number of obs: 126, groups: HNDid, 63; subclass, 21
## Fixed Effects:
## (Intercept)
## 96.46
## Age
## 2.64
## IPVstatus1
## 9.54
## SexMen
## -67.80
## RaceAfrAm
## 103.30
## CES11
## -155.66
## Age:IPVstatus1
## 1.70
## Age:SexMen
## -4.54
## Age:RaceAfrAm
## 3.77
## Age:CES11
## -9.44
## IPVstatus1:SexMen
## 369.06
## IPVstatus1:RaceAfrAm
## -135.66
## IPVstatus1:CES11
## 118.66
## SexMen:RaceAfrAm
## -46.08
## SexMen:CES11
## 179.60
## RaceAfrAm:CES11
## 276.79
## Age:IPVstatus1:SexMen
## 16.15
## Age:IPVstatus1:RaceAfrAm
## -7.46
## Age:IPVstatus1:CES11
## 5.64
## Age:SexMen:RaceAfrAm
## -6.38
## Age:SexMen:CES11
## 8.94
## Age:RaceAfrAm:CES11
## -1.47
## IPVstatus1:SexMen:RaceAfrAm
## 184.18
## IPVstatus1:SexMen:CES11
## -420.85
## IPVstatus1:RaceAfrAm:CES11
## -225.01
## SexMen:RaceAfrAm:CES11
## -186.60
## Age:IPVstatus1:SexMen:RaceAfrAm
## -5.87
## Age:IPVstatus1:SexMen:CES11
## -14.09
## Age:IPVstatus1:RaceAfrAm:CES11
## 8.19
## Age:SexMen:RaceAfrAm:CES11
## 16.21
## IPVstatus1:SexMen:RaceAfrAm:CES11
## 208.52
## Age:IPVstatus1:SexMen:RaceAfrAm:CES11
## -2.06
(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
##
## Random effects:
## Chi.sq Chi.DF elim.num p.value
## (1 | subclass) 0.00 1 1 1.0000
## (Age | HNDid) 4.08 1 kept 0.0433
##
## Fixed effects:
## Sum Sq Mean Sq NumDF DenDF F.value
## Age:IPVstatus:Sex:Race:CES1 4.545 4.545 1 56.07 0.0017
## Age:IPVstatus:Sex:Race 103.758 103.758 1 57.35 0.0296
## Age:IPVstatus:Race:CES1 110.399 110.399 1 58.56 0.0656
## Age:Sex:Race:CES1 600.965 600.965 1 59.47 0.1722
## Age:Sex:Race 3.270 3.270 1 59.64 0.0314
## IPVstatus:Sex:Race:CES1 677.659 677.659 1 48.03 0.2344
## Age:IPVstatus:Race 192.506 192.506 1 61.12 0.2287
## Age:Race:CES1 315.788 315.788 1 63.69 0.1258
## Age:Race 87.421 87.421 1 60.83 0.1282
## Age:IPVstatus:Sex:CES1 1236.407 1236.407 1 60.71 0.7885
## Age:IPVstatus:CES1 10.282 10.282 1 61.38 0.0284
## Age:IPVstatus:Sex 2578.655 2578.655 1 63.03 0.5153
## Age:IPVstatus 33.413 33.413 1 65.62 0.4677
## IPVstatus:Sex:CES1 505.380 505.380 1 39.54 0.4955
## IPVstatus:Race:CES1 1985.103 1985.103 1 40.96 0.7588
## Sex:Race:CES1 3680.134 3680.134 1 45.49 0.6822
## Race:CES1 77.165 77.165 1 46.82 0.1807
## IPVstatus:Sex:Race 4374.819 4374.819 1 46.11 1.3926
## Sex:Race 16.975 16.975 1 47.31 0.0525
## IPVstatus:Race 2444.368 2444.368 1 49.36 0.3051
## Age:Sex:CES1 6623.435 6623.435 1 65.05 2.8053
## Age:Sex 6.226 6.226 1 65.13 0.0762
## Age:CES1 1820.510 1820.510 1 67.11 0.3588
## Age 5683.786 5683.786 1 67.29 2.3382
## IPVstatus:CES1 5577.498 5577.498 1 53.35 3.3572
## IPVstatus 42.337 42.337 1 52.31 0.4321
## Sex 364.240 364.240 1 53.15 0.7216
## Race 8016.936 8016.936 1 50.67 4.8084
## CES1 175.128 175.128 1 51.08 0.0492
## IPVstatus:Sex 21890.428 21890.428 1 52.52 12.3166
## Sex:CES1 18159.979 18159.979 1 51.88 6.2451
## elim.num Pr(>F)
## Age:IPVstatus:Sex:Race:CES1 1 0.9675
## Age:IPVstatus:Sex:Race 2 0.8640
## Age:IPVstatus:Race:CES1 3 0.7988
## Age:Sex:Race:CES1 4 0.6796
## Age:Sex:Race 5 0.8600
## IPVstatus:Sex:Race:CES1 6 0.6305
## Age:IPVstatus:Race 7 0.6342
## Age:Race:CES1 8 0.7240
## Age:Race 9 0.7215
## Age:IPVstatus:Sex:CES1 10 0.3781
## Age:IPVstatus:CES1 11 0.8667
## Age:IPVstatus:Sex 12 0.4755
## Age:IPVstatus 13 0.4965
## IPVstatus:Sex:CES1 14 0.4856
## IPVstatus:Race:CES1 15 0.3888
## Sex:Race:CES1 16 0.4131
## Race:CES1 17 0.6727
## IPVstatus:Sex:Race 18 0.2440
## Sex:Race 19 0.8197
## IPVstatus:Race 20 0.5832
## Age:Sex:CES1 21 0.0988
## Age:Sex 22 0.7834
## Age:CES1 23 0.5512
## Age 24 0.1309
## IPVstatus:CES1 25 0.0725
## IPVstatus kept 0.5139
## Sex kept 0.3994
## Race kept 0.0329
## CES1 kept 0.8254
## IPVstatus:Sex kept 0.0009
## Sex:CES1 kept 0.0157
##
## Least squares means:
## IPVstatus Sex Race CES1 Estimate Standard Error
## IPVstatus 0 1.0 NA NA NA 114.9 26.5
## IPVstatus 1 2.0 NA NA NA 142.4 33.4
## Sex Women NA 2.0 NA NA 111.5 27.5
## Sex Men NA 1.0 NA NA 145.8 31.6
## Race White NA NA 2.0 NA 81.4 35.9
## Race AfrAm NA NA 1.0 NA 175.9 24.0
## CES1 0 NA NA NA 1.0 124.4 28.2
## CES1 1 NA NA NA 2.0 132.9 29.9
## IPVstatus:Sex 0 Women 1.0 2.0 NA NA 170.2 35.8
## IPVstatus:Sex 1 Women 2.0 2.0 NA NA 52.9 41.9
## IPVstatus:Sex 0 Men 1.0 1.0 NA NA 59.6 35.5
## IPVstatus:Sex 1 Men 2.0 1.0 NA NA 232.0 51.7
## Sex:CES1 Women 0 NA 2.0 NA 1.0 58.5 36.3
## Sex:CES1 Men 0 NA 1.0 NA 1.0 190.2 41.7
## Sex:CES1 Women 1 NA 2.0 NA 2.0 164.5 41.1
## Sex:CES1 Men 1 NA 1.0 NA 2.0 101.4 42.2
## DF t-value Lower CI Upper CI p-value
## IPVstatus 0 50.4 4.34 61.67 168 0.0001
## IPVstatus 1 52.5 4.27 75.46 209 0.0001
## Sex Women 51.1 4.05 56.27 167 0.0002
## Sex Men 52.9 4.62 82.52 209 <2e-16
## Race White 49.4 2.27 9.29 154 0.0277
## Race AfrAm 53.9 7.32 127.73 224 <2e-16
## CES1 0 48.5 4.42 67.77 181 0.0001
## CES1 1 52.7 4.45 72.98 193 <2e-16
## IPVstatus:Sex 0 Women 55.2 4.75 98.44 242 <2e-16
## IPVstatus:Sex 1 Women 47.6 1.26 -31.41 137 0.2133
## IPVstatus:Sex 0 Men 48.1 1.68 -11.84 131 0.1000
## IPVstatus:Sex 1 Men 56.2 4.49 128.52 336 <2e-16
## Sex:CES1 Women 0 49.1 1.61 -14.53 132 0.1139
## Sex:CES1 Men 0 47.0 4.56 106.29 274 <2e-16
## Sex:CES1 Women 1 54.3 4.01 82.22 247 0.0002
## Sex:CES1 Men 1 53.1 2.40 16.83 186 0.0197
##
## Differences of LSMEANS:
## Estimate Standard Error DF t-value
## IPVstatus 0-1 -27.6 41.94 52.3 -0.66
## Sex Women-Men -34.3 40.39 53.1 -0.85
## Race White-AfrAm -94.5 43.10 50.7 -2.19
## CES1 0-1 -8.6 38.72 51.1 -0.22
## IPVstatus:Sex 0 Women- 1 Women 117.3 55.17 50.2 2.13
## IPVstatus:Sex 0 Women- 0 Men 110.6 47.73 52.8 2.32
## IPVstatus:Sex 0 Women- 1 Men -61.9 62.10 56.5 -1.00
## IPVstatus:Sex 1 Women- 0 Men -6.7 54.08 48.2 -0.12
## IPVstatus:Sex 1 Women- 1 Men -179.2 66.28 52.9 -2.70
## IPVstatus:Sex 0 Men- 1 Men -172.4 62.31 53.9 -2.77
## Sex:CES1 Women 0- Men 0 -131.7 54.33 48.3 -2.42
## Sex:CES1 Women 0- Women 1 -106.0 54.62 53.0 -1.94
## Sex:CES1 Women 0- Men 1 -42.9 53.80 51.3 -0.80
## Sex:CES1 Men 0- Women 1 25.7 58.02 52.6 0.44
## Sex:CES1 Men 0- Men 1 88.9 55.27 49.3 1.61
## Sex:CES1 Women 1- Men 1 63.1 57.89 54.6 1.09
## Lower CI Upper CI p-value
## IPVstatus 0-1 -111.73 56.59 0.514
## Sex Women-Men -115.31 46.69 0.399
## Race White-AfrAm -181.05 -7.97 0.033
## CES1 0-1 -86.31 69.14 0.825
## IPVstatus:Sex 0 Women- 1 Women 6.49 228.11 0.038
## IPVstatus:Sex 0 Women- 0 Men 14.82 206.30 0.025
## IPVstatus:Sex 0 Women- 1 Men -186.27 62.51 0.323
## IPVstatus:Sex 1 Women- 0 Men -115.45 101.98 0.901
## IPVstatus:Sex 1 Women- 1 Men -312.12 -46.24 0.009
## IPVstatus:Sex 0 Men- 1 Men -297.37 -47.51 0.008
## Sex:CES1 Women 0- Men 0 -240.97 -22.52 0.019
## Sex:CES1 Women 0- Women 1 -215.58 3.54 0.058
## Sex:CES1 Women 0- Men 1 -150.89 65.10 0.429
## Sex:CES1 Men 0- Women 1 -90.67 142.12 0.659
## Sex:CES1 Men 0- Men 1 -22.20 199.91 0.114
## Sex:CES1 Women 1- Men 1 -52.90 179.15 0.280
##
## Final model:
## lme4::lmer(formula = TrailsBtestSec ~ IPVstatus + Sex + Race +
## CES1 + (Age | HNDid) + IPVstatus:Sex + Sex:CES1, data = IPVandCognitionDataSet2,
## REML = reml, contrasts = l)
Re-run the suggested Trails B final Model 2
(mm2 = lmer(TrailsBtestSec ~ IPVstatus + Sex + Race + CES1 + (Age | HNDid) +
(1 | subclass) + IPVstatus:Sex + Sex:CES1, 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 ~ IPVstatus + Sex + Race + CES1 + (Age | HNDid) + (1 | subclass) + IPVstatus:Sex + Sex:CES1
## Data: IPVandCognitionDataSet2
## AIC BIC logLik deviance
## 1572 1606 -774 1548
## Random effects:
## Groups Name Std.Dev. Corr
## HNDid (Intercept) 189.4
## Age 12.5 0.78
## subclass (Intercept) 0.0
## Residual 49.2
## Number of obs: 126, groups: HNDid, 63; subclass, 21
## Fixed Effects:
## (Intercept) IPVstatus1 SexMen
## 69.7 -118.0 -14.9
## RaceAfrAm CES11 IPVstatus1:SexMen
## 95.9 106.7 290.7
## SexMen:CES11
## -192.4
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 ~ IPVstatus + Sex + Race + CES1 + (Age | HNDid) + (1 | subclass) + IPVstatus:Sex + Sex:CES1
## Data: IPVandCognitionDataSet2
##
## AIC BIC logLik deviance
## 1572 1606 -774 1548
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## HNDid (Intercept) 35881 189.4
## Age 155 12.5 0.78
## subclass (Intercept) 0 0.0
## Residual 2416 49.2
## Number of obs: 126, groups: HNDid, 63; subclass, 21
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 69.7 45.9 59.8 1.52 0.1340
## IPVstatus1 -118.0 51.7 57.0 -2.28 0.0263
## SexMen -14.9 49.3 55.7 -0.30 0.7634
## RaceAfrAm 95.9 40.4 57.6 2.37 0.0211
## CES11 106.7 51.3 60.0 2.08 0.0418
## IPVstatus1:SexMen 290.7 77.5 59.8 3.75 0.0004
## SexMen:CES11 -192.4 73.2 59.1 -2.63 0.0109
##
## Correlation of Fixed Effects:
## (Intr) IPVst1 SexMen RcAfrA CES11 IPV1:S
## IPVstatus1 -0.395
## SexMen -0.512 0.305
## RaceAfrAm -0.640 0.095 -0.059
## CES11 -0.390 -0.224 0.322 0.063
## IPVstts1:SM 0.182 -0.655 -0.351 0.064 0.158
## SexMn:CES11 0.327 0.149 -0.488 -0.129 -0.706 -0.258
plot(st)
plot(mm2)
Fluency (Word) Regression Model 1
load("~/Desktop/Megan/Research/IPV and Cognition Paper/IPV R Output/IPVandCognitionDataSet2.rda")
library(lme4)
library(lmerTest)
(mm1 = lmer(formula = FluencyWord ~ (Age + IPVstatus + Sex + Race)^4 + (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)^4 + (Age | HNDid) + (1 | subclass)
## Data: IPVandCognitionDataSet2
## AIC BIC logLik deviance
## 774.3 833.9 -366.1 732.3
## Random effects:
## Groups Name Std.Dev. Corr
## HNDid (Intercept) 4.7066
## Age 0.0264 1.00
## subclass (Intercept) 1.8153
## Residual 2.6705
## Number of obs: 126, groups: HNDid, 63; subclass, 21
## Fixed Effects:
## (Intercept) Age
## 21.5445 -0.3411
## IPVstatus1 SexMen
## 0.0857 9.4832
## RaceAfrAm Age:IPVstatus1
## -5.8343 0.4135
## Age:SexMen Age:RaceAfrAm
## 0.7086 0.0515
## IPVstatus1:SexMen IPVstatus1:RaceAfrAm
## -4.5990 2.7355
## SexMen:RaceAfrAm Age:IPVstatus1:SexMen
## -5.3848 -0.8766
## Age:IPVstatus1:RaceAfrAm Age:SexMen:RaceAfrAm
## -0.0880 -0.4566
## IPVstatus1:SexMen:RaceAfrAm Age:IPVstatus1:SexMen:RaceAfrAm
## 0.3580 0.3453
(st = step(mm1))
## 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) 0.64 1 1 0.4228
## (1 | HNDid) 56.52 1 kept <1e-07
##
## Fixed effects:
## Sum Sq Mean Sq NumDF DenDF F.value elim.num
## Age:IPVstatus:Sex:Race 1.5984 1.5984 1 107.68 0.2061 1
## Age:IPVstatus:Race 0.0530 0.0530 1 108.57 0.0204 2
## IPVstatus:Sex:Race 1.5183 1.5183 1 53.15 0.1994 3
## IPVstatus:Race 4.9799 4.9799 1 53.98 0.6803 4
## Age:Sex:Race 7.8751 7.8751 1 113.79 0.8746 5
## Age:Race 0.7672 0.7672 1 114.96 0.1805 6
## Sex:Race 4.4220 4.4220 1 54.69 0.5217 7
## Age:IPVstatus:Sex 19.9061 19.9061 1 116.18 2.9196 8
## IPVstatus:Sex 0.7636 0.7636 1 57.94 0.0906 9
## Age:IPVstatus 2.8672 2.8672 1 117.12 0.2346 10
## IPVstatus 1.1052 1.1052 1 58.87 0.0799 11
## Age:Sex 6.7766 6.7766 1 120.47 1.0154 12
## Sex 11.6342 11.6342 1 59.92 3.1989 13
## Age 39.3136 39.3136 1 122.92 4.2764 kept
## Race 96.8765 96.8765 1 59.73 11.2532 kept
## Pr(>F)
## Age:IPVstatus:Sex:Race 0.6508
## Age:IPVstatus:Race 0.8866
## IPVstatus:Sex:Race 0.6571
## IPVstatus:Race 0.4131
## Age:Sex:Race 0.3517
## Age:Race 0.6717
## Sex:Race 0.4732
## Age:IPVstatus:Sex 0.0902
## IPVstatus:Sex 0.7644
## Age:IPVstatus 0.6290
## IPVstatus 0.7784
## Age:Sex 0.3156
## Sex 0.0787
## Age 0.0407
## Race 0.0014
##
## Least squares means:
## Race Estimate Standard Error DF t-value Lower CI Upper CI
## Race White 2.0 24.271 1.381 59.7 17.570 21.5 27.0
## Race AfrAm 1.0 18.846 0.839 59.6 22.460 17.2 20.5
## p-value
## Race White <2e-16
## Race AfrAm <2e-16
##
## Differences of LSMEANS:
## Estimate Standard Error DF t-value Lower CI Upper CI
## Race White-AfrAm 5.4 1.62 59.7 3.35 2.19 8.66
## p-value
## Race White-AfrAm 0.001
##
## Final model:
## lme4::lmer(formula = FluencyWord ~ Age + Race + (1 | HNDid),
## data = IPVandCognitionDataSet2, REML = reml, contrasts = l)
Re-run Fluency Word suggested final Model 1
(mm1 = lmer(FluencyWord ~ Age + Race + (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 + Race + (Age | HNDid) + (1 | subclass)
## Data: IPVandCognitionDataSet2
## AIC BIC logLik deviance
## 759.4 782.1 -371.7 743.4
## Random effects:
## Groups Name Std.Dev. Corr
## HNDid (Intercept) 5.2687
## Age 0.0352 1.00
## subclass (Intercept) 1.7340
## Residual 2.6939
## Number of obs: 126, groups: HNDid, 63; subclass, 21
## Fixed Effects:
## (Intercept) Age RaceAfrAm
## 23.163 -0.149 -5.356
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 maximum likelihood ['merModLmerTest']
## Formula: FluencyWord ~ Age + Race + (Age | HNDid) + (1 | subclass)
## Data: IPVandCognitionDataSet2
##
## AIC BIC logLik deviance
## 759.4 782.1 -371.7 743.4
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## HNDid (Intercept) 27.75967 5.2687
## Age 0.00124 0.0352 1.00
## subclass (Intercept) 3.00676 1.7340
## Residual 7.25685 2.6939
## Number of obs: 126, groups: HNDid, 63; subclass, 21
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 23.1630 1.5499 50.7000 14.95 <2e-16
## Age -0.1492 0.0773 81.5000 -1.93 0.0571
## RaceAfrAm -5.3561 1.5776 60.6000 -3.40 0.0012
##
## Correlation of Fixed Effects:
## (Intr) Age
## Age 0.456
## RaceAfrAm -0.771 -0.078
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
## 773.2 878.1 -349.6 699.2
## Random effects:
## Groups Name Std.Dev. Corr
## HNDid (Intercept) 4.18e+00
## Age 7.87e-03 -1.00
## subclass (Intercept) 2.66e-06
## Residual 2.34e+00
## Number of obs: 126, groups: HNDid, 63; subclass, 21
## Fixed Effects:
## (Intercept)
## 21.1073
## Age
## -0.4225
## IPVstatus1
## -5.1073
## SexMen
## 4.2704
## RaceAfrAm
## -5.3151
## CES11
## 13.4927
## Age:IPVstatus1
## 0.5892
## Age:SexMen
## 0.3104
## Age:RaceAfrAm
## 0.1590
## Age:CES11
## 0.8225
## IPVstatus1:SexMen
## -0.7472
## IPVstatus1:RaceAfrAm
## 10.6360
## IPVstatus1:CES11
## -7.0650
## SexMen:RaceAfrAm
## 3.2490
## SexMen:CES11
## -4.0704
## RaceAfrAm:CES11
## -13.3566
## Age:IPVstatus1:SexMen
## -0.5604
## Age:IPVstatus1:RaceAfrAm
## -0.0167
## Age:IPVstatus1:CES11
## -0.9994
## Age:SexMen:RaceAfrAm
## 0.2025
## Age:SexMen:CES11
## 1.0896
## Age:RaceAfrAm:CES11
## -0.8428
## IPVstatus1:SexMen:RaceAfrAm
## -3.0932
## IPVstatus1:SexMen:CES11
## 36.1195
## IPVstatus1:RaceAfrAm:CES11
## 3.2895
## SexMen:RaceAfrAm:CES11
## -5.9985
## Age:IPVstatus1:SexMen:RaceAfrAm
## -0.2615
## Age:IPVstatus1:SexMen:CES11
## 1.1705
## Age:IPVstatus1:RaceAfrAm:CES11
## 0.4626
## Age:SexMen:RaceAfrAm:CES11
## -1.8691
## IPVstatus1:SexMen:RaceAfrAm:CES11
## -29.4970
## Age:IPVstatus1:SexMen:RaceAfrAm:CES11
## -0.0153
(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 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) 53.93 1 kept < 1e-07
##
## Fixed effects:
## Sum Sq Mean Sq NumDF DenDF F.value elim.num
## Age:IPVstatus:Sex:Race:CES1 0.0024 0.0024 1 59.48 0.0004 1
## Age:IPVstatus:Sex:Race 0.2956 0.2956 1 61.24 0.0609 2
## Age:IPVstatus:Race:CES1 4.2967 4.2967 1 66.78 0.2627 3
## Age:IPVstatus:Race 0.0817 0.0817 1 67.41 0.0258 4
## Age:IPVstatus:Sex:CES1 28.1737 28.1737 1 85.77 2.0244 5
## Age:IPVstatus:Sex 13.3544 13.3544 1 89.33 0.0000 6
## Age:IPVstatus:CES1 0.0991 0.0991 1 89.05 0.2444 7
## Age:IPVstatus 2.7655 2.7655 1 90.75 1.9341 8
## IPVstatus:Sex:Race:CES1 16.9661 16.9661 1 52.24 3.1744 9
## IPVstatus:Sex:CES1 3.9072 3.9072 1 50.61 0.2075 10
## IPVstatus:Sex:Race 15.1421 15.1421 1 52.31 1.8721 11
## IPVstatus:Sex 0.8020 0.8020 1 51.70 2.2151 12
## IPVstatus:Race:CES1 18.8467 18.8467 1 55.71 2.6942 13
## IPVstatus:CES1 6.5805 6.5805 1 52.93 0.9125 14
## IPVstatus:Race 5.3101 5.3101 1 55.24 1.5882 15
## IPVstatus 0.9999 0.9999 1 54.97 0.1409 16
## Age 35.2381 35.2381 1 94.10 0.0661 kept
## Sex 10.8191 10.8191 1 90.47 9.1604 kept
## Race 89.0907 89.0907 1 90.47 14.6683 kept
## CES1 0.2259 0.2259 1 90.47 3.0414 kept
## Age:Sex 6.7866 6.7866 1 94.10 4.8247 kept
## Age:Race 0.9934 0.9934 1 94.10 2.6959 kept
## Age:CES1 3.2265 3.2265 1 94.10 1.5529 kept
## Sex:Race 9.5822 9.5822 1 90.47 2.7649 kept
## Sex:CES1 1.1223 1.1223 1 90.47 1.4572 kept
## Race:CES1 26.7105 26.7105 1 90.47 11.3809 kept
## Age:Sex:Race 5.1503 5.1503 1 94.10 2.3126 kept
## Age:Sex:CES1 5.6543 5.6543 1 94.10 0.9979 kept
## Age:Race:CES1 40.3108 40.3108 1 94.10 8.5681 kept
## Sex:Race:CES1 3.9103 3.9103 1 90.47 7.2790 kept
## Age:Sex:Race:CES1 21.9428 21.9428 1 94.10 3.9511 kept
## Pr(>F)
## Age:IPVstatus:Sex:Race:CES1 0.9849
## Age:IPVstatus:Sex:Race 0.8058
## Age:IPVstatus:Race:CES1 0.6100
## Age:IPVstatus:Race 0.8728
## Age:IPVstatus:Sex:CES1 0.1584
## Age:IPVstatus:Sex 0.9958
## Age:IPVstatus:CES1 0.6222
## Age:IPVstatus 0.1677
## IPVstatus:Sex:Race:CES1 0.0806
## IPVstatus:Sex:CES1 0.6507
## IPVstatus:Sex:Race 0.1771
## IPVstatus:Sex 0.1427
## IPVstatus:Race:CES1 0.1063
## IPVstatus:CES1 0.3438
## IPVstatus:Race 0.2129
## IPVstatus 0.7088
## Age 0.7976
## Sex 0.0032
## Race 0.0002
## CES1 0.0846
## Age:Sex 0.0305
## Age:Race 0.1039
## Age:CES1 0.2158
## Sex:Race 0.0998
## Sex:CES1 0.2305
## Race:CES1 0.0011
## Age:Sex:Race 0.1317
## Age:Sex:CES1 0.3204
## Age:Race:CES1 0.0043
## Sex:Race:CES1 0.0083
## Age:Sex:Race:CES1 0.0497
##
## Least squares means:
## Sex Race CES1 Estimate Standard Error DF
## Sex Women 2.0 NA NA 20.547 1.109 56.2
## Sex Men 1.0 NA NA 24.183 1.388 54.2
## Race White NA 2.0 NA 25.510 1.536 54.4
## Race AfrAm NA 1.0 NA 19.220 0.892 56.6
## CES1 0 NA NA 1.0 21.303 1.095 53.1
## CES1 1 NA NA 2.0 23.427 1.398 56.2
## Sex:Race Women White 2.0 2.0 NA 22.926 1.776 55.3
## Sex:Race Men White 1.0 2.0 NA 28.094 2.507 54.0
## Sex:Race Women AfrAm 2.0 1.0 NA 18.168 1.326 58.0
## Sex:Race Men AfrAm 1.0 1.0 NA 20.272 1.192 55.1
## Sex:CES1 Women 0 2.0 NA 1.0 20.119 1.409 51.6
## Sex:CES1 Men 0 1.0 NA 1.0 22.487 1.678 54.1
## Sex:CES1 Women 1 2.0 NA 2.0 20.975 1.712 59.6
## Sex:CES1 Men 1 1.0 NA 2.0 25.879 2.211 54.2
## Race:CES1 White 0 NA 2.0 1.0 22.777 1.852 52.2
## Race:CES1 AfrAm 0 NA 1.0 1.0 19.829 1.170 55.2
## Race:CES1 White 1 NA 2.0 2.0 28.243 2.451 55.7
## Race:CES1 AfrAm 1 NA 1.0 2.0 18.611 1.346 57.8
## Sex:Race:CES1 Women White 0 2.0 2.0 1.0 22.425 2.330 51.6
## Sex:Race:CES1 Men White 0 1.0 2.0 1.0 23.128 2.880 52.6
## Sex:Race:CES1 Women AfrAm 0 2.0 1.0 1.0 17.812 1.583 51.6
## Sex:Race:CES1 Men AfrAm 0 1.0 1.0 1.0 21.846 1.724 58.5
## Sex:Race:CES1 Women White 1 2.0 2.0 2.0 23.427 2.682 58.2
## Sex:Race:CES1 Men White 1 1.0 2.0 2.0 33.060 4.104 54.7
## Sex:Race:CES1 Women AfrAm 1 2.0 1.0 2.0 18.523 2.129 61.8
## Sex:Race:CES1 Men AfrAm 1 1.0 1.0 2.0 18.699 1.647 51.6
## t-value Lower CI Upper CI p-value
## Sex Women 18.54 18.3 22.8 <2e-16
## Sex Men 17.42 21.4 27.0 <2e-16
## Race White 16.61 22.4 28.6 <2e-16
## Race AfrAm 21.55 17.4 21.0 <2e-16
## CES1 0 19.45 19.1 23.5 <2e-16
## CES1 1 16.75 20.6 26.2 <2e-16
## Sex:Race Women White 12.91 19.4 26.5 <2e-16
## Sex:Race Men White 11.21 23.1 33.1 <2e-16
## Sex:Race Women AfrAm 13.70 15.5 20.8 <2e-16
## Sex:Race Men AfrAm 17.00 17.9 22.7 <2e-16
## Sex:CES1 Women 0 14.28 17.3 22.9 <2e-16
## Sex:CES1 Men 0 13.40 19.1 25.9 <2e-16
## Sex:CES1 Women 1 12.25 17.5 24.4 <2e-16
## Sex:CES1 Men 1 11.70 21.4 30.3 <2e-16
## Race:CES1 White 0 12.30 19.1 26.5 <2e-16
## Race:CES1 AfrAm 0 16.94 17.5 22.2 <2e-16
## Race:CES1 White 1 11.52 23.3 33.2 <2e-16
## Race:CES1 AfrAm 1 13.83 15.9 21.3 <2e-16
## Sex:Race:CES1 Women White 0 9.62 17.7 27.1 <2e-16
## Sex:Race:CES1 Men White 0 8.03 17.4 28.9 <2e-16
## Sex:Race:CES1 Women AfrAm 0 11.26 14.6 21.0 <2e-16
## Sex:Race:CES1 Men AfrAm 0 12.67 18.4 25.3 <2e-16
## Sex:Race:CES1 Women White 1 8.74 18.1 28.8 <2e-16
## Sex:Race:CES1 Men White 1 8.06 24.8 41.3 <2e-16
## Sex:Race:CES1 Women AfrAm 1 8.70 14.3 22.8 <2e-16
## Sex:Race:CES1 Men AfrAm 1 11.35 15.4 22.0 <2e-16
##
## Differences of LSMEANS:
## Estimate Standard Error DF t-value
## Sex Women-Men -3.6 1.776 55.0 -2.05
## Race White-AfrAm 6.3 1.776 55.0 3.54
## CES1 0-1 -2.1 1.776 55.0 -1.20
## Sex:Race Women White- Men White -5.2 3.072 54.4 -1.68
## Sex:Race Women White- Women AfrAm 4.8 2.217 56.2 2.15
## Sex:Race Women White- Men AfrAm 2.7 2.139 55.2 1.24
## Sex:Race Men White- Women AfrAm 9.9 2.836 54.8 3.50
## Sex:Race Men White- Men AfrAm 7.8 2.776 54.2 2.82
## Sex:Race Women AfrAm- Men AfrAm -2.1 1.784 56.6 -1.18
## Sex:CES1 Women 0- Men 0 -2.4 2.191 53.1 -1.08
## Sex:CES1 Women 0- Women 1 -0.9 2.217 56.2 -0.39
## Sex:CES1 Women 0- Men 1 -5.8 2.622 53.5 -2.20
## Sex:CES1 Men 0- Women 1 1.5 2.397 56.8 0.63
## Sex:CES1 Men 0- Men 1 -3.4 2.776 54.2 -1.22
## Sex:CES1 Women 1- Men 1 -4.9 2.796 56.2 -1.75
## Race:CES1 White 0- AfrAm 0 2.9 2.191 53.1 1.35
## Race:CES1 White 0- White 1 -5.5 3.072 54.4 -1.78
## Race:CES1 White 0- AfrAm 1 4.2 2.290 54.1 1.82
## Race:CES1 AfrAm 0- White 1 -8.4 2.716 55.6 -3.10
## Race:CES1 AfrAm 0- AfrAm 1 1.2 1.784 56.6 0.68
## Race:CES1 White 1- AfrAm 1 9.6 2.796 56.2 3.44
## Lower CI Upper CI p-value
## Sex Women-Men -7.196 -0.0764 0.045
## Race White-AfrAm 2.730 9.8497 8e-04
## CES1 0-1 -5.684 1.4356 0.237
## Sex:Race Women White- Men White -11.326 0.9908 0.098
## Sex:Race Women White- Women AfrAm 0.317 9.1992 0.036
## Sex:Race Women White- Men AfrAm -1.634 6.9411 0.220
## Sex:Race Men White- Women AfrAm 4.242 15.6100 9e-04
## Sex:Race Men White- Men AfrAm 2.257 13.3863 0.007
## Sex:Race Women AfrAm- Men AfrAm -5.677 1.4676 0.243
## Sex:CES1 Women 0- Men 0 -6.763 2.0262 0.285
## Sex:CES1 Women 0- Women 1 -5.297 3.5846 0.701
## Sex:CES1 Women 0- Men 1 -11.018 -0.5032 0.032
## Sex:CES1 Men 0- Women 1 -3.289 6.3132 0.531
## Sex:CES1 Men 0- Men 1 -8.957 2.1728 0.227
## Sex:CES1 Women 1- Men 1 -10.506 0.6975 0.085
## Race:CES1 White 0- AfrAm 0 -1.447 7.3422 0.184
## Race:CES1 White 0- White 1 -11.625 0.6921 0.081
## Race:CES1 White 0- AfrAm 1 -0.425 8.7562 0.074
## Race:CES1 AfrAm 0- White 1 -13.856 -2.9720 0.003
## Race:CES1 AfrAm 0- AfrAm 1 -2.354 4.7903 0.497
## Race:CES1 White 1- AfrAm 1 4.031 15.2337 0.001
##
## Final model:
## lme4::lmer(formula = FluencyWord ~ Age + Sex + Race + CES1 +
## (1 | HNDid) + Age:Sex + Age:Race + Age:CES1 + Sex:Race +
## Sex:CES1 + Race:CES1 + Age:Sex:Race + Age:Sex:CES1 + Age:Race:CES1 +
## Sex:Race:CES1 + Age:Sex:Race:CES1, data = IPVandCognitionDataSet2,
## REML = reml, contrasts = l)
Re-run FluencyWord suggested final Model 2
(mm2 = lmer(FluencyWord ~ Age + Sex + Race + CES1 + (Age | HNDid) + (1 | subclass) +
Age:Sex + Age:Race + Age:CES1 + Sex:Race + Sex:CES1 + Race:CES1 + Age:Sex:Race +
Age:Sex:CES1 + Age:Race:CES1 + Sex:Race:CES1 + Age:Sex:Race:CES1, 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 + Sex + Race + CES1 + (Age | HNDid) + (1 | subclass) + Age:Sex + Age:Race + Age:CES1 + Sex:Race + Sex:CES1 + Race:CES1 + Age:Sex:Race + Age:Sex:CES1 + Age:Race:CES1 + Sex:Race:CES1 + Age:Sex:Race:CES1
## Data: IPVandCognitionDataSet2
## AIC BIC logLik deviance
## 762.5 822.1 -360.3 720.5
## Random effects:
## Groups Name Std.Dev. Corr
## HNDid (Intercept) 4.4640
## Age 0.0325 -1.00
## subclass (Intercept) 1.0434
## Residual 2.4648
## Number of obs: 126, groups: HNDid, 63; subclass, 21
## Fixed Effects:
## (Intercept) Age
## 20.202 -0.297
## SexMen RaceAfrAm
## 1.674 -3.494
## CES11 Age:SexMen
## 2.746 0.113
## Age:RaceAfrAm Age:CES11
## 0.147 0.223
## SexMen:RaceAfrAm SexMen:CES11
## 4.841 16.956
## RaceAfrAm:CES11 Age:SexMen:RaceAfrAm
## -3.235 0.229
## Age:SexMen:CES11 Age:RaceAfrAm:CES11
## 1.164 -0.384
## SexMen:RaceAfrAm:CES11 Age:SexMen:RaceAfrAm:CES11
## -23.320 -1.527
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: FluencyWord ~ Age + Sex + Race + CES1 + (Age | HNDid) + (1 | subclass) + Age:Sex + Age:Race + Age:CES1 + Sex:Race + Sex:CES1 + Race:CES1 + Age:Sex:Race + Age:Sex:CES1 + Age:Race:CES1 + Sex:Race:CES1 + Age:Sex:Race:CES1
## Data: IPVandCognitionDataSet2
##
## AIC BIC logLik deviance
## 762.5 822.1 -360.3 720.5
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## HNDid (Intercept) 19.92707 4.4640
## Age 0.00105 0.0325 -1.00
## subclass (Intercept) 1.08865 1.0434
## Residual 6.07533 2.4648
## Number of obs: 126, groups: HNDid, 63; subclass, 21
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 20.202 2.422 25.700 8.34 8.7e-09
## Age -0.297 0.205 113.100 -1.45 0.1505
## SexMen 1.674 3.951 25.500 0.42 0.6753
## RaceAfrAm -3.494 2.959 28.000 -1.18 0.2476
## CES11 2.746 4.321 51.900 0.64 0.5279
## Age:SexMen 0.113 0.306 101.400 0.37 0.7127
## Age:RaceAfrAm 0.147 0.254 114.000 0.58 0.5641
## Age:CES11 0.223 0.355 122.900 0.63 0.5313
## SexMen:RaceAfrAm 4.841 4.844 32.200 1.00 0.3250
## SexMen:CES11 16.956 7.101 46.500 2.39 0.0211
## RaceAfrAm:CES11 -3.235 4.952 43.400 -0.65 0.5170
## Age:SexMen:RaceAfrAm 0.229 0.383 114.400 0.60 0.5509
## Age:SexMen:CES11 1.164 0.692 101.000 1.68 0.0956
## Age:RaceAfrAm:CES11 -0.384 0.420 110.000 -0.91 0.3624
## SexMen:RaceAfrAm:CES11 -23.320 8.029 45.600 -2.90 0.0057
## Age:SexMen:RaceAfrAm:CES11 -1.527 0.766 105.800 -1.99 0.0487
##
## Correlation of Fixed Effects:
## (Intr) Age SexMen RcAfrA CES11 Ag:SxM Ag:RAA A:CES1
## Age 0.514
## SexMen -0.613 -0.315
## RaceAfrAm -0.809 -0.415 0.498
## CES11 -0.557 -0.291 0.341 0.456
## Age:SexMen -0.344 -0.669 0.544 0.276 0.195
## Age:RcAfrAm -0.408 -0.800 0.250 0.531 0.234 0.536
## Age:CES11 -0.295 -0.578 0.181 0.241 0.682 0.387 0.465
## SxMn:RcAfrA 0.497 0.252 -0.812 -0.613 -0.276 -0.440 -0.324 -0.146
## SexMn:CES11 0.340 0.174 -0.543 -0.279 -0.609 -0.293 -0.140 -0.413
## RcAfA:CES11 0.490 0.252 -0.300 -0.604 -0.875 -0.168 -0.322 -0.594
## Ag:SxMn:RAA 0.272 0.531 -0.432 -0.353 -0.154 -0.797 -0.663 -0.308
## Ag:SM:CES11 0.150 0.300 -0.234 -0.122 -0.349 -0.441 -0.241 -0.515
## A:RAA:CES11 0.251 0.492 -0.154 -0.328 -0.579 -0.329 -0.613 -0.848
## SM:RAA:CES1 -0.302 -0.150 0.483 0.376 0.538 0.258 0.196 0.362
## A:SM:RAA:CE -0.136 -0.271 0.212 0.179 0.315 0.399 0.338 0.466
## SxM:RAA SM:CES RAA:CE Ag:SM:RAA A:SM:C A:RAA: SM:RAA:
## Age
## SexMen
## RaceAfrAm
## CES11
## Age:SexMen
## Age:RcAfrAm
## Age:CES11
## SxMn:RcAfrA
## SexMn:CES11 0.441
## RcAfA:CES11 0.367 0.534
## Ag:SxMn:RAA 0.588 0.233 0.213
## Ag:SM:CES11 0.190 0.637 0.303 0.352
## A:RAA:CES11 0.199 0.350 0.590 0.406 0.437
## SM:RAA:CES1 -0.596 -0.884 -0.617 -0.348 -0.561 -0.362
## A:SM:RAA:CE -0.292 -0.574 -0.321 -0.501 -0.904 -0.550 0.613
plot(st)
plot(mm2)
Clock Total Regression Model 1
load("~/Desktop/Megan/Research/IPV and Cognition Paper/IPV R Output/IPVandCognitionDataSet2.rda")
library(lme4)
library(lmerTest)
(mm1 = lmer(ClockTotal ~ (Age + IPVstatus + Sex + Race)^4 + (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)^4 + (Age | HNDid) + (1 | subclass)
## Data: IPVandCognitionDataSet2
## AIC BIC logLik deviance
## 405.8 465.3 -181.9 363.8
## Random effects:
## Groups Name Std.Dev. Corr
## HNDid (Intercept) 0.400
## Age 0.057 0.59
## subclass (Intercept) 0.218
## Residual 0.880
## Number of obs: 126, groups: HNDid, 63; subclass, 21
## Fixed Effects:
## (Intercept) Age
## 9.35798 0.02102
## IPVstatus1 SexMen
## -0.48057 -0.82971
## RaceAfrAm Age:IPVstatus1
## -0.88554 -0.05239
## Age:SexMen Age:RaceAfrAm
## -0.06757 -0.02295
## IPVstatus1:SexMen IPVstatus1:RaceAfrAm
## 1.73382 0.61140
## SexMen:RaceAfrAm Age:IPVstatus1:SexMen
## 1.56166 0.15811
## Age:IPVstatus1:RaceAfrAm Age:SexMen:RaceAfrAm
## -0.00684 0.08456
## IPVstatus1:SexMen:RaceAfrAm Age:IPVstatus1:SexMen:RaceAfrAm
## -2.46362 -0.11757
(st = step(mm1))
## 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: no non-missing arguments to max; returning -Inf
## Warning: no non-missing arguments to max; returning -Inf
##
## Random effects:
## Chi.sq Chi.DF elim.num p.value
## (1 | subclass) 0.00 1 1 1.0000
## (Age | HNDid) 0.11 1 2 0.7384
## (1 | HNDid) 0.78 1 3 0.3786
## (Age + 0 | HNDid) 9.19 1 kept 0.0024
##
## Fixed effects:
## Sum Sq Mean Sq NumDF DenDF F.value elim.num Pr(>F)
## Age:IPVstatus:Sex:Race 0.2722 0.2722 1 79.58 0.2916 1 0.5907
## Age:Sex:Race 0.0000 0.0000 1 80.63 0.0366 2 0.8488
## Age:IPVstatus:Race 0.2994 0.2994 1 71.90 0.2320 3 0.6315
## Age:Race 0.9411 0.9411 1 69.37 0.2028 4 0.6539
## Age:IPVstatus:Sex 0.7395 0.7395 1 83.15 1.3169 5 0.2544
## Age:Sex 0.0012 0.0012 1 81.27 0.0431 6 0.8360
## Age:IPVstatus 0.0315 0.0315 1 79.34 0.0630 7 0.8024
## Age 0.0025 0.0025 1 74.66 0.2146 8 0.6445
## IPVstatus:Sex:Race 2.7757 2.7757 1 104.69 3.3260 9 0.0710
## IPVstatus:Race 0.0000 0.0000 1 104.63 0.0005 10 0.9821
## Sex:Race 0.4528 0.4528 1 104.87 0.4871 11 0.4867
## IPVstatus:Sex 1.1399 1.1399 1 113.24 1.2338 12 0.2690
## IPVstatus 0.3123 0.3123 1 114.48 0.0963 13 0.7569
## Sex 0.8179 0.8179 1 111.70 1.3379 14 0.2499
## Race 3.4895 3.4895 1 106.16 3.4704 15 0.0652
##
## Least squares means:
## Estimate Standard Error DF t-value Lower CI Upper CI p-value
##
## Differences of LSMEANS:
## Estimate Standard Error DF t-value Lower CI Upper CI p-value
##
## Final model:
## lme4::lmer(formula = ClockTotal ~ (Age + 0 | HNDid), data = IPVandCognitionDataSet2,
## REML = reml, contrasts = l)
Will not allow me to run Clock Total suggested final Model 1
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 + 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: ClockTotal ~ (Age + IPVstatus + Sex + Race + CES1)^5 + (Age | HNDid) + (1 | subclass)
## Data: IPVandCognitionDataSet2
## AIC BIC logLik deviance
## 415.4 520.4 -170.7 341.4
## Random effects:
## Groups Name Std.Dev. Corr
## HNDid (Intercept) 3.59e-01
## Age 2.13e-02 -1.00
## subclass (Intercept) 3.56e-06
## Residual 8.06e-01
## Number of obs: 126, groups: HNDid, 63; subclass, 21
## Fixed Effects:
## (Intercept)
## 9.41642
## Age
## 0.03486
## IPVstatus1
## -5.41642
## SexMen
## 0.60028
## RaceAfrAm
## -1.21740
## CES11
## 0.58358
## Age:IPVstatus1
## -0.53486
## Age:SexMen
## 0.00763
## Age:RaceAfrAm
## -0.04693
## Age:CES11
## -0.03486
## IPVstatus1:SexMen
## 5.02035
## IPVstatus1:RaceAfrAm
## 5.35593
## IPVstatus1:CES11
## 4.93851
## SexMen:RaceAfrAm
## 0.39368
## SexMen:CES11
## -1.20028
## RaceAfrAm:CES11
## 0.17369
## Age:IPVstatus1:SexMen
## 0.55548
## Age:IPVstatus1:RaceAfrAm
## 0.42436
## Age:IPVstatus1:CES11
## 0.55313
## Age:SexMen:RaceAfrAm
## 0.04527
## Age:SexMen:CES11
## 0.39237
## Age:RaceAfrAm:CES11
## 0.08477
## IPVstatus1:SexMen:RaceAfrAm
## -5.15284
## IPVstatus1:SexMen:CES11
## -1.60910
## IPVstatus1:RaceAfrAm:CES11
## -4.95051
## SexMen:RaceAfrAm:CES11
## 0.12016
## Age:IPVstatus1:SexMen:RaceAfrAm
## -0.48581
## Age:IPVstatus1:SexMen:CES11
## -0.64042
## Age:IPVstatus1:RaceAfrAm:CES11
## -0.46341
## Age:SexMen:RaceAfrAm:CES11
## -0.53494
## IPVstatus1:SexMen:RaceAfrAm:CES11
## 1.61897
## Age:IPVstatus1:SexMen:RaceAfrAm:CES11
## 0.65588
(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: no non-missing arguments to max; returning -Inf
## Warning: no non-missing arguments to max; returning -Inf
##
## Random effects:
## Chi.sq Chi.DF elim.num p.value
## (1 | subclass) 0.00 1 1 1.0000
## (Age | HNDid) 0.18 1 2 0.6719
## (1 | HNDid) 0.72 1 3 0.3956
## (Age + 0 | HNDid) 9.78 1 kept 0.0018
##
## Fixed effects:
## Sum Sq Mean Sq NumDF DenDF F.value elim.num
## Age:IPVstatus:Sex:Race:CES1 0.5866 0.5866 1 78.27 0.6957 1
## Age:IPVstatus:Sex:Race 1.5806 1.5806 1 82.17 0.3955 2
## Age:IPVstatus:Race:CES1 0.5322 0.5322 1 88.89 0.6771 3
## Age:Sex:Race:CES1 0.5575 0.5575 1 88.95 0.8544 4
## Age:Sex:Race 0.0990 0.0990 1 97.62 0.5314 5
## Age:Race:CES1 0.5093 0.5093 1 95.28 1.3878 6
## IPVstatus:Sex:Race:CES1 0.8670 0.8670 1 37.72 0.8784 7
## Age:IPVstatus:Race 0.0182 0.0182 1 74.09 0.5649 8
## Age:IPVstatus:Sex:CES1 0.0055 0.0055 1 67.78 0.6975 9
## IPVstatus:Sex:CES1 0.0721 0.0721 1 36.72 0.3726 10
## Age:Race 1.4281 1.4281 1 61.10 1.1355 11
## Sex:Race:CES1 2.6549 2.6549 1 53.96 1.3240 12
## Age:IPVstatus:CES1 0.9043 0.9043 1 72.35 2.0257 13
## Age:Sex:CES1 2.1034 2.1034 1 62.58 1.4008 14
## Age:CES1 0.2287 0.2287 1 57.00 0.0565 15
## Age:IPVstatus:Sex 0.6362 0.6362 1 71.18 1.2393 16
## Age:IPVstatus 0.0170 0.0170 1 81.04 0.0005 17
## Age:Sex 0.0129 0.0129 1 71.98 0.3729 18
## Age 0.0076 0.0076 1 70.30 0.3555 19
## Sex:CES1 1.5555 1.5555 1 80.77 1.4728 20
## IPVstatus:Sex:Race 2.1308 2.1308 1 74.75 2.4266 21
## Sex:Race 0.8845 0.8845 1 70.60 0.1410 22
## IPVstatus:Sex 0.8511 0.8511 1 99.49 0.4861 23
## Sex 0.8085 0.8085 1 97.50 3.1651 24
## IPVstatus:Race:CES1 3.0983 3.0983 1 99.68 3.5256 25
## IPVstatus:Race 0.0044 0.0044 1 98.45 0.0401 26
## Race:CES1 0.2397 0.2397 1 104.19 0.1656 27
## IPVstatus:CES1 0.2126 0.2126 1 102.39 1.1486 28
## IPVstatus 0.2545 0.2545 1 107.77 0.0040 29
## CES1 0.2208 0.2208 1 110.72 0.6150 30
## Race 3.6428 3.6428 1 106.16 3.4704 31
## Pr(>F)
## Age:IPVstatus:Sex:Race:CES1 0.4068
## Age:IPVstatus:Sex:Race 0.5312
## Age:IPVstatus:Race:CES1 0.4128
## Age:Sex:Race:CES1 0.3578
## Age:Sex:Race 0.4678
## Age:Race:CES1 0.2417
## IPVstatus:Sex:Race:CES1 0.3546
## Age:IPVstatus:Race 0.4547
## Age:IPVstatus:Sex:CES1 0.4066
## IPVstatus:Sex:CES1 0.5453
## Age:Race 0.2908
## Sex:Race:CES1 0.2549
## Age:IPVstatus:CES1 0.1590
## Age:Sex:CES1 0.2411
## Age:CES1 0.8130
## Age:IPVstatus:Sex 0.2694
## Age:IPVstatus 0.9826
## Age:Sex 0.5434
## Age 0.5529
## Sex:CES1 0.2284
## IPVstatus:Sex:Race 0.1235
## Sex:Race 0.7084
## IPVstatus:Sex 0.4873
## Sex 0.0783
## IPVstatus:Race:CES1 0.0634
## IPVstatus:Race 0.8418
## Race:CES1 0.6849
## IPVstatus:CES1 0.2864
## IPVstatus 0.9498
## CES1 0.4346
## Race 0.0652
##
## Least squares means:
## Estimate Standard Error DF t-value Lower CI Upper CI p-value
##
## Differences of LSMEANS:
## Estimate Standard Error DF t-value Lower CI Upper CI p-value
##
## Final model:
## lme4::lmer(formula = ClockTotal ~ (Age + 0 | HNDid), data = IPVandCognitionDataSet2,
## REML = reml, contrasts = l)
Will not allow me to run Clock Total suggested final Model 2