Trails B-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 REML ['merModLmerTest']
## Formula: IPVandCognitionDataSet2$"TrailsB-A" ~ (Age + IPVstatus + Sex + PovStat)^4 + (Age | HNDid) + (1 | subclass)
## Data: IPVandCognitionDataSet2
## REML criterion at convergence: 1406
## Random effects:
## Groups Name Std.Dev. Corr
## HNDid (Intercept) 1.74e+02
## Age 6.22e+00 1.00
## subclass (Intercept) 1.05e-04
## Residual 6.41e+01
## Number of obs: 126, groups: HNDid, 63; subclass, 21
## Fixed Effects:
## (Intercept) Age
## 95.418 0.624
## IPVstatus1 SexMen
## -54.901 -54.505
## PovStatBelow Age:IPVstatus1
## 122.141 -0.585
## Age:SexMen Age:PovStatBelow
## -0.715 11.252
## IPVstatus1:SexMen IPVstatus1:PovStatBelow
## 167.783 -127.305
## SexMen:PovStatBelow Age:IPVstatus1:SexMen
## 66.170 -10.656
## Age:IPVstatus1:PovStatBelow Age:SexMen:PovStatBelow
## -10.320 4.704
## IPVstatus1:SexMen:PovStatBelow Age:IPVstatus1:SexMen:PovStatBelow
## 77.881 23.200
## 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) 52.93 1 kept < 1e-07
##
## Fixed effects:
## Sum Sq Mean Sq NumDF DenDF F.value elim.num
## Age:IPVstatus:Sex:PovStat 3877.35 3877.35 1 102.38 0.8522 1
## Age:IPVstatus:Sex 227.33 227.33 1 106.36 0.0501 2
## Age:IPVstatus:PovStat 1480.48 1480.48 1 108.23 0.0705 3
## Age:IPVstatus 45.17 45.17 1 110.45 0.0857 4
## IPVstatus:Sex:PovStat 2389.92 2389.92 1 51.89 0.6491 5
## IPVstatus:PovStat 4822.49 4822.49 1 52.87 1.0664 6
## Age:Sex:PovStat 11467.54 11467.54 1 115.68 2.7120 7
## Sex:PovStat 471.79 471.79 1 60.81 0.0716 8
## Age:Sex 6684.75 6684.75 1 117.55 1.0876 9
## Age 31667.36 31667.36 1 119.00 8.9432 kept
## IPVstatus 3416.47 3416.47 1 55.46 1.7471 kept
## Sex 13818.56 13818.56 1 56.61 4.0675 kept
## PovStat 8449.74 8449.74 1 77.63 7.0533 kept
## Age:PovStat 51228.54 51228.54 1 118.97 11.6901 kept
## IPVstatus:Sex 32789.34 32789.34 1 55.45 7.1152 kept
## Pr(>F)
## Age:IPVstatus:Sex:PovStat 0.3581
## Age:IPVstatus:Sex 0.8234
## Age:IPVstatus:PovStat 0.7911
## Age:IPVstatus 0.7703
## IPVstatus:Sex:PovStat 0.4241
## IPVstatus:PovStat 0.3065
## Age:Sex:PovStat 0.1023
## Sex:PovStat 0.7899
## Age:Sex 0.2991
## Age 0.0034
## IPVstatus 0.1917
## Sex 0.0485
## PovStat 0.0096
## Age:PovStat 0.0009
## IPVstatus:Sex 0.0100
##
## Least squares means:
## IPVstatus Sex PovStat Estimate Standard Error DF
## IPVstatus 0 1.0 NA NA 85.4 22.8 57.8
## IPVstatus 1 2.0 NA NA 134.2 31.0 56.2
## Sex Women NA 2.0 NA 72.0 25.9 55.5
## Sex Men NA 1.0 NA 147.5 28.8 58.8
## PovStat Above NA NA 1.0 96.9 22.3 56.5
## PovStat Below NA NA 2.0 122.7 32.1 58.7
## IPVstatus:Sex 0 Women 1.0 2.0 NA 97.1 30.1 55.8
## IPVstatus:Sex 1 Women 2.0 2.0 NA 47.0 40.6 55.4
## IPVstatus:Sex 0 Men 1.0 1.0 NA 73.6 32.3 58.2
## IPVstatus:Sex 1 Men 2.0 1.0 NA 221.4 46.2 56.6
## t-value Lower CI Upper CI p-value
## IPVstatus 0 3.75 39.74 131 0.0004
## IPVstatus 1 4.33 72.14 196 0.0001
## Sex Women 2.78 20.14 124 0.0074
## Sex Men 5.13 89.97 205 <2e-16
## PovStat Above 4.34 52.18 142 0.0001
## PovStat Below 3.82 58.46 187 0.0003
## IPVstatus:Sex 0 Women 3.23 36.88 157 0.0021
## IPVstatus:Sex 1 Women 1.15 -34.50 128 0.2531
## IPVstatus:Sex 0 Men 2.28 8.93 138 0.0264
## IPVstatus:Sex 1 Men 4.79 128.88 314 <2e-16
##
## Differences of LSMEANS:
## Estimate Standard Error DF t-value
## IPVstatus 0-1 -48.8 36.9 55.5 -1.32
## Sex Women-Men -75.5 37.4 56.6 -2.02
## PovStat Above-Below -25.8 38.2 58.0 -0.68
## IPVstatus:Sex 0 Women- 1 Women 50.2 49.3 55.6 1.02
## IPVstatus:Sex 0 Women- 0 Men 23.5 42.7 56.2 0.55
## IPVstatus:Sex 0 Women- 1 Men -124.3 54.6 55.8 -2.28
## IPVstatus:Sex 1 Women- 0 Men -26.7 50.4 56.3 -0.53
## IPVstatus:Sex 1 Women- 1 Men -174.5 61.1 55.9 -2.85
## IPVstatus:Sex 0 Men- 1 Men -147.8 55.2 55.4 -2.68
## Lower CI Upper CI p-value
## IPVstatus 0-1 -122.8 25.177 0.192
## Sex Women-Men -150.5 -0.526 0.049
## PovStat Above-Below -102.3 50.704 0.502
## IPVstatus:Sex 0 Women- 1 Women -48.6 148.937 0.313
## IPVstatus:Sex 0 Women- 0 Men -62.0 108.928 0.584
## IPVstatus:Sex 0 Women- 1 Men -233.7 -14.863 0.027
## IPVstatus:Sex 1 Women- 0 Men -127.7 74.358 0.599
## IPVstatus:Sex 1 Women- 1 Men -296.9 -52.017 0.006
## IPVstatus:Sex 0 Men- 1 Men -258.5 -37.111 0.010
##
## Final model:
## lme4::lmer(formula = IPVandCognitionDataSet2$"TrailsB-A" ~ 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(IPVandCognitionDataSet2$"TrailsB-A" ~ Age + IPVstatus + Sex + PovStat +
(Age | HNDid) + (1 | subclass) + Age:PovStat + IPVstatus:Sex, data = IPVandCognitionDataSet2))
## Warning: number of observations <= rank(Z); variance-covariance matrix
## will be unidentifiable
## Linear mixed model fit by REML ['merModLmerTest']
## Formula: IPVandCognitionDataSet2$"TrailsB-A" ~ Age + IPVstatus + Sex + PovStat + (Age | HNDid) + (1 | subclass) + Age:PovStat + IPVstatus:Sex
## Data: IPVandCognitionDataSet2
## REML criterion at convergence: 1482
## Random effects:
## Groups Name Std.Dev. Corr
## HNDid (Intercept) 1.61e+02
## Age 4.50e+00 0.99
## subclass (Intercept) 4.63e-04
## Residual 6.29e+01
## Number of obs: 126, groups: HNDid, 63; subclass, 21
## Fixed Effects:
## (Intercept) Age IPVstatus1
## 84.703 -0.941 -54.709
## SexMen PovStatBelow Age:PovStatBelow
## -42.139 131.706 14.384
## IPVstatus1:SexMen
## 227.502
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: IPVandCognitionDataSet2$"TrailsB-A" ~ Age + IPVstatus + Sex + PovStat + (Age | HNDid) + (1 | subclass) + Age:PovStat + IPVstatus:Sex
## Data: IPVandCognitionDataSet2
##
## REML criterion at convergence: 1482
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## HNDid (Intercept) 2.60e+04 1.61e+02
## Age 2.03e+01 4.50e+00 0.99
## subclass (Intercept) 2.14e-07 4.63e-04
## Residual 3.96e+03 6.29e+01
## Number of obs: 126, groups: HNDid, 63; subclass, 21
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 84.703 40.539 68.200 2.09 0.04040
## Age -0.941 2.379 57.700 -0.40 0.69400
## IPVstatus1 -54.709 45.661 55.900 -1.20 0.23591
## SexMen -42.139 39.570 54.700 -1.06 0.29159
## PovStatBelow 131.706 53.670 35.200 2.45 0.01921
## Age:PovStatBelow 14.384 3.981 42.800 3.61 0.00079
## IPVstatus1:SexMen 227.502 69.765 56.200 3.26 0.00189
##
## Correlation of Fixed Effects:
## (Intr) Age IPVst1 SexMen PvSttB Ag:PSB
## Age 0.718
## IPVstatus1 -0.443 -0.095
## SexMen -0.475 -0.034 0.383
## PovStatBelw -0.514 -0.517 0.052 -0.009
## Ag:PvSttBlw -0.368 -0.595 0.018 -0.107 0.738
## IPVstts1:SM 0.318 0.067 -0.660 -0.570 -0.075 0.011
plot(st)
plot(mm1)
Trails B-A Regression Model 2 (with CES)
load("~/Desktop/Megan/Research/IPV and Cognition Paper/IPV R Output/IPVandCognitionDataSet2.rda")
library(lme4)
library(lmerTest)
(mm2 = lmer(IPVandCognitionDataSet2$"TrailsB-A" ~ (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: IPVandCognitionDataSet2$"TrailsB-A" ~ (Age + IPVstatus + Sex + PovStat + CES1)^5 + (Age | HNDid) + (1 | subclass)
## Data: IPVandCognitionDataSet2
## REML criterion at convergence: 1238
## Random effects:
## Groups Name Std.Dev. Corr
## HNDid (Intercept) 1.89e+02
## Age 7.41e+00 1.00
## subclass (Intercept) 2.38e-04
## Residual 5.98e+01
## Number of obs: 126, groups: HNDid, 63; subclass, 21
## Fixed Effects:
## (Intercept)
## 36.98
## Age
## -1.02
## IPVstatus1
## -3.78
## SexMen
## -29.14
## PovStatBelow
## 376.55
## CES11
## 116.38
## Age:IPVstatus1
## 0.22
## Age:SexMen
## -1.28
## Age:PovStatBelow
## 27.22
## Age:CES11
## 2.83
## IPVstatus1:SexMen
## 470.94
## IPVstatus1:PovStatBelow
## -432.74
## IPVstatus1:CES11
## -103.94
## SexMen:PovStatBelow
## -176.19
## SexMen:CES11
## 36.70
## PovStatBelow:CES11
## -466.72
## Age:IPVstatus1:SexMen
## 2.08
## Age:IPVstatus1:PovStatBelow
## -30.42
## Age:IPVstatus1:CES11
## -1.25
## Age:SexMen:PovStatBelow
## -14.20
## Age:SexMen:CES11
## 7.89
## Age:PovStatBelow:CES11
## -26.78
## IPVstatus1:SexMen:PovStatBelow
## 99.43
## IPVstatus1:SexMen:CES11
## -334.47
## IPVstatus1:PovStatBelow:CES11
## 538.79
## SexMen:PovStatBelow:CES11
## 397.82
## Age:IPVstatus1:SexMen:PovStatBelow
## 32.83
## Age:IPVstatus1:SexMen:CES11
## -8.88
## Age:IPVstatus1:PovStatBelow:CES11
## 33.25
## Age:SexMen:PovStatBelow:CES11
## 34.93
## IPVstatus1:SexMen:PovStatBelow:CES11
## -344.73
## Age:IPVstatus1:SexMen:PovStatBelow:CES11
## -17.23
(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
## 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) 52.16 1 kept < 1e-07
##
## Fixed effects:
## Sum Sq Mean Sq NumDF DenDF F.value
## Age:IPVstatus:Sex:PovStat:CES1 230.679 230.679 1 63.05 0.0563
## IPVstatus:Sex:PovStat:CES1 1389.423 1389.423 1 77.09 0.3471
## Age:IPVstatus:Sex:PovStat 7346.170 7346.170 1 82.63 0.8400
## Age:IPVstatus:PovStat:CES1 7005.244 7005.244 1 69.00 1.0303
## Age:IPVstatus:PovStat 1421.755 1421.755 1 78.72 0.0892
## IPVstatus:PovStat:CES1 731.412 731.412 1 51.68 0.6711
## Age:IPVstatus:Sex:CES1 1171.430 1171.430 1 94.72 0.4163
## Age:IPVstatus:Sex 1.673 1.673 1 87.91 0.0553
## Age:IPVstatus:CES1 3749.771 3749.771 1 99.03 0.0461
## Age:IPVstatus 7.247 7.247 1 94.54 0.0337
## IPVstatus:Sex:PovStat 2621.567 2621.567 1 48.75 2.3323
## IPVstatus:PovStat 4081.316 4081.316 1 49.22 1.5413
## IPVstatus:Sex:CES1 8567.174 8567.174 1 50.37 1.8152
## IPVstatus:CES1 8467.653 8467.653 1 50.95 2.5215
## Age:Sex:PovStat:CES1 8243.273 8243.273 1 88.72 2.6392
## Sex:PovStat:CES1 1115.114 1115.114 1 65.23 0.0056
## Age:PovStat:CES1 2366.113 2366.113 1 101.15 0.4253
## Age:Sex:PovStat 9770.732 9770.732 1 108.55 1.7663
## Sex:PovStat 4.921 4.921 1 60.11 0.0393
## PovStat:CES1 6560.823 6560.823 1 59.26 3.9887
## Age:Sex:CES1 19338.929 19338.929 1 107.12 3.4708
## Age:CES1 7.385 7.385 1 111.50 0.0405
## Sex:CES1 1699.823 1699.823 1 53.97 0.2341
## CES1 293.625 293.625 1 54.83 0.0514
## Age:Sex 5757.628 5757.628 1 117.55 1.0876
## Age 30845.522 30845.522 1 119.00 8.9432
## IPVstatus 2826.192 2826.192 1 55.46 1.7471
## Sex 11399.262 11399.262 1 56.61 4.0675
## PovStat 6815.499 6815.499 1 77.63 7.0533
## Age:PovStat 49948.802 49948.802 1 118.97 11.6901
## IPVstatus:Sex 27535.028 27535.028 1 55.45 7.1152
## elim.num Pr(>F)
## Age:IPVstatus:Sex:PovStat:CES1 1 0.8133
## IPVstatus:Sex:PovStat:CES1 2 0.5575
## Age:IPVstatus:Sex:PovStat 3 0.3621
## Age:IPVstatus:PovStat:CES1 4 0.3136
## Age:IPVstatus:PovStat 5 0.7659
## IPVstatus:PovStat:CES1 6 0.4164
## Age:IPVstatus:Sex:CES1 7 0.5203
## Age:IPVstatus:Sex 8 0.8147
## Age:IPVstatus:CES1 9 0.8305
## Age:IPVstatus 10 0.8547
## IPVstatus:Sex:PovStat 11 0.1332
## IPVstatus:PovStat 12 0.2203
## IPVstatus:Sex:CES1 13 0.1839
## IPVstatus:CES1 14 0.1185
## Age:Sex:PovStat:CES1 15 0.1078
## Sex:PovStat:CES1 16 0.9407
## Age:PovStat:CES1 17 0.5158
## Age:Sex:PovStat 18 0.1866
## Sex:PovStat 19 0.8436
## PovStat:CES1 20 0.0504
## Age:Sex:CES1 21 0.0652
## Age:CES1 22 0.8409
## Sex:CES1 23 0.6305
## CES1 24 0.8215
## Age:Sex 25 0.2991
## Age kept 0.0034
## IPVstatus kept 0.1917
## Sex kept 0.0485
## PovStat kept 0.0096
## Age:PovStat kept 0.0009
## IPVstatus:Sex kept 0.0100
##
## Least squares means:
## IPVstatus Sex PovStat Estimate Standard Error DF
## IPVstatus 0 1.0 NA NA 85.4 22.8 57.8
## IPVstatus 1 2.0 NA NA 134.2 31.0 56.2
## Sex Women NA 2.0 NA 72.0 25.9 55.5
## Sex Men NA 1.0 NA 147.5 28.8 58.8
## PovStat Above NA NA 1.0 96.9 22.3 56.5
## PovStat Below NA NA 2.0 122.7 32.1 58.7
## IPVstatus:Sex 0 Women 1.0 2.0 NA 97.1 30.1 55.8
## IPVstatus:Sex 1 Women 2.0 2.0 NA 47.0 40.6 55.4
## IPVstatus:Sex 0 Men 1.0 1.0 NA 73.6 32.3 58.2
## IPVstatus:Sex 1 Men 2.0 1.0 NA 221.4 46.2 56.6
## t-value Lower CI Upper CI p-value
## IPVstatus 0 3.75 39.74 131 0.0004
## IPVstatus 1 4.33 72.14 196 0.0001
## Sex Women 2.78 20.14 124 0.0074
## Sex Men 5.13 89.97 205 <2e-16
## PovStat Above 4.34 52.18 142 0.0001
## PovStat Below 3.82 58.46 187 0.0003
## IPVstatus:Sex 0 Women 3.23 36.88 157 0.0021
## IPVstatus:Sex 1 Women 1.15 -34.50 128 0.2531
## IPVstatus:Sex 0 Men 2.28 8.93 138 0.0264
## IPVstatus:Sex 1 Men 4.79 128.88 314 <2e-16
##
## Differences of LSMEANS:
## Estimate Standard Error DF t-value
## IPVstatus 0-1 -48.8 36.9 55.5 -1.32
## Sex Women-Men -75.5 37.4 56.6 -2.02
## PovStat Above-Below -25.8 38.2 58.0 -0.68
## IPVstatus:Sex 0 Women- 1 Women 50.2 49.3 55.6 1.02
## IPVstatus:Sex 0 Women- 0 Men 23.5 42.7 56.2 0.55
## IPVstatus:Sex 0 Women- 1 Men -124.3 54.6 55.8 -2.28
## IPVstatus:Sex 1 Women- 0 Men -26.7 50.4 56.3 -0.53
## IPVstatus:Sex 1 Women- 1 Men -174.5 61.1 55.9 -2.85
## IPVstatus:Sex 0 Men- 1 Men -147.8 55.2 55.4 -2.68
## Lower CI Upper CI p-value
## IPVstatus 0-1 -122.8 25.177 0.192
## Sex Women-Men -150.5 -0.526 0.049
## PovStat Above-Below -102.3 50.704 0.502
## IPVstatus:Sex 0 Women- 1 Women -48.6 148.937 0.313
## IPVstatus:Sex 0 Women- 0 Men -62.0 108.928 0.584
## IPVstatus:Sex 0 Women- 1 Men -233.7 -14.863 0.027
## IPVstatus:Sex 1 Women- 0 Men -127.7 74.358 0.599
## IPVstatus:Sex 1 Women- 1 Men -296.9 -52.017 0.006
## IPVstatus:Sex 0 Men- 1 Men -258.5 -37.111 0.010
##
## Final model:
## lme4::lmer(formula = IPVandCognitionDataSet2$"TrailsB-A" ~ Age +
## IPVstatus + Sex + PovStat + (1 | HNDid) + Age:PovStat + IPVstatus:Sex,
## data = IPVandCognitionDataSet2, REML = reml, contrasts = l)
Re-run the suggested final Model 2
(mm2 = lmer(IPVandCognitionDataSet2$"TrailsB-A" ~ Age + IPVstatus + Sex + PovStat +
(Age | HNDid) + (1 | subclass) + Age:PovStat + IPVstatus:Sex, data = IPVandCognitionDataSet2))
## Warning: number of observations <= rank(Z); variance-covariance matrix
## will be unidentifiable
## Linear mixed model fit by REML ['merModLmerTest']
## Formula: IPVandCognitionDataSet2$"TrailsB-A" ~ Age + IPVstatus + Sex + PovStat + (Age | HNDid) + (1 | subclass) + Age:PovStat + IPVstatus:Sex
## Data: IPVandCognitionDataSet2
## REML criterion at convergence: 1482
## Random effects:
## Groups Name Std.Dev. Corr
## HNDid (Intercept) 1.61e+02
## Age 4.50e+00 0.99
## subclass (Intercept) 4.63e-04
## Residual 6.29e+01
## Number of obs: 126, groups: HNDid, 63; subclass, 21
## Fixed Effects:
## (Intercept) Age IPVstatus1
## 84.703 -0.941 -54.709
## SexMen PovStatBelow Age:PovStatBelow
## -42.139 131.706 14.384
## IPVstatus1:SexMen
## 227.502
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: IPVandCognitionDataSet2$"TrailsB-A" ~ Age + IPVstatus + Sex + PovStat + (Age | HNDid) + (1 | subclass) + Age:PovStat + IPVstatus:Sex
## Data: IPVandCognitionDataSet2
##
## REML criterion at convergence: 1482
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## HNDid (Intercept) 2.60e+04 1.61e+02
## Age 2.03e+01 4.50e+00 0.99
## subclass (Intercept) 2.14e-07 4.63e-04
## Residual 3.96e+03 6.29e+01
## Number of obs: 126, groups: HNDid, 63; subclass, 21
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 84.703 40.539 68.200 2.09 0.04040
## Age -0.941 2.379 57.700 -0.40 0.69400
## IPVstatus1 -54.709 45.661 55.900 -1.20 0.23591
## SexMen -42.139 39.570 54.700 -1.06 0.29159
## PovStatBelow 131.706 53.670 35.200 2.45 0.01921
## Age:PovStatBelow 14.384 3.981 42.800 3.61 0.00079
## IPVstatus1:SexMen 227.502 69.765 56.200 3.26 0.00189
##
## Correlation of Fixed Effects:
## (Intr) Age IPVst1 SexMen PvSttB Ag:PSB
## Age 0.718
## IPVstatus1 -0.443 -0.095
## SexMen -0.475 -0.034 0.383
## PovStatBelw -0.514 -0.517 0.052 -0.009
## Ag:PvSttBlw -0.368 -0.595 0.018 -0.107 0.738
## IPVstts1:SM 0.318 0.067 -0.660 -0.570 -0.075 0.011
plot(st)
plot(mm2)