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
## 962.6 1022.1 -460.3 920.6
## Random effects:
## Groups Name Std.Dev. Corr
## HNDid (Intercept) 7.04
## Age 0.20 1.00
## subclass (Intercept) 1.85
## Residual 7.63
## Number of obs: 126, groups: HNDid, 63; subclass, 21
## Fixed Effects:
## (Intercept) Age
## 29.0401 0.3065
## IPVstatus1 SexMen
## -0.0520 -4.7692
## RaceAfrAm Age:IPVstatus1
## 15.0198 -0.0764
## Age:SexMen Age:RaceAfrAm
## -0.4153 0.7334
## IPVstatus1:SexMen IPVstatus1:RaceAfrAm
## 17.5731 1.5937
## SexMen:RaceAfrAm Age:IPVstatus1:SexMen
## -2.5314 0.9969
## Age:IPVstatus1:RaceAfrAm Age:SexMen:RaceAfrAm
## 0.6192 0.0895
## IPVstatus1:SexMen:RaceAfrAm Age:IPVstatus1:SexMen:RaceAfrAm
## -9.8200 -1.0854
## Warning: number of observations <= rank(Z); variance-covariance matrix will be unidentifiable
## Warning:
## model has been refitted with REML=TRUE
##
## Warning: number of observations <= rank(Z); variance-covariance matrix will be unidentifiable
## Warning: number of observations <= rank(Z); variance-covariance matrix will be unidentifiable
## Warning: number of observations <= rank(Z); variance-covariance matrix will be unidentifiable
## Warning: number of observations <= rank(Z); variance-covariance matrix will be unidentifiable
## Warning: number of observations <= rank(Z); variance-covariance matrix will be unidentifiable
##
## Random effects:
## Chi.sq Chi.DF elim.num p.value
## (1 | subclass) 0.11 1 1 0.7382
## (Age | HNDid) 0.91 1 2 0.3398
## (Age + 0 | HNDid) 0.00 1 3 1.0000
## (1 | HNDid) 13.54 1 kept 0.0002
##
## Fixed effects:
## Sum Sq Mean Sq NumDF DenDF F.value elim.num
## Age:IPVstatus:Sex:Race 32.410 32.410 1 97.30 0.5255 1
## IPVstatus:Sex:Race 1.375 1.375 1 52.92 0.0224 2
## Age:IPVstatus:Race 1.623 1.623 1 99.08 0.0320 3
## Age:Sex:Race 2.552 2.552 1 99.49 0.0331 4
## Age:IPVstatus:Sex 13.846 13.846 1 95.84 0.2420 5
## Age:Sex 46.681 46.681 1 102.62 0.2731 6
## IPVstatus:Race 30.428 30.428 1 54.17 0.5885 7
## Sex:Race 35.710 35.710 1 56.47 0.5556 8
## Age:IPVstatus 157.114 157.114 1 96.15 2.8413 9
## Age 1855.248 1855.248 1 102.33 16.1443 kept
## IPVstatus 97.756 97.756 1 56.67 2.0191 kept
## Sex 14.223 14.223 1 57.11 0.0285 kept
## Race 433.600 433.600 1 81.23 10.8756 kept
## Age:Race 229.423 229.423 1 103.84 4.6166 kept
## IPVstatus:Sex 233.415 233.415 1 57.41 4.8137 kept
## Pr(>F)
## Age:IPVstatus:Sex:Race 0.4702
## IPVstatus:Sex:Race 0.8815
## Age:IPVstatus:Race 0.8584
## Age:Sex:Race 0.8559
## Age:IPVstatus:Sex 0.6239
## Age:Sex 0.6024
## IPVstatus:Race 0.4463
## Sex:Race 0.4591
## Age:IPVstatus 0.0951
## Age 0.0001
## IPVstatus 0.1608
## Sex 0.8664
## Race 0.0014
## Age:Race 0.0340
## IPVstatus:Sex 0.0323
##
## Least squares means:
## IPVstatus Sex Race Estimate Standard Error DF
## IPVstatus 0 1.0 NA NA 30.84 1.42 57.2
## IPVstatus 1 2.0 NA NA 34.20 1.96 56.9
## Sex Women NA 2.0 NA 32.32 1.68 57.0
## Sex Men NA 1.0 NA 32.72 1.76 57.4
## Race White NA NA 2.0 29.38 1.92 57.8
## Race AfrAm NA NA 1.0 35.66 1.45 56.9
## IPVstatus:Sex 0 Women 1.0 2.0 NA 33.27 2.14 56.9
## IPVstatus:Sex 1 Women 2.0 2.0 NA 31.36 2.55 57.1
## IPVstatus:Sex 0 Men 1.0 1.0 NA 28.40 1.85 58.8
## IPVstatus:Sex 1 Men 2.0 1.0 NA 37.04 2.95 56.7
## t-value Lower CI Upper CI p-value
## IPVstatus 0 21.7 28.0 33.7 <2e-16
## IPVstatus 1 17.4 30.3 38.1 <2e-16
## Sex Women 19.2 29.0 35.7 <2e-16
## Sex Men 18.6 29.2 36.2 <2e-16
## Race White 15.3 25.5 33.2 <2e-16
## Race AfrAm 24.5 32.7 38.6 <2e-16
## IPVstatus:Sex 0 Women 15.6 29.0 37.6 <2e-16
## IPVstatus:Sex 1 Women 12.3 26.3 36.5 <2e-16
## IPVstatus:Sex 0 Men 15.3 24.7 32.1 <2e-16
## IPVstatus:Sex 1 Men 12.6 31.1 42.9 <2e-16
##
## Differences of LSMEANS:
## Estimate Standard Error DF t-value
## IPVstatus 0-1 -3.4 2.368 56.7 -1.42
## Sex Women-Men -0.4 2.385 57.1 -0.17
## Race White-AfrAm -6.3 2.343 57.6 -2.68
## IPVstatus:Sex 0 Women- 1 Women 1.9 3.297 57.0 0.58
## IPVstatus:Sex 0 Women- 0 Men 4.9 2.814 58.2 1.73
## IPVstatus:Sex 0 Women- 1 Men -3.8 3.589 56.8 -1.05
## IPVstatus:Sex 1 Women- 0 Men 3.0 3.116 57.0 0.95
## IPVstatus:Sex 1 Women- 1 Men -5.7 3.877 56.8 -1.47
## IPVstatus:Sex 0 Men- 1 Men -8.6 3.452 57.1 -2.50
## Lower CI Upper CI p-value
## IPVstatus 0-1 -8.108 1.38 0.16
## Sex Women-Men -5.179 4.37 0.87
## Race White-AfrAm -10.969 -1.59 0.01
## IPVstatus:Sex 0 Women- 1 Women -4.691 8.51 0.56
## IPVstatus:Sex 0 Women- 0 Men -0.758 10.51 0.09
## IPVstatus:Sex 0 Women- 1 Men -10.956 3.42 0.30
## IPVstatus:Sex 1 Women- 0 Men -3.278 9.20 0.35
## IPVstatus:Sex 1 Women- 1 Men -13.443 2.08 0.15
## IPVstatus:Sex 0 Men- 1 Men -15.554 -1.73 0.02
##
## Final model:
## lme4::lmer(formula = TrailsAtestSec ~ Age + IPVstatus + Sex +
## Race + (1 | HNDid) + Age:Race + IPVstatus:Sex, data = IPVandCognitionDataSet2,
## REML = reml, contrasts = l)
Re-run final Model 1
(mm1 = lmer(TrailsAtestSec ~ Age + IPVstatus + Sex + Race + (1 | HNDid) + Age:Race +
IPVstatus:Sex, data = IPVandCognitionDataSet2, REML = F))
## Linear mixed model fit by maximum likelihood ['merModLmerTest']
## Formula: TrailsAtestSec ~ Age + IPVstatus + Sex + Race + (1 | HNDid) + Age:Race + IPVstatus:Sex
## Data: IPVandCognitionDataSet2
## AIC BIC logLik deviance
## 945.2 970.7 -463.6 927.2
## Random effects:
## Groups Name Std.Dev.
## HNDid (Intercept) 6.20
## Residual 7.82
## Number of obs: 126, groups: HNDid, 63
## Fixed Effects:
## (Intercept) Age IPVstatus1
## 32.279 0.325 -1.905
## SexMen RaceAfrAm Age:RaceAfrAm
## -4.872 11.151 0.745
## IPVstatus1:SexMen
## 10.542
summary(mm1)
## Linear mixed model fit by maximum likelihood ['merModLmerTest']
## Formula: TrailsAtestSec ~ Age + IPVstatus + Sex + Race + (1 | HNDid) + Age:Race + IPVstatus:Sex
## Data: IPVandCognitionDataSet2
##
## AIC BIC logLik deviance
## 945.2 970.7 -463.6 927.2
##
## Random effects:
## Groups Name Variance Std.Dev.
## HNDid (Intercept) 38.4 6.20
## Residual 61.2 7.82
## Number of obs: 126, groups: HNDid, 63
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 32.279 3.215 80.500 10.04 8.9e-16
## Age 0.325 0.279 112.800 1.16 0.24725
## IPVstatus1 -1.905 3.126 63.000 -0.61 0.54433
## SexMen -4.872 2.669 64.200 -1.83 0.07256
## RaceAfrAm 11.151 3.250 88.200 3.43 0.00092
## Age:RaceAfrAm 0.745 0.337 110.200 2.21 0.02906
## IPVstatus1:SexMen 10.542 4.560 63.400 2.31 0.02405
##
## Correlation of Fixed Effects:
## (Intr) Age IPVst1 SexMen RcAfrA Ag:RAA
## Age 0.616
## IPVstatus1 -0.382 0.083
## SexMen -0.443 0.121 0.489
## RaceAfrAm -0.718 -0.668 -0.005 -0.059
## Age:RcAfrAm -0.473 -0.840 -0.096 -0.196 0.732
## IPVstts1:SM 0.250 -0.116 -0.697 -0.592 0.032 0.143
plot(st)
plot(mm1)
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
## 974.5 1079.4 -450.2 900.5
## Random effects:
## Groups Name Std.Dev. Corr
## HNDid (Intercept) 8.06e+00
## Age 5.30e-01 1.00
## subclass (Intercept) 4.37e-05
## Residual 7.30e+00
## Number of obs: 126, groups: HNDid, 63; subclass, 21
## Fixed Effects:
## (Intercept)
## 27.0101
## Age
## -0.3701
## IPVstatus1
## 17.9899
## SexMen
## -2.1002
## RaceAfrAm
## 17.2220
## CES11
## -0.0101
## Age:IPVstatus1
## 2.3701
## Age:SexMen
## 0.5344
## Age:RaceAfrAm
## 1.6667
## Age:CES11
## 1.0368
## IPVstatus1:SexMen
## 0.8834
## IPVstatus1:RaceAfrAm
## -12.1315
## IPVstatus1:CES11
## -19.3984
## SexMen:RaceAfrAm
## -4.8642
## SexMen:CES11
## -1.2683
## RaceAfrAm:CES11
## -0.0197
## Age:IPVstatus1:SexMen
## -1.6788
## Age:IPVstatus1:RaceAfrAm
## -1.8567
## Age:IPVstatus1:CES11
## -3.1796
## Age:SexMen:RaceAfrAm
## -1.1907
## Age:SexMen:CES11
## -1.9039
## Age:RaceAfrAm:CES11
## -1.3374
## IPVstatus1:SexMen:RaceAfrAm
## -6.0095
## IPVstatus1:SexMen:CES11
## 15.8937
## IPVstatus1:RaceAfrAm:CES11
## 12.1511
## SexMen:RaceAfrAm:CES11
## -0.4474
## Age:IPVstatus1:SexMen:RaceAfrAm
## 0.5251
## Age:IPVstatus1:SexMen:CES11
## 4.1912
## Age:IPVstatus1:RaceAfrAm:CES11
## 3.4548
## Age:SexMen:RaceAfrAm:CES11
## 2.5378
## IPVstatus1:SexMen:RaceAfrAm:CES11
## 2.9370
## Age:IPVstatus1:SexMen:RaceAfrAm:CES11
## -2.3917
(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
## 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) 0.0 1 1 1.0000
## (Age | HNDid) 6.5 1 kept 0.0108
##
## Fixed effects:
## Sum Sq Mean Sq NumDF DenDF F.value elim.num
## Age:IPVstatus:Sex:Race:CES1 7.797 7.797 1 61.93 0.1247 1
## Age:IPVstatus:Sex:Race 20.724 20.724 1 61.20 0.0306 2
## Age:Sex:Race:CES1 33.368 33.368 1 57.78 0.5596 3
## Age:Sex:Race 25.615 25.615 1 46.82 0.4293 4
## Age:IPVstatus:Race:CES1 23.468 23.468 1 59.61 0.6472 5
## Age:IPVstatus:Race 13.590 13.590 1 58.43 0.0115 6
## Age:Race:CES1 32.164 32.164 1 46.82 0.3185 7
## IPVstatus:Sex:Race:CES1 61.024 61.024 1 25.41 0.2965 8
## IPVstatus:Sex:Race 13.409 13.409 1 25.46 0.6953 9
## Age:IPVstatus:Sex:CES1 9.700 9.700 1 27.48 1.4031 10
## Age:IPVstatus:CES1 1.110 1.110 1 34.17 0.0170 11
## Age:Sex:CES1 30.907 30.907 1 24.92 0.5388 12
## Age:CES1 4.364 4.364 1 32.09 0.1780 13
## Age:IPVstatus:Sex 11.693 11.693 1 32.77 1.0107 14
## Age:Sex 52.725 52.725 1 37.76 0.0001 15
## IPVstatus:Race:CES1 71.931 71.931 1 28.61 2.4394 16
## IPVstatus:Race 27.163 27.163 1 32.06 0.0250 17
## IPVstatus:Sex:CES1 95.565 95.565 1 40.65 2.0288 18
## Sex:Race:CES1 307.587 307.587 1 36.25 1.7969 19
## Sex:Race 3.314 3.314 1 40.06 0.0753 20
## Sex:CES1 11.055 11.055 1 48.88 0.9094 21
## Race:CES1 99.815 99.815 1 34.04 1.1367 22
## IPVstatus:CES1 173.059 173.059 1 47.90 2.6503 23
## CES1 9.410 9.410 1 46.38 0.6675 24
## Age:IPVstatus 52.712 52.712 1 15.52 1.8633 25
## IPVstatus:Sex 151.129 151.129 1 52.37 3.3747 26
## Sex 8.521 8.521 1 50.26 0.2922 27
## IPVstatus 44.687 44.687 1 44.73 0.6145 28
## Age 1031.579 1031.579 1 49.49 7.8819 kept
## Race 265.864 265.864 1 39.09 8.4388 kept
## Age:Race 249.551 249.551 1 49.49 4.7818 kept
## Pr(>F)
## Age:IPVstatus:Sex:Race:CES1 0.7252
## Age:IPVstatus:Sex:Race 0.8616
## Age:Sex:Race:CES1 0.4575
## Age:Sex:Race 0.5155
## Age:IPVstatus:Race:CES1 0.4243
## Age:IPVstatus:Race 0.9149
## Age:Race:CES1 0.5752
## IPVstatus:Sex:Race:CES1 0.5908
## IPVstatus:Sex:Race 0.4121
## Age:IPVstatus:Sex:CES1 0.2463
## Age:IPVstatus:CES1 0.8971
## Age:Sex:CES1 0.4698
## Age:CES1 0.6759
## Age:IPVstatus:Sex 0.3221
## Age:Sex 0.9907
## IPVstatus:Race:CES1 0.1293
## IPVstatus:Race 0.8755
## IPVstatus:Sex:CES1 0.1620
## Sex:Race:CES1 0.1884
## Sex:Race 0.7852
## Sex:CES1 0.3450
## Race:CES1 0.2939
## IPVstatus:CES1 0.1101
## CES1 0.4181
## Age:IPVstatus 0.1917
## IPVstatus:Sex 0.0719
## Sex 0.5912
## IPVstatus 0.4372
## Age 0.0071
## Race 0.0060
## Age:Race 0.0335
##
## Least squares means:
## Race Estimate Standard Error DF t-value Lower CI Upper CI
## Race White 2.0 27.04 1.93 55.8 14.02 23.2 30.9
## Race AfrAm 1.0 33.91 1.42 55.7 23.88 31.1 36.8
## 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 -6.9 2.39 56.0 -2.87 -11.7 -2.07
## p-value
## Race White-AfrAm 0.006
##
## Final model:
## lme4::lmer(formula = TrailsAtestSec ~ Age + Race + (Age | HNDid) +
## Age:Race, data = IPVandCognitionDataSet2, REML = reml, contrasts = l)
Re-run suggested final Model 2
(mm2 = lmer(TrailsAtestSec ~ Age + IPVstatus + Sex + Race + (Age | HNDid) +
Age:Race, 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 + (Age | HNDid) + Age:Race
## Data: IPVandCognitionDataSet2
## AIC BIC logLik deviance
## 947.8 976.2 -463.9 927.8
## Random effects:
## Groups Name Std.Dev. Corr
## HNDid (Intercept) 9.989
## Age 0.764 0.92
## Residual 7.446
## Number of obs: 126, groups: HNDid, 63
## Fixed Effects:
## (Intercept) Age IPVstatus1 SexMen RaceAfrAm
## 28.159 0.136 1.496 -1.153 12.727
## Age:RaceAfrAm
## 0.899
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: TrailsAtestSec ~ Age + IPVstatus + Sex + Race + (Age | HNDid) + Age:Race
## Data: IPVandCognitionDataSet2
##
## AIC BIC logLik deviance
## 947.8 976.2 -463.9 927.8
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## HNDid (Intercept) 99.784 9.989
## Age 0.584 0.764 0.92
## Residual 55.444 7.446
## Number of obs: 126, groups: HNDid, 63
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 28.159 3.756 50.000 7.50 1e-09
## Age 0.136 0.324 57.400 0.42 0.6756
## IPVstatus1 1.496 2.142 49.700 0.70 0.4880
## SexMen -1.153 2.087 54.000 -0.55 0.5831
## RaceAfrAm 12.727 4.167 37.100 3.05 0.0042
## Age:RaceAfrAm 0.899 0.392 49.600 2.29 0.0262
##
## Correlation of Fixed Effects:
## (Intr) Age IPVst1 SexMen RcAfrA
## Age 0.814
## IPVstatus1 -0.237 0.022
## SexMen -0.316 0.056 0.193
## RaceAfrAm -0.759 -0.755 -0.019 -0.070
## Age:RcAfrAm -0.638 -0.832 -0.034 -0.155 0.870
plot(st)
plot(mm2)
