## 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
Trails B-A/A Regression Model 1
(mm1 = lmer(IPVandCognitionDataSet2$"TrailsB-A/A" ~ (Age + IPVstatus + Sex +
Race)^4 + (Age | HNDid) + (1 | subclass), data = IPVandCognitionDataSet2,
REML = F))
## Warning: number of observations <= rank(Z); variance-covariance matrix
## will be unidentifiable
## Linear mixed model fit by maximum likelihood ['merModLmerTest']
## Formula: IPVandCognitionDataSet2$"TrailsB-A/A" ~ (Age + IPVstatus + Sex + Race)^4 + (Age | HNDid) + (1 | subclass)
## Data: IPVandCognitionDataSet2
## AIC BIC logLik deviance
## 685.2 744.8 -321.6 643.2
## Random effects:
## Groups Name Std.Dev. Corr
## HNDid (Intercept) 3.8358
## Age 0.1080 1.00
## subclass (Intercept) 0.0001
## Residual 2.0046
## Number of obs: 126, groups: HNDid, 63; subclass, 21
## Fixed Effects:
## (Intercept) Age
## 1.2354 -0.0492
## IPVstatus1 SexMen
## 0.3820 0.2026
## RaceAfrAm Age:IPVstatus1
## 3.2317 0.0694
## Age:SexMen Age:RaceAfrAm
## 0.0727 0.0509
## IPVstatus1:SexMen IPVstatus1:RaceAfrAm
## 4.7251 -4.2402
## SexMen:RaceAfrAm Age:IPVstatus1:SexMen
## -1.3036 0.2379
## Age:IPVstatus1:RaceAfrAm Age:SexMen:RaceAfrAm
## -0.1458 0.0470
## IPVstatus1:SexMen:RaceAfrAm Age:IPVstatus1:SexMen:RaceAfrAm
## 2.2568 -0.5267
(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 (1 | subclass) was eliminated because of standard deviation being equal to 0
## 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) 32.18 1 kept < 1e-07
##
## Fixed effects:
## Sum Sq Mean Sq NumDF DenDF F.value elim.num
## Age:IPVstatus:Sex:Race 7.6061 7.6061 1 109.32 1.5389 1
## Age:IPVstatus:Sex 0.5819 0.5819 1 110.45 0.2848 2
## Age:Sex:Race 2.3297 2.3297 1 111.87 0.3411 3
## Age:Sex 2.7335 2.7335 1 112.99 0.5717 4
## IPVstatus:Sex:Race 6.8026 6.8026 1 51.83 1.2286 5
## Sex:Race 0.1229 0.1229 1 53.42 0.1092 6
## Age:IPVstatus:Race 14.6821 14.6821 1 115.91 2.5723 7
## Age:IPVstatus 0.0465 0.0465 1 116.50 0.1707 8
## IPVstatus:Race 0.6457 0.6457 1 53.26 0.2375 9
## Age:Race 7.1186 7.1186 1 118.74 0.5927 10
## Age 0.2662 0.2662 1 119.91 0.0121 11
## Race 22.8557 22.8557 1 58.00 3.0459 12
## IPVstatus 4.0744 4.0744 1 59.00 1.3190 kept
## Sex 6.5221 6.5221 1 59.00 4.4171 kept
## IPVstatus:Sex 46.0172 46.0172 1 59.00 10.0837 kept
## Pr(>F)
## Age:IPVstatus:Sex:Race 0.2174
## Age:IPVstatus:Sex 0.5947
## Age:Sex:Race 0.5604
## Age:Sex 0.4512
## IPVstatus:Sex:Race 0.2728
## Sex:Race 0.7424
## Age:IPVstatus:Race 0.1115
## Age:IPVstatus 0.6803
## IPVstatus:Race 0.6280
## Age:Race 0.4429
## Age 0.9125
## Race 0.0862
## IPVstatus 0.2554
## Sex 0.0399
## IPVstatus:Sex 0.0024
##
## Least squares means:
## IPVstatus Sex Estimate Standard Error DF t-value
## IPVstatus 0 1 NA 2.780 0.587 59 4.74
## IPVstatus 1 2 NA 3.948 0.830 59 4.76
## Sex Women NA 2 2.296 0.702 59 3.27
## Sex Men NA 1 4.433 0.736 59 6.02
## IPVstatus:Sex 0 Women 1 2 3.326 0.887 59 3.75
## IPVstatus:Sex 1 Women 2 2 1.266 1.087 59 1.16
## IPVstatus:Sex 0 Men 1 1 2.235 0.768 59 2.91
## IPVstatus:Sex 1 Men 2 1 6.630 1.255 59 5.28
## Lower CI Upper CI p-value
## IPVstatus 0 1.606 3.95 <2e-16
## IPVstatus 1 2.287 5.61 <2e-16
## Sex Women 0.892 3.70 0.0018
## Sex Men 2.960 5.90 <2e-16
## IPVstatus:Sex 0 Women 1.551 5.10 0.0004
## IPVstatus:Sex 1 Women -0.909 3.44 0.2489
## IPVstatus:Sex 0 Men 0.697 3.77 0.0051
## IPVstatus:Sex 1 Men 4.119 9.14 <2e-16
##
## Differences of LSMEANS:
## Estimate Standard Error DF t-value
## IPVstatus 0-1 -1.2 1.017 59.0 -1.15
## Sex Women-Men -2.1 1.017 59.0 -2.10
## IPVstatus:Sex 0 Women- 1 Women 2.1 1.403 59.0 1.47
## IPVstatus:Sex 0 Women- 0 Men 1.1 1.174 59.0 0.93
## IPVstatus:Sex 0 Women- 1 Men -3.3 1.537 59.0 -2.15
## IPVstatus:Sex 1 Women- 0 Men -1.0 1.331 59.0 -0.73
## IPVstatus:Sex 1 Women- 1 Men -5.4 1.660 59.0 -3.23
## IPVstatus:Sex 0 Men- 1 Men -4.4 1.472 59.0 -2.99
## Lower CI Upper CI p-value
## IPVstatus 0-1 -3.202 0.867 0.255
## Sex Women-Men -4.171 -0.102 0.040
## IPVstatus:Sex 0 Women- 1 Women -0.747 4.868 0.147
## IPVstatus:Sex 0 Women- 0 Men -1.257 3.441 0.356
## IPVstatus:Sex 0 Women- 1 Men -6.380 -0.229 0.036
## IPVstatus:Sex 1 Women- 0 Men -3.632 1.694 0.469
## IPVstatus:Sex 1 Women- 1 Men -8.687 -2.043 0.002
## IPVstatus:Sex 0 Men- 1 Men -7.340 -1.451 0.004
##
## Final model:
## lme4::lmer(formula = IPVandCognitionDataSet2$"TrailsB-A/A" ~
## IPVstatus + Sex + (1 | HNDid) + IPVstatus:Sex, data = IPVandCognitionDataSet2,
## REML = reml, contrasts = l)
Re-run the suggested final Model 1
(mm1 = lmer(IPVandCognitionDataSet2$"TrailsB-A/A" ~ 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: IPVandCognitionDataSet2$"TrailsB-A/A" ~ IPVstatus + Sex + (Age | HNDid) + (1 | subclass) + IPVstatus:Sex
## Data: IPVandCognitionDataSet2
## AIC BIC logLik deviance
## 674.2 699.8 -328.1 656.2
## Random effects:
## Groups Name Std.Dev. Corr
## HNDid (Intercept) 3.9720
## Age 0.0744 1.00
## subclass (Intercept) 0.0000
## Residual 2.0177
## Number of obs: 126, groups: HNDid, 63; subclass, 21
## Fixed Effects:
## (Intercept) IPVstatus1 SexMen
## 3.58 -2.27 -1.54
## IPVstatus1:SexMen
## 6.98
summary(mm1)
## Warning: number of observations <= rank(Z); variance-covariance matrix will be unidentifiable
## Warning: number of observations <= rank(Z); variance-covariance matrix will be unidentifiable
## Linear mixed model fit by maximum likelihood ['merModLmerTest']
## Formula: IPVandCognitionDataSet2$"TrailsB-A/A" ~ IPVstatus + Sex + (Age | HNDid) + (1 | subclass) + IPVstatus:Sex
## Data: IPVandCognitionDataSet2
##
## AIC BIC logLik deviance
## 674.2 699.8 -328.1 656.2
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## HNDid (Intercept) 15.77679 3.9720
## Age 0.00554 0.0744 1.00
## subclass (Intercept) 0.00000 0.0000
## Residual 4.07102 2.0177
## Number of obs: 126, groups: HNDid, 63; subclass, 21
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 3.581 0.852 60.800 4.21 8.7e-05
## IPVstatus1 -2.269 1.354 63.700 -1.68 0.0986
## SexMen -1.545 1.144 64.600 -1.35 0.1816
## IPVstatus1:SexMen 6.976 1.981 64.500 3.52 0.0008
##
## Correlation of Fixed Effects:
## (Intr) IPVst1 SexMen
## IPVstatus1 -0.629
## SexMen -0.744 0.468
## IPVstts1:SM 0.430 -0.683 -0.577
plot(st)
plot(mm1)
Trails B-A/A Regression Model 2 (with CES)
(mm2 = lmer(IPVandCognitionDataSet2$"TrailsB-A/A" ~ (Age + IPVstatus + Sex +
Race + CES1)^5 + (Age | HNDid) + (1 | subclass), data = IPVandCognitionDataSet2,
REML = F))
## Warning: number of observations <= rank(Z); variance-covariance matrix
## will be unidentifiable
## Linear mixed model fit by maximum likelihood ['merModLmerTest']
## Formula: IPVandCognitionDataSet2$"TrailsB-A/A" ~ (Age + IPVstatus + Sex + Race + CES1)^5 + (Age | HNDid) + (1 | subclass)
## Data: IPVandCognitionDataSet2
## AIC BIC logLik deviance
## 707.5 812.4 -316.7 633.5
## Random effects:
## Groups Name Std.Dev. Corr
## HNDid (Intercept) 5.51e+00
## Age 2.43e-01 1.00
## subclass (Intercept) 3.81e-07
## Residual 1.61e+00
## Number of obs: 126, groups: HNDid, 63; subclass, 21
## Fixed Effects:
## (Intercept)
## 1.5535
## Age
## 0.0017
## IPVstatus1
## -0.2765
## SexMen
## -0.2353
## RaceAfrAm
## 9.1593
## CES11
## -0.4135
## Age:IPVstatus1
## -0.0262
## Age:SexMen
## 0.0164
## Age:RaceAfrAm
## 0.4349
## Age:CES11
## -0.0708
## IPVstatus1:SexMen
## 6.3176
## IPVstatus1:RaceAfrAm
## -10.8837
## IPVstatus1:CES11
## 1.1579
## SexMen:RaceAfrAm
## -7.4982
## SexMen:CES11
## 0.2199
## RaceAfrAm:CES11
## -10.9525
## Age:IPVstatus1:SexMen
## 0.3269
## Age:IPVstatus1:RaceAfrAm
## -0.5835
## Age:IPVstatus1:CES11
## 0.1528
## Age:SexMen:RaceAfrAm
## -0.3957
## Age:SexMen:CES11
## 0.0340
## Age:RaceAfrAm:CES11
## -0.6306
## IPVstatus1:SexMen:RaceAfrAm
## 14.3631
## IPVstatus1:SexMen:CES11
## -5.7375
## IPVstatus1:RaceAfrAm:CES11
## 11.7321
## SexMen:RaceAfrAm:CES11
## 12.2423
## Age:IPVstatus1:SexMen:RaceAfrAm
## 0.2255
## Age:IPVstatus1:SexMen:CES11
## -0.2797
## Age:IPVstatus1:RaceAfrAm:CES11
## 0.7424
## Age:SexMen:RaceAfrAm:CES11
## 0.8445
## IPVstatus1:SexMen:RaceAfrAm:CES11
## -13.6014
## Age:IPVstatus1:SexMen:RaceAfrAm:CES11
## -0.8037
(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) 0.08 1 1 0.7793
## (1 | HNDid) 27.00 1 kept 0
##
## Fixed effects:
## Sum Sq Mean Sq NumDF DenDF F.value elim.num
## Age:IPVstatus:Sex:Race:CES1 0.2827 0.2827 1 53.52 0.0553 1
## Age:IPVstatus:Race:CES1 0.0687 0.0687 1 61.23 0.0450 2
## IPVstatus:Sex:Race:CES1 0.2828 0.2828 1 60.48 0.0681 3
## IPVstatus:Race:CES1 0.3367 0.3367 1 52.84 0.0812 4
## Age:Sex:Race:CES1 0.7430 0.7430 1 79.14 0.0794 5
## Age:Race:CES1 0.7460 0.7460 1 81.11 0.0214 6
## Sex:Race:CES1 0.3224 0.3224 1 42.09 0.0514 7
## Race:CES1 1.6171 1.6171 1 44.07 0.1430 8
## Age:IPVstatus:Sex:Race 4.3961 4.3961 1 100.61 0.2139 9
## Age:Sex:Race 2.6591 2.6591 1 101.46 0.0838 10
## Age:IPVstatus:Race 10.1705 10.1705 1 101.80 0.6406 11
## Age:Race 4.3602 4.3602 1 103.20 0.0492 12
## Age:IPVstatus:Sex:CES1 2.2309 2.2309 1 105.86 2.4681 13
## Age:Sex:CES1 3.3125 3.3125 1 106.66 0.0290 14
## Age:IPVstatus:Sex 0.3145 0.3145 1 107.96 0.1028 15
## Age:IPVstatus:CES1 0.2185 0.2185 1 108.60 0.1154 16
## Age:IPVstatus 0.0064 0.0064 1 109.89 0.0291 17
## Age:CES1 1.8053 1.8053 1 110.89 0.7205 18
## Age:Sex 2.2057 2.2057 1 111.92 1.2338 19
## Age 0.6052 0.6052 1 112.99 0.0500 20
## IPVstatus:Sex:Race 6.8717 6.8717 1 51.00 2.6251 21
## IPVstatus:Race 0.5951 0.5951 1 52.00 0.0017 22
## Sex:Race 0.3189 0.3189 1 53.00 0.1573 23
## IPVstatus:Sex:CES1 9.5512 9.5512 1 54.00 2.2465 24
## Sex:CES1 0.0532 0.0532 1 55.00 0.0004 25
## IPVstatus:CES1 0.0070 0.0070 1 56.00 0.0243 26
## CES1 2.5915 2.5915 1 57.00 0.8854 27
## Race 18.9616 18.9616 1 58.00 3.0459 28
## IPVstatus 3.4647 3.4647 1 59.00 1.3190 kept
## Sex 5.4117 5.4117 1 59.00 4.4171 kept
## IPVstatus:Sex 40.0130 40.0130 1 59.00 10.0837 kept
## Pr(>F)
## Age:IPVstatus:Sex:Race:CES1 0.8150
## Age:IPVstatus:Race:CES1 0.8327
## IPVstatus:Sex:Race:CES1 0.7950
## IPVstatus:Race:CES1 0.7768
## Age:Sex:Race:CES1 0.7788
## Age:Race:CES1 0.8839
## Sex:Race:CES1 0.8218
## Race:CES1 0.7071
## Age:IPVstatus:Sex:Race 0.6447
## Age:Sex:Race 0.7729
## Age:IPVstatus:Race 0.4253
## Age:Race 0.8249
## Age:IPVstatus:Sex:CES1 0.1192
## Age:Sex:CES1 0.8651
## Age:IPVstatus:Sex 0.7491
## Age:IPVstatus:CES1 0.7347
## Age:IPVstatus 0.8649
## Age:CES1 0.3978
## Age:Sex 0.2691
## Age 0.8235
## IPVstatus:Sex:Race 0.1114
## IPVstatus:Race 0.9674
## Sex:Race 0.6932
## IPVstatus:Sex:CES1 0.1397
## Sex:CES1 0.9833
## IPVstatus:CES1 0.8766
## CES1 0.3507
## Race 0.0862
## IPVstatus 0.2554
## Sex 0.0399
## IPVstatus:Sex 0.0024
##
## Least squares means:
## IPVstatus Sex Estimate Standard Error DF t-value
## IPVstatus 0 1 NA 2.780 0.587 59 4.74
## IPVstatus 1 2 NA 3.948 0.830 59 4.76
## Sex Women NA 2 2.296 0.702 59 3.27
## Sex Men NA 1 4.433 0.736 59 6.02
## IPVstatus:Sex 0 Women 1 2 3.326 0.887 59 3.75
## IPVstatus:Sex 1 Women 2 2 1.266 1.087 59 1.16
## IPVstatus:Sex 0 Men 1 1 2.235 0.768 59 2.91
## IPVstatus:Sex 1 Men 2 1 6.630 1.255 59 5.28
## Lower CI Upper CI p-value
## IPVstatus 0 1.606 3.95 <2e-16
## IPVstatus 1 2.287 5.61 <2e-16
## Sex Women 0.892 3.70 0.0018
## Sex Men 2.960 5.90 <2e-16
## IPVstatus:Sex 0 Women 1.551 5.10 0.0004
## IPVstatus:Sex 1 Women -0.909 3.44 0.2489
## IPVstatus:Sex 0 Men 0.697 3.77 0.0051
## IPVstatus:Sex 1 Men 4.119 9.14 <2e-16
##
## Differences of LSMEANS:
## Estimate Standard Error DF t-value
## IPVstatus 0-1 -1.2 1.017 59.0 -1.15
## Sex Women-Men -2.1 1.017 59.0 -2.10
## IPVstatus:Sex 0 Women- 1 Women 2.1 1.403 59.0 1.47
## IPVstatus:Sex 0 Women- 0 Men 1.1 1.174 59.0 0.93
## IPVstatus:Sex 0 Women- 1 Men -3.3 1.537 59.0 -2.15
## IPVstatus:Sex 1 Women- 0 Men -1.0 1.331 59.0 -0.73
## IPVstatus:Sex 1 Women- 1 Men -5.4 1.660 59.0 -3.23
## IPVstatus:Sex 0 Men- 1 Men -4.4 1.472 59.0 -2.99
## Lower CI Upper CI p-value
## IPVstatus 0-1 -3.202 0.867 0.255
## Sex Women-Men -4.171 -0.102 0.040
## IPVstatus:Sex 0 Women- 1 Women -0.747 4.868 0.147
## IPVstatus:Sex 0 Women- 0 Men -1.257 3.441 0.356
## IPVstatus:Sex 0 Women- 1 Men -6.380 -0.229 0.036
## IPVstatus:Sex 1 Women- 0 Men -3.632 1.694 0.469
## IPVstatus:Sex 1 Women- 1 Men -8.687 -2.043 0.002
## IPVstatus:Sex 0 Men- 1 Men -7.340 -1.451 0.004
##
## Final model:
## lme4::lmer(formula = IPVandCognitionDataSet2$"TrailsB-A/A" ~
## IPVstatus + Sex + (1 | HNDid) + IPVstatus:Sex, data = IPVandCognitionDataSet2,
## REML = reml, contrasts = l)
Re-run the suggested final Model 2
(mm2 = lmer(IPVandCognitionDataSet2$"TrailsB-A/A" ~ 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: IPVandCognitionDataSet2$"TrailsB-A/A" ~ IPVstatus + Sex + (Age | HNDid) + (1 | subclass) + IPVstatus:Sex
## Data: IPVandCognitionDataSet2
## AIC BIC logLik deviance
## 674.2 699.8 -328.1 656.2
## Random effects:
## Groups Name Std.Dev. Corr
## HNDid (Intercept) 3.9720
## Age 0.0744 1.00
## subclass (Intercept) 0.0000
## Residual 2.0177
## Number of obs: 126, groups: HNDid, 63; subclass, 21
## Fixed Effects:
## (Intercept) IPVstatus1 SexMen
## 3.58 -2.27 -1.54
## IPVstatus1:SexMen
## 6.98
summary(mm2)
## Warning: number of observations <= rank(Z); variance-covariance matrix will be unidentifiable
## Warning: number of observations <= rank(Z); variance-covariance matrix will be unidentifiable
## Linear mixed model fit by maximum likelihood ['merModLmerTest']
## Formula: IPVandCognitionDataSet2$"TrailsB-A/A" ~ IPVstatus + Sex + (Age | HNDid) + (1 | subclass) + IPVstatus:Sex
## Data: IPVandCognitionDataSet2
##
## AIC BIC logLik deviance
## 674.2 699.8 -328.1 656.2
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## HNDid (Intercept) 15.77679 3.9720
## Age 0.00554 0.0744 1.00
## subclass (Intercept) 0.00000 0.0000
## Residual 4.07102 2.0177
## Number of obs: 126, groups: HNDid, 63; subclass, 21
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 3.581 0.852 60.800 4.21 8.7e-05
## IPVstatus1 -2.269 1.354 63.700 -1.68 0.0986
## SexMen -1.545 1.144 64.600 -1.35 0.1816
## IPVstatus1:SexMen 6.976 1.981 64.500 3.52 0.0008
##
## Correlation of Fixed Effects:
## (Intr) IPVst1 SexMen
## IPVstatus1 -0.629
## SexMen -0.744 0.468
## IPVstts1:SM 0.430 -0.683 -0.577
plot(st)
plot(mm2)