logTrailsB 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 REML ['merModLmerTest']
## Formula: logTrailsB ~ (Age + IPVstatus + Sex + PovStat)^4 + (Age | HNDid) + (1 | subclass)
## Data: IPVandCognitionDataSet2
## REML criterion at convergence: 239.3
## Random effects:
## Groups Name Std.Dev. Corr
## HNDid (Intercept) 7.47e-01
## Age 1.72e-02 1.00
## subclass (Intercept) 2.14e-06
## Residual 3.24e-01
## Number of obs: 126, groups: HNDid, 63; subclass, 21
## Fixed Effects:
## (Intercept) Age
## 4.489142 0.000787
## IPVstatus1 SexMen
## -0.228592 -0.428486
## PovStatBelow Age:IPVstatus1
## 0.637323 0.007884
## Age:SexMen Age:PovStatBelow
## -0.007740 0.054454
## IPVstatus1:SexMen IPVstatus1:PovStatBelow
## 1.069023 -0.335275
## SexMen:PovStatBelow Age:IPVstatus1:SexMen
## 0.432274 -0.033118
## Age:IPVstatus1:PovStatBelow Age:SexMen:PovStatBelow
## -0.001306 0.013654
## IPVstatus1:SexMen:PovStatBelow Age:IPVstatus1:SexMen:PovStatBelow
## -0.165345 0.035246
## 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) 50.37 1 kept < 1e-07
##
## Fixed effects:
## Sum Sq Mean Sq NumDF DenDF F.value elim.num
## Age:IPVstatus:Sex:PovStat 0.0013 0.0013 1 104.50 0.0114 1
## Age:IPVstatus:PovStat 0.0285 0.0285 1 106.54 0.1581 2
## Age:IPVstatus:Sex 0.0330 0.0330 1 110.77 0.2044 3
## Age:IPVstatus 0.0246 0.0246 1 111.69 0.0977 4
## IPVstatus:Sex:PovStat 0.0455 0.0455 1 52.23 0.4080 5
## Age:Sex:PovStat 0.0793 0.0793 1 114.96 0.9784 6
## Age:Sex 0.0177 0.0177 1 115.45 0.0108 7
## Sex:PovStat 0.0638 0.0638 1 57.49 0.6347 8
## IPVstatus:PovStat 0.2055 0.2055 1 55.85 1.7008 9
## Age 0.9812 0.9812 1 118.81 10.3788 kept
## IPVstatus 0.0608 0.0608 1 56.91 1.5240 kept
## Sex 0.1851 0.1851 1 58.06 3.1538 kept
## PovStat 0.3942 0.3942 1 78.83 9.3020 kept
## Age:PovStat 1.2501 1.2501 1 118.90 11.4385 kept
## IPVstatus:Sex 1.1530 1.1530 1 56.91 10.4439 kept
## Pr(>F)
## Age:IPVstatus:Sex:PovStat 0.9152
## Age:IPVstatus:PovStat 0.6917
## Age:IPVstatus:Sex 0.6521
## Age:IPVstatus 0.7551
## IPVstatus:Sex:PovStat 0.5258
## Age:Sex:PovStat 0.3247
## Age:Sex 0.9173
## Sex:PovStat 0.4289
## IPVstatus:PovStat 0.1975
## Age 0.0016
## IPVstatus 0.2221
## Sex 0.0810
## PovStat 0.0031
## Age:PovStat 0.0010
## IPVstatus:Sex 0.0020
##
## Least squares means:
## IPVstatus Sex PovStat Estimate Standard Error DF
## IPVstatus 0 1.0 NA NA 4.458 0.108 59.2
## IPVstatus 1 2.0 NA NA 4.673 0.146 57.7
## Sex Women NA 2.0 NA 4.409 0.122 57.0
## Sex Men NA 1.0 NA 4.722 0.136 60.2
## PovStat Above NA NA 1.0 4.463 0.105 57.9
## PovStat Below NA NA 2.0 4.668 0.151 60.1
## IPVstatus:Sex 0 Women 1.0 2.0 NA 4.584 0.142 57.2
## IPVstatus:Sex 1 Women 2.0 2.0 NA 4.234 0.191 56.9
## IPVstatus:Sex 0 Men 1.0 1.0 NA 4.332 0.152 59.6
## IPVstatus:Sex 1 Men 2.0 1.0 NA 5.112 0.218 58.0
## t-value Lower CI Upper CI p-value
## IPVstatus 0 41.5 4.24 4.67 <2e-16
## IPVstatus 1 32.0 4.38 4.97 <2e-16
## Sex Women 36.1 4.16 4.65 <2e-16
## Sex Men 34.8 4.45 4.99 <2e-16
## PovStat Above 42.5 4.25 4.67 <2e-16
## PovStat Below 30.9 4.37 4.97 <2e-16
## IPVstatus:Sex 0 Women 32.4 4.30 4.87 <2e-16
## IPVstatus:Sex 1 Women 22.1 3.85 4.62 <2e-16
## IPVstatus:Sex 0 Men 28.4 4.03 4.64 <2e-16
## IPVstatus:Sex 1 Men 23.5 4.68 5.55 <2e-16
##
## Differences of LSMEANS:
## Estimate Standard Error DF t-value
## IPVstatus 0-1 -0.2 0.1739 56.9 -1.23
## Sex Women-Men -0.3 0.1764 58.1 -1.78
## PovStat Above-Below -0.2 0.1802 59.4 -1.13
## IPVstatus:Sex 0 Women- 1 Women 0.4 0.2322 57.0 1.51
## IPVstatus:Sex 0 Women- 0 Men 0.3 0.2010 57.7 1.25
## IPVstatus:Sex 0 Women- 1 Men -0.5 0.2573 57.3 -2.05
## IPVstatus:Sex 1 Women- 0 Men -0.1 0.2377 57.7 -0.41
## IPVstatus:Sex 1 Women- 1 Men -0.9 0.2879 57.4 -3.05
## IPVstatus:Sex 0 Men- 1 Men -0.8 0.2601 56.8 -3.00
## Lower CI Upper CI p-value
## IPVstatus 0-1 -0.563 0.1336 0.222
## Sex Women-Men -0.666 0.0398 0.081
## PovStat Above-Below -0.565 0.1562 0.262
## IPVstatus:Sex 0 Women- 1 Women -0.115 0.8150 0.137
## IPVstatus:Sex 0 Women- 0 Men -0.151 0.6539 0.216
## IPVstatus:Sex 0 Women- 1 Men -1.043 -0.0127 0.045
## IPVstatus:Sex 1 Women- 0 Men -0.574 0.3773 0.680
## IPVstatus:Sex 1 Women- 1 Men -1.454 -0.3015 0.004
## IPVstatus:Sex 0 Men- 1 Men -1.300 -0.2585 0.004
##
## Final model:
## lme4::lmer(formula = logTrailsB ~ Age + IPVstatus + Sex + PovStat +
## (1 | HNDid) + Age:PovStat + IPVstatus:Sex, data = IPVandCognitionDataSet2,
## REML = reml, contrasts = l)
Re-run the suggested final Model 1
(mm1 = lmer(logTrailsB ~ Age + IPVstatus + Sex + PovStat + (Age | HNDid) + (1 |
subclass) + Age:PovStat + IPVstatus:Sex, data = IPVandCognitionDataSet2))
## Warning: number of observations <= rank(Z); variance-covariance matrix
## will be unidentifiable
## Linear mixed model fit by REML ['merModLmerTest']
## Formula: logTrailsB ~ Age + IPVstatus + Sex + PovStat + (Age | HNDid) + (1 | subclass) + Age:PovStat + IPVstatus:Sex
## Data: IPVandCognitionDataSet2
## REML criterion at convergence: 215.7
## Random effects:
## Groups Name Std.Dev. Corr
## HNDid (Intercept) 7.16e-01
## Age 1.38e-02 1.00
## subclass (Intercept) 2.22e-06
## Residual 3.12e-01
## Number of obs: 126, groups: HNDid, 63; subclass, 21
## Fixed Effects:
## (Intercept) Age IPVstatus1
## 4.4710 -0.0031 -0.3839
## SexMen PovStatBelow Age:PovStatBelow
## -0.2869 0.6882 0.0663
## IPVstatus1:SexMen
## 1.2236
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 REML ['merModLmerTest']
## Formula: logTrailsB ~ Age + IPVstatus + Sex + PovStat + (Age | HNDid) + (1 | subclass) + Age:PovStat + IPVstatus:Sex
## Data: IPVandCognitionDataSet2
##
## REML criterion at convergence: 215.7
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## HNDid (Intercept) 5.12e-01 7.16e-01
## Age 1.91e-04 1.38e-02 1.00
## subclass (Intercept) 4.92e-12 2.22e-06
## Residual 9.76e-02 3.12e-01
## Number of obs: 126, groups: HNDid, 63; subclass, 21
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 4.4710 0.1911 63.9000 23.40 < 2e-16
## Age -0.0031 0.0114 106.7000 -0.27 0.78641
## IPVstatus1 -0.3839 0.2275 58.6000 -1.69 0.09691
## SexMen -0.2869 0.1978 59.7000 -1.45 0.15217
## PovStatBelow 0.6882 0.2430 34.8000 2.83 0.00764
## Age:PovStatBelow 0.0663 0.0188 90.9000 3.52 0.00068
## IPVstatus1:SexMen 1.2236 0.3466 59.4000 3.53 0.00081
##
## Correlation of Fixed Effects:
## (Intr) Age IPVst1 SexMen PvSttB Ag:PSB
## Age 0.662
## IPVstatus1 -0.468 -0.083
## SexMen -0.500 -0.019 0.394
## PovStatBelw -0.506 -0.501 0.057 -0.018
## Ag:PvSttBlw -0.340 -0.606 0.022 -0.114 0.675
## IPVstts1:SM 0.331 0.055 -0.661 -0.569 -0.085 0.002
plot(st)
plot(mm1)
logTrailsB Regression Model 2 (with CES)
load("~/Desktop/Megan/Research/IPV and Cognition Paper/IPV R Output/IPVandCognitionDataSet2.rda")
library(lme4)
library(lmerTest)
(mm2 = lmer(logTrailsB ~ (Age + IPVstatus + Sex + PovStat + CES1)^5 + (Age |
HNDid) + (1 | subclass), data = IPVandCognitionDataSet2))
## Warning: number of observations <= rank(Z); variance-covariance matrix
## will be unidentifiable
## Linear mixed model fit by REML ['merModLmerTest']
## Formula: logTrailsB ~ (Age + IPVstatus + Sex + PovStat + CES1)^5 + (Age | HNDid) + (1 | subclass)
## Data: IPVandCognitionDataSet2
## REML criterion at convergence: 245.8
## Random effects:
## Groups Name Std.Dev. Corr
## HNDid (Intercept) 8.02e-01
## Age 2.74e-02 0.90
## subclass (Intercept) 4.01e-05
## Residual 3.15e-01
## Number of obs: 126, groups: HNDid, 63; subclass, 21
## Fixed Effects:
## (Intercept)
## 4.20513
## Age
## -0.00549
## IPVstatus1
## 0.11789
## SexMen
## -0.43669
## PovStatBelow
## 1.83358
## CES11
## 0.57292
## Age:IPVstatus1
## 0.01859
## Age:SexMen
## -0.01940
## Age:PovStatBelow
## 0.11746
## Age:CES11
## 0.01171
## IPVstatus1:SexMen
## 2.35395
## IPVstatus1:PovStatBelow
## -1.72588
## IPVstatus1:CES11
## -0.69433
## SexMen:PovStatBelow
## -0.53289
## SexMen:CES11
## 0.78222
## PovStatBelow:CES11
## -2.20878
## Age:IPVstatus1:SexMen
## 0.00630
## Age:IPVstatus1:PovStatBelow
## -0.09658
## Age:IPVstatus1:CES11
## -0.02100
## Age:SexMen:PovStatBelow
## -0.03469
## Age:SexMen:CES11
## 0.07643
## Age:PovStatBelow:CES11
## -0.09979
## IPVstatus1:SexMen:PovStatBelow
## -0.14772
## IPVstatus1:SexMen:CES11
## -1.87770
## IPVstatus1:PovStatBelow:CES11
## 2.54610
## SexMen:PovStatBelow:CES11
## 1.00493
## Age:IPVstatus1:SexMen:PovStatBelow
## 0.08011
## Age:IPVstatus1:SexMen:CES11
## -0.04652
## Age:IPVstatus1:PovStatBelow:CES11
## 0.16165
## Age:SexMen:PovStatBelow:CES11
## 0.01715
## IPVstatus1:SexMen:PovStatBelow:CES11
## -0.61976
## Age:IPVstatus1:SexMen:PovStatBelow:CES11
## -0.04524
(st = step(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
## Warning: number of observations <= rank(Z); variance-covariance matrix will be unidentifiable
## Warning: number of observations <= rank(Z); variance-covariance matrix will be unidentifiable
## Warning: number of observations <= rank(Z); variance-covariance matrix will be unidentifiable
## Warning: number of observations <= rank(Z); variance-covariance matrix will be unidentifiable
## Warning: number of observations <= rank(Z); variance-covariance matrix will be unidentifiable
## Warning: number of observations <= rank(Z); variance-covariance matrix will be unidentifiable
## Warning: number of observations <= rank(Z); variance-covariance matrix will be unidentifiable
## Warning: number of observations <= rank(Z); variance-covariance matrix will be unidentifiable
## Warning: number of observations <= rank(Z); variance-covariance matrix will be unidentifiable
## Warning: number of observations <= rank(Z); variance-covariance matrix will be unidentifiable
## Warning: number of observations <= rank(Z); variance-covariance matrix will be unidentifiable
## Warning: number of observations <= rank(Z); variance-covariance matrix will be unidentifiable
## Warning: number of observations <= rank(Z); variance-covariance matrix will be unidentifiable
## Warning: number of observations <= rank(Z); variance-covariance matrix will be unidentifiable
## Warning: number of observations <= rank(Z); variance-covariance matrix will be unidentifiable
## Warning: number of observations <= rank(Z); variance-covariance matrix will be unidentifiable
## Warning: number of observations <= rank(Z); variance-covariance matrix will be unidentifiable
## Warning: number of observations <= rank(Z); variance-covariance matrix will be unidentifiable
## Warning: number of observations <= rank(Z); variance-covariance matrix will be unidentifiable
## Warning: number of observations <= rank(Z); variance-covariance matrix will be unidentifiable
## Warning: number of observations <= rank(Z); variance-covariance matrix will be unidentifiable
## Warning: number of observations <= rank(Z); variance-covariance matrix will be unidentifiable
## Warning: number of observations <= rank(Z); variance-covariance matrix will be unidentifiable
## Warning: number of observations <= rank(Z); variance-covariance matrix will be unidentifiable
## Warning: number of observations <= rank(Z); variance-covariance matrix will be unidentifiable
## Warning: number of observations <= rank(Z); variance-covariance matrix will be unidentifiable
## Warning: number of observations <= rank(Z); variance-covariance matrix will be unidentifiable
## Warning: number of observations <= rank(Z); variance-covariance matrix will be unidentifiable
## Warning: number of observations <= rank(Z); variance-covariance matrix will be unidentifiable
## Warning: number of observations <= rank(Z); variance-covariance matrix will be unidentifiable
## Warning: number of observations <= rank(Z); variance-covariance matrix will be unidentifiable
## Warning: number of observations <= rank(Z); variance-covariance matrix will be unidentifiable
## Warning: number of observations <= rank(Z); variance-covariance matrix will be unidentifiable
## Warning: number of observations <= rank(Z); variance-covariance matrix will be unidentifiable
## Warning: number of observations <= rank(Z); variance-covariance matrix will be unidentifiable
## Warning: number of observations <= rank(Z); variance-covariance matrix will be unidentifiable
## Warning: number of observations <= rank(Z); variance-covariance matrix will be unidentifiable
## Warning: number of observations <= rank(Z); variance-covariance matrix will be unidentifiable
## Warning: number of observations <= rank(Z); variance-covariance matrix will be unidentifiable
## Warning: number of observations <= rank(Z); variance-covariance matrix will be unidentifiable
## Warning: number of observations <= rank(Z); variance-covariance matrix will be unidentifiable
## Warning: number of observations <= rank(Z); variance-covariance matrix will be unidentifiable
## Warning: number of observations <= rank(Z); variance-covariance matrix will be unidentifiable
## Warning: number of observations <= rank(Z); variance-covariance matrix will be unidentifiable
## Warning: number of observations <= rank(Z); variance-covariance matrix will be unidentifiable
## Warning: number of observations <= rank(Z); variance-covariance matrix will be unidentifiable
## Warning: number of observations <= rank(Z); variance-covariance matrix will be unidentifiable
## Warning: number of observations <= rank(Z); variance-covariance matrix will be unidentifiable
## Warning: number of observations <= rank(Z); variance-covariance matrix will be unidentifiable
## Warning: number of observations <= rank(Z); variance-covariance matrix will be unidentifiable
## Warning: number of observations <= rank(Z); variance-covariance matrix will be unidentifiable
## Warning: number of observations <= rank(Z); variance-covariance matrix will be unidentifiable
## Warning: number of observations <= rank(Z); variance-covariance matrix will be unidentifiable
## Warning: number of observations <= rank(Z); variance-covariance matrix will be unidentifiable
##
## Random effects:
## Chi.sq Chi.DF elim.num p.value
## (1 | subclass) 0.00 1 1 1.0000
## (Age | HNDid) 3.17 1 kept 0.0752
##
## Fixed effects:
## Sum Sq Mean Sq NumDF DenDF F.value elim.num
## Age:IPVstatus:Sex:PovStat:CES1 0.0038 0.0038 1 55.39 0.0386 1
## Age:Sex:PovStat:CES1 0.0004 0.0004 1 48.29 0.0020 2
## IPVstatus:Sex:PovStat:CES1 0.0004 0.0004 1 60.41 0.0072 3
## Age:IPVstatus:Sex:PovStat 0.0264 0.0264 1 49.48 0.2946 4
## Age:Sex:PovStat 0.0357 0.0357 1 44.92 0.0815 5
## Age:IPVstatus:Sex:CES1 0.0022 0.0022 1 52.70 0.4610 6
## Age:IPVstatus:Sex 0.0099 0.0099 1 74.24 0.0001 7
## Sex:PovStat:CES1 0.0668 0.0668 1 62.22 1.0410 8
## Age:IPVstatus:PovStat:CES1 0.1623 0.1623 1 81.92 1.0657 9
## Age:IPVstatus:CES1 0.0307 0.0307 1 82.44 0.0560 10
## Age:IPVstatus:PovStat 0.0424 0.0424 1 83.32 0.2762 11
## Age:IPVstatus 0.0012 0.0012 1 86.70 0.0458 12
## IPVstatus:Sex:PovStat 0.0518 0.0518 1 52.79 0.7804 13
## Sex:PovStat 0.0004 0.0004 1 61.91 0.1953 14
## Age:PovStat:CES1 0.1684 0.1684 1 77.59 1.6957 15
## IPVstatus:PovStat:CES1 0.0788 0.0788 1 51.13 1.7431 16
## IPVstatus:Sex:CES1 0.1515 0.1515 1 51.04 2.0764 17
## IPVstatus:PovStat 0.2337 0.2337 1 53.02 2.0488 18
## IPVstatus:CES1 0.3410 0.3410 1 54.59 2.4988 19
## Age:Sex:CES1 0.3422 0.3422 1 94.26 3.7995 20
## Age:Sex 0.0010 0.0010 1 31.47 0.0053 21
## Sex:CES1 0.0337 0.0337 1 55.53 0.0091 22
## Age:CES1 0.0454 0.0454 1 92.56 0.2974 23
## PovStat:CES1 0.2080 0.2080 1 56.65 3.2383 24
## CES1 0.0177 0.0177 1 57.37 0.0000 25
## Age 0.3997 0.3997 1 86.04 10.1621 kept
## IPVstatus 0.0608 0.0608 1 59.00 1.7491 kept
## Sex 0.1204 0.1204 1 59.36 3.4989 kept
## PovStat 0.0440 0.0440 1 34.76 8.0204 kept
## Age:PovStat 1.0834 1.0834 1 90.86 12.3735 kept
## IPVstatus:Sex 1.2837 1.2837 1 59.41 12.4610 kept
## Pr(>F)
## Age:IPVstatus:Sex:PovStat:CES1 0.8451
## Age:Sex:PovStat:CES1 0.9648
## IPVstatus:Sex:PovStat:CES1 0.9328
## Age:IPVstatus:Sex:PovStat 0.5897
## Age:Sex:PovStat 0.7765
## Age:IPVstatus:Sex:CES1 0.5001
## Age:IPVstatus:Sex 0.9913
## Sex:PovStat:CES1 0.3116
## Age:IPVstatus:PovStat:CES1 0.3050
## Age:IPVstatus:CES1 0.8136
## Age:IPVstatus:PovStat 0.6006
## Age:IPVstatus 0.8311
## IPVstatus:Sex:PovStat 0.3810
## Sex:PovStat 0.6601
## Age:PovStat:CES1 0.1967
## IPVstatus:PovStat:CES1 0.1926
## IPVstatus:Sex:CES1 0.1557
## IPVstatus:PovStat 0.1582
## IPVstatus:CES1 0.1197
## Age:Sex:CES1 0.0542
## Age:Sex 0.9427
## Sex:CES1 0.9242
## Age:CES1 0.5868
## PovStat:CES1 0.0773
## CES1 0.9981
## Age 0.0020
## IPVstatus 0.1911
## Sex 0.0663
## PovStat 0.0076
## Age:PovStat 0.0007
## IPVstatus:Sex 0.0008
##
## Least squares means:
## IPVstatus Sex PovStat Estimate Standard Error DF
## IPVstatus 0 1.0 NA NA 4.453 0.108 59.2
## IPVstatus 1 2.0 NA NA 4.681 0.146 59.4
## Sex Women NA 2.0 NA 4.405 0.121 58.5
## Sex Men NA 1.0 NA 4.730 0.135 61.4
## PovStat Above NA NA 1.0 4.464 0.108 55.6
## PovStat Below NA NA 2.0 4.671 0.151 57.9
## IPVstatus:Sex 0 Women 1.0 2.0 NA 4.597 0.139 59.4
## IPVstatus:Sex 1 Women 2.0 2.0 NA 4.213 0.190 58.9
## IPVstatus:Sex 0 Men 1.0 1.0 NA 4.310 0.153 61.3
## IPVstatus:Sex 1 Men 2.0 1.0 NA 5.150 0.217 59.9
## t-value Lower CI Upper CI p-value
## IPVstatus 0 41.4 4.24 4.67 <2e-16
## IPVstatus 1 32.2 4.39 4.97 <2e-16
## Sex Women 36.4 4.16 4.65 <2e-16
## Sex Men 35.0 4.46 5.00 <2e-16
## PovStat Above 41.5 4.25 4.68 <2e-16
## PovStat Below 30.9 4.37 4.97 <2e-16
## IPVstatus:Sex 0 Women 33.1 4.32 4.87 <2e-16
## IPVstatus:Sex 1 Women 22.2 3.83 4.59 <2e-16
## IPVstatus:Sex 0 Men 28.1 4.00 4.62 <2e-16
## IPVstatus:Sex 1 Men 23.8 4.72 5.58 <2e-16
##
## Differences of LSMEANS:
## Estimate Standard Error DF t-value
## IPVstatus 0-1 -0.2 0.1723 59.0 -1.32
## Sex Women-Men -0.3 0.1737 59.4 -1.87
## PovStat Above-Below -0.2 0.1812 57.0 -1.14
## IPVstatus:Sex 0 Women- 1 Women 0.4 0.2275 58.6 1.69
## IPVstatus:Sex 0 Women- 0 Men 0.3 0.1978 59.7 1.45
## IPVstatus:Sex 0 Women- 1 Men -0.6 0.2535 57.9 -2.18
## IPVstatus:Sex 1 Women- 0 Men -0.1 0.2355 60.2 -0.41
## IPVstatus:Sex 1 Women- 1 Men -0.9 0.2851 59.3 -3.29
## IPVstatus:Sex 0 Men- 1 Men -0.8 0.2602 59.5 -3.23
## Lower CI Upper CI p-value
## IPVstatus 0-1 -0.5728 0.1169 0.191
## Sex Women-Men -0.6723 0.0226 0.066
## PovStat Above-Below -0.5695 0.1563 0.259
## IPVstatus:Sex 0 Women- 1 Women -0.0715 0.8392 0.097
## IPVstatus:Sex 0 Women- 0 Men -0.1088 0.6827 0.152
## IPVstatus:Sex 0 Women- 1 Men -1.0602 -0.0453 0.033
## IPVstatus:Sex 1 Women- 0 Men -0.5680 0.3741 0.682
## IPVstatus:Sex 1 Women- 1 Men -1.5070 -0.3663 0.002
## IPVstatus:Sex 0 Men- 1 Men -1.3603 -0.3192 0.002
##
## Final model:
## lme4::lmer(formula = logTrailsB ~ Age + IPVstatus + Sex + PovStat +
## (Age | HNDid) + Age:PovStat + IPVstatus:Sex, data = IPVandCognitionDataSet2,
## REML = reml, contrasts = l)
Re-run the suggested final Model 2
(mm2 = lmer(logTrailsB ~ Age + IPVstatus + Sex + PovStat + (Age | HNDid) + (1 |
subclass) + Age:PovStat + IPVstatus:Sex, data = IPVandCognitionDataSet2))
## Warning: number of observations <= rank(Z); variance-covariance matrix
## will be unidentifiable
## Linear mixed model fit by REML ['merModLmerTest']
## Formula: logTrailsB ~ Age + IPVstatus + Sex + PovStat + (Age | HNDid) + (1 | subclass) + Age:PovStat + IPVstatus:Sex
## Data: IPVandCognitionDataSet2
## REML criterion at convergence: 215.7
## Random effects:
## Groups Name Std.Dev. Corr
## HNDid (Intercept) 7.16e-01
## Age 1.38e-02 1.00
## subclass (Intercept) 2.22e-06
## Residual 3.12e-01
## Number of obs: 126, groups: HNDid, 63; subclass, 21
## Fixed Effects:
## (Intercept) Age IPVstatus1
## 4.4710 -0.0031 -0.3839
## SexMen PovStatBelow Age:PovStatBelow
## -0.2869 0.6882 0.0663
## IPVstatus1:SexMen
## 1.2236
summary(mm2)
## Warning: number of observations <= rank(Z); variance-covariance matrix will be unidentifiable
## Warning: number of observations <= rank(Z); variance-covariance matrix will be unidentifiable
## Linear mixed model fit by REML ['merModLmerTest']
## Formula: logTrailsB ~ Age + IPVstatus + Sex + PovStat + (Age | HNDid) + (1 | subclass) + Age:PovStat + IPVstatus:Sex
## Data: IPVandCognitionDataSet2
##
## REML criterion at convergence: 215.7
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## HNDid (Intercept) 5.12e-01 7.16e-01
## Age 1.91e-04 1.38e-02 1.00
## subclass (Intercept) 4.92e-12 2.22e-06
## Residual 9.76e-02 3.12e-01
## Number of obs: 126, groups: HNDid, 63; subclass, 21
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 4.4710 0.1911 63.9000 23.40 < 2e-16
## Age -0.0031 0.0114 106.7000 -0.27 0.78641
## IPVstatus1 -0.3839 0.2275 58.6000 -1.69 0.09691
## SexMen -0.2869 0.1978 59.7000 -1.45 0.15217
## PovStatBelow 0.6882 0.2430 34.8000 2.83 0.00764
## Age:PovStatBelow 0.0663 0.0188 90.9000 3.52 0.00068
## IPVstatus1:SexMen 1.2236 0.3466 59.4000 3.53 0.00081
##
## Correlation of Fixed Effects:
## (Intr) Age IPVst1 SexMen PvSttB Ag:PSB
## Age 0.662
## IPVstatus1 -0.468 -0.083
## SexMen -0.500 -0.019 0.394
## PovStatBelw -0.506 -0.501 0.057 -0.018
## Ag:PvSttBlw -0.340 -0.606 0.022 -0.114 0.675
## IPVstts1:SM 0.331 0.055 -0.661 -0.569 -0.085 0.002
plot(st)
plot(mm2)