## 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 Regression Model 1
(mm1 = lmer(IPVandCognitionDataSet2$"TrailsB:A" ~ (Age + IPVstatus + Sex + PovStat)^4 +
(Age | HNDid) + (1 | subclass), data = IPVandCognitionDataSet2))
## Warning: number of observations <= rank(Z); variance-covariance matrix
## will be unidentifiable
## Linear mixed model fit by REML ['merModLmerTest']
## Formula: IPVandCognitionDataSet2$"TrailsB:A" ~ (Age + IPVstatus + Sex + PovStat)^4 + (Age | HNDid) + (1 | subclass)
## Data: IPVandCognitionDataSet2
## REML criterion at convergence: 593.1
## Random effects:
## Groups Name Std.Dev. Corr
## HNDid (Intercept) 5.31e+00
## Age 2.15e-01 1.00
## subclass (Intercept) 1.35e-05
## Residual 1.43e+00
## Number of obs: 126, groups: HNDid, 63; subclass, 21
## Fixed Effects:
## (Intercept) Age
## 4.2644 0.0261
## IPVstatus1 SexMen
## -2.2151 -1.9660
## PovStatBelow Age:IPVstatus1
## 2.9750 -0.0714
## Age:SexMen Age:PovStatBelow
## -0.0571 0.3123
## IPVstatus1:SexMen IPVstatus1:PovStatBelow
## 6.9839 -3.7097
## SexMen:PovStatBelow Age:IPVstatus1:SexMen
## 0.5953 -0.1587
## Age:IPVstatus1:PovStatBelow Age:SexMen:PovStatBelow
## -0.3304 -0.0554
## IPVstatus1:SexMen:PovStatBelow Age:IPVstatus1:SexMen:PovStatBelow
## -0.6478 0.5884
(st = step(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
## 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) 67.25 1 kept < 1e-07
##
## Fixed effects:
## Sum Sq Mean Sq NumDF DenDF F.value elim.num
## Age:IPVstatus:Sex:PovStat 2.1829 2.1829 1 95.42 0.8563 1
## Age:IPVstatus:Sex 0.0314 0.0314 1 99.55 0.0014 2
## Age:IPVstatus:PovStat 0.0654 0.0654 1 101.87 0.0202 3
## Age:IPVstatus 1.4070 1.4070 1 104.47 0.5698 4
## Age:Sex:PovStat 2.8983 2.8983 1 110.92 0.7175 5
## Age:Sex 0.2104 0.2104 1 109.52 0.0039 6
## IPVstatus:Sex:PovStat 4.0913 4.0913 1 52.99 2.1556 7
## Sex:PovStat 0.0011 0.0011 1 56.96 0.0002 8
## IPVstatus:PovStat 4.4102 4.4102 1 55.11 2.1265 9
## Age 5.1626 5.1626 1 116.69 3.6119 kept
## IPVstatus 1.6390 1.6390 1 56.25 1.5766 kept
## Sex 5.2384 5.2384 1 57.40 3.9280 kept
## PovStat 0.1884 0.1884 1 78.16 1.4353 kept
## Age:PovStat 16.5689 16.5689 1 116.30 6.5051 kept
## IPVstatus:Sex 21.6601 21.6601 1 56.24 8.1093 kept
## Pr(>F)
## Age:IPVstatus:Sex:PovStat 0.3571
## Age:IPVstatus:Sex 0.9706
## Age:IPVstatus:PovStat 0.8873
## Age:IPVstatus 0.4520
## Age:Sex:PovStat 0.3988
## Age:Sex 0.9501
## IPVstatus:Sex:PovStat 0.1480
## Sex:PovStat 0.9878
## IPVstatus:PovStat 0.1504
## Age 0.0598
## IPVstatus 0.2144
## Sex 0.0523
## PovStat 0.2345
## Age:PovStat 0.0121
## IPVstatus:Sex 0.0061
##
## Least squares means:
## IPVstatus Sex PovStat Estimate Standard Error DF
## IPVstatus 0 1.0 NA NA 3.423 0.620 58.5
## IPVstatus 1 2.0 NA NA 4.689 0.845 57.0
## Sex Women NA 2.0 NA 3.045 0.707 56.3
## Sex Men NA 1.0 NA 5.067 0.782 59.6
## PovStat Above NA NA 1.0 4.215 0.608 57.2
## PovStat Below NA NA 2.0 3.897 0.873 59.5
## IPVstatus:Sex 0 Women 1.0 2.0 NA 3.854 0.820 56.5
## IPVstatus:Sex 1 Women 2.0 2.0 NA 2.236 1.109 56.2
## IPVstatus:Sex 0 Men 1.0 1.0 NA 2.992 0.880 59.0
## IPVstatus:Sex 1 Men 2.0 1.0 NA 7.142 1.259 57.4
## t-value Lower CI Upper CI p-value
## IPVstatus 0 5.52 2.1814 4.67 <2e-16
## IPVstatus 1 5.55 2.9972 6.38 <2e-16
## Sex Women 4.31 1.6291 4.46 0.0001
## Sex Men 6.48 3.5025 6.63 <2e-16
## PovStat Above 6.93 2.9965 5.43 <2e-16
## PovStat Below 4.47 2.1512 5.64 <2e-16
## IPVstatus:Sex 0 Women 4.70 2.2111 5.50 <2e-16
## IPVstatus:Sex 1 Women 2.02 0.0133 4.46 0.0487
## IPVstatus:Sex 0 Men 3.40 1.2325 4.75 0.0012
## IPVstatus:Sex 1 Men 5.67 4.6201 9.66 <2e-16
##
## Differences of LSMEANS:
## Estimate Standard Error DF t-value
## IPVstatus 0-1 -1.3 1.008 56.2 -1.26
## Sex Women-Men -2.0 1.020 57.4 -1.98
## PovStat Above-Below 0.3 1.040 58.7 0.31
## IPVstatus:Sex 0 Women- 1 Women 1.6 1.345 56.4 1.20
## IPVstatus:Sex 0 Women- 0 Men 0.9 1.163 57.0 0.74
## IPVstatus:Sex 0 Women- 1 Men -3.3 1.490 56.6 -2.21
## IPVstatus:Sex 1 Women- 0 Men -0.8 1.375 57.1 -0.55
## IPVstatus:Sex 1 Women- 1 Men -4.9 1.667 56.7 -2.94
## IPVstatus:Sex 0 Men- 1 Men -4.1 1.508 56.2 -2.75
## Lower CI Upper CI p-value
## IPVstatus 0-1 -3.28 0.7532 0.214
## Sex Women-Men -4.06 0.0207 0.052
## PovStat Above-Below -1.76 2.3990 0.761
## IPVstatus:Sex 0 Women- 1 Women -1.08 4.3129 0.234
## IPVstatus:Sex 0 Women- 0 Men -1.47 3.1911 0.462
## IPVstatus:Sex 0 Women- 1 Men -6.27 -0.3028 0.032
## IPVstatus:Sex 1 Women- 0 Men -3.51 1.9976 0.584
## IPVstatus:Sex 1 Women- 1 Men -8.25 -1.5668 0.005
## IPVstatus:Sex 0 Men- 1 Men -7.17 -1.1294 0.008
##
## 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$"TrailsB:A" ~ Age + IPVstatus + Sex + PovStat + (Age | HNDid) + (1 | subclass) + Age:PovStat + IPVstatus:Sex
## Data: IPVandCognitionDataSet2
## REML criterion at convergence: 606.6
## Random effects:
## Groups Name Std.Dev. Corr
## HNDid (Intercept) 4.88e+00
## Age 1.71e-01 0.95
## subclass (Intercept) 1.10e-06
## Residual 1.42e+00
## Number of obs: 126, groups: HNDid, 63; subclass, 21
## Fixed Effects:
## (Intercept) Age IPVstatus1
## 3.6907 -0.0347 -1.3308
## SexMen PovStatBelow Age:PovStatBelow
## -1.0897 1.8562 0.2881
## IPVstatus1:SexMen
## 6.5609
summary(mm1)
## Warning: number of observations <= rank(Z); variance-covariance matrix will be unidentifiable
## Warning: number of observations <= rank(Z); variance-covariance matrix will be unidentifiable
## Linear mixed model fit by REML ['merModLmerTest']
## Formula: IPVandCognitionDataSet2$"TrailsB:A" ~ Age + IPVstatus + Sex + PovStat + (Age | HNDid) + (1 | subclass) + Age:PovStat + IPVstatus:Sex
## Data: IPVandCognitionDataSet2
##
## REML criterion at convergence: 606.6
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## HNDid (Intercept) 2.39e+01 4.88e+00
## Age 2.94e-02 1.71e-01 0.95
## subclass (Intercept) 1.22e-12 1.10e-06
## Residual 2.02e+00 1.42e+00
## Number of obs: 126, groups: HNDid, 63; subclass, 21
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 3.6907 1.1101 53.9000 3.32 0.0016
## Age -0.0347 0.0608 48.2000 -0.57 0.5711
## IPVstatus1 -1.3308 1.2244 49.8000 -1.09 0.2823
## SexMen -1.0897 1.0537 46.0000 -1.03 0.3065
## PovStatBelow 1.8562 1.5362 24.7000 1.21 0.2384
## Age:PovStatBelow 0.2881 0.1033 36.9000 2.79 0.0083
## IPVstatus1:SexMen 6.5609 1.8723 47.8000 3.50 0.0010
##
## Correlation of Fixed Effects:
## (Intr) Age IPVst1 SexMen PvSttB Ag:PSB
## Age 0.739
## IPVstatus1 -0.423 -0.089
## SexMen -0.457 -0.031 0.375
## PovStatBelw -0.511 -0.512 0.043 0.002
## Ag:PvSttBlw -0.382 -0.585 0.010 -0.095 0.774
## IPVstts1:SM 0.304 0.060 -0.660 -0.569 -0.066 0.018
plot(st)
plot(mm1)
Trails B:A Regression Model 2 (with CES)
(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: 548.4
## Random effects:
## Groups Name Std.Dev. Corr
## HNDid (Intercept) 5.50e+00
## Age 2.14e-01 1.00
## subclass (Intercept) 1.26e-05
## Residual 1.38e+00
## Number of obs: 126, groups: HNDid, 63; subclass, 21
## Fixed Effects:
## (Intercept)
## 3.2408
## Age
## 0.0141
## IPVstatus1
## -2.0553
## SexMen
## -2.2487
## PovStatBelow
## 9.1611
## CES11
## 1.8252
## Age:IPVstatus1
## -0.1523
## Age:SexMen
## -0.1348
## Age:PovStatBelow
## 0.7252
## Age:CES11
## -0.0143
## IPVstatus1:SexMen
## 14.5632
## IPVstatus1:PovStatBelow
## -10.7771
## IPVstatus1:CES11
## -0.1922
## SexMen:PovStatBelow
## -4.4751
## SexMen:CES11
## 4.2153
## PovStatBelow:CES11
## -11.6195
## Age:IPVstatus1:SexMen
## 0.2730
## Age:IPVstatus1:PovStatBelow
## -0.7863
## Age:IPVstatus1:CES11
## 0.2068
## Age:SexMen:PovStatBelow
## -0.4190
## Age:SexMen:CES11
## 0.4527
## Age:PovStatBelow:CES11
## -0.7123
## IPVstatus1:SexMen:PovStatBelow
## 0.6187
## IPVstatus1:SexMen:CES11
## -12.1825
## IPVstatus1:PovStatBelow:CES11
## 12.5917
## SexMen:PovStatBelow:CES11
## 6.3200
## Age:IPVstatus1:SexMen:PovStatBelow
## 0.7555
## Age:IPVstatus1:SexMen:CES11
## -0.7671
## Age:IPVstatus1:PovStatBelow:CES11
## 0.7446
## Age:SexMen:PovStatBelow:CES11
## 0.4201
## IPVstatus1:SexMen:PovStatBelow:CES11
## -5.1822
## Age:IPVstatus1:SexMen:PovStatBelow:CES11
## -0.1099
(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) 68.59 1 kept < 1e-07
##
## Fixed effects:
## Sum Sq Mean Sq NumDF DenDF F.value
## Age:IPVstatus:Sex:PovStat:CES1 0.0019 0.0019 1 61.41 0.0008
## IPVstatus:Sex:PovStat:CES1 0.6218 0.6218 1 71.66 0.2722
## Age:Sex:PovStat:CES1 1.2502 1.2502 1 70.38 0.5489
## Sex:PovStat:CES1 0.3918 0.3918 1 59.24 0.1236
## Age:IPVstatus:PovStat:CES1 4.1623 4.1623 1 68.65 1.7443
## IPVstatus:PovStat:CES1 0.2163 0.2163 1 59.59 0.3298
## Age:IPVstatus:Sex:CES1 3.7006 3.7006 1 90.38 1.3498
## Age:IPVstatus:CES1 1.7794 1.7794 1 89.60 0.6619
## IPVstatus:Sex:CES1 2.1018 2.1018 1 53.37 0.7526
## IPVstatus:CES1 1.9566 1.9566 1 52.01 0.8689
## Age:IPVstatus:Sex:PovStat 5.6565 5.6565 1 88.19 2.7401
## Age:Sex:PovStat 2.0509 2.0509 1 84.48 0.0364
## Age:IPVstatus:Sex 0.0143 0.0143 1 93.96 0.2293
## Age:IPVstatus:PovStat 0.0488 0.0488 1 96.65 0.3286
## IPVstatus:Sex:PovStat 2.2915 2.2915 1 54.97 0.8698
## Sex:PovStat 0.0940 0.0940 1 58.36 0.2194
## IPVstatus:PovStat 3.5181 3.5181 1 55.35 0.5308
## Age:IPVstatus 1.6572 1.6572 1 102.92 2.4570
## Age:Sex:CES1 6.9881 6.9881 1 97.20 2.7627
## Sex:CES1 0.0856 0.0856 1 55.60 0.1405
## Age:Sex 0.0940 0.0940 1 103.92 0.1165
## Age:PovStat:CES1 7.5027 7.5027 1 112.57 2.6625
## Age:CES1 0.2429 0.2429 1 111.49 0.1554
## PovStat:CES1 3.9144 3.9144 1 54.81 3.0325
## CES1 0.6298 0.6298 1 55.72 0.0029
## Age 5.4731 5.4731 1 116.69 3.6119
## IPVstatus 1.3228 1.3228 1 56.25 1.5766
## Sex 4.2082 4.2082 1 57.40 3.9280
## PovStat 0.1299 0.1299 1 78.16 1.4353
## Age:PovStat 16.2250 16.2250 1 116.30 6.5051
## IPVstatus:Sex 17.3115 17.3115 1 56.24 8.1093
## elim.num Pr(>F)
## Age:IPVstatus:Sex:PovStat:CES1 1 0.9774
## IPVstatus:Sex:PovStat:CES1 2 0.6034
## Age:Sex:PovStat:CES1 3 0.4612
## Sex:PovStat:CES1 4 0.7264
## Age:IPVstatus:PovStat:CES1 5 0.1910
## IPVstatus:PovStat:CES1 6 0.5680
## Age:IPVstatus:Sex:CES1 7 0.2484
## Age:IPVstatus:CES1 8 0.4180
## IPVstatus:Sex:CES1 9 0.3895
## IPVstatus:CES1 10 0.3556
## Age:IPVstatus:Sex:PovStat 11 0.1014
## Age:Sex:PovStat 12 0.8491
## Age:IPVstatus:Sex 13 0.6332
## Age:IPVstatus:PovStat 14 0.5678
## IPVstatus:Sex:PovStat 15 0.3551
## Sex:PovStat 16 0.6412
## IPVstatus:PovStat 17 0.4694
## Age:IPVstatus 18 0.1201
## Age:Sex:CES1 19 0.0997
## Sex:CES1 20 0.7092
## Age:Sex 21 0.7335
## Age:PovStat:CES1 22 0.1055
## Age:CES1 23 0.6942
## PovStat:CES1 24 0.0872
## CES1 25 0.9573
## Age kept 0.0598
## IPVstatus kept 0.2144
## Sex kept 0.0523
## PovStat kept 0.2345
## Age:PovStat kept 0.0121
## IPVstatus:Sex kept 0.0061
##
## Least squares means:
## IPVstatus Sex PovStat Estimate Standard Error DF
## IPVstatus 0 1.0 NA NA 3.423 0.620 58.5
## IPVstatus 1 2.0 NA NA 4.689 0.845 57.0
## Sex Women NA 2.0 NA 3.045 0.707 56.3
## Sex Men NA 1.0 NA 5.067 0.782 59.6
## PovStat Above NA NA 1.0 4.215 0.608 57.2
## PovStat Below NA NA 2.0 3.897 0.873 59.5
## IPVstatus:Sex 0 Women 1.0 2.0 NA 3.854 0.820 56.5
## IPVstatus:Sex 1 Women 2.0 2.0 NA 2.236 1.109 56.2
## IPVstatus:Sex 0 Men 1.0 1.0 NA 2.992 0.880 59.0
## IPVstatus:Sex 1 Men 2.0 1.0 NA 7.142 1.259 57.4
## t-value Lower CI Upper CI p-value
## IPVstatus 0 5.52 2.1814 4.67 <2e-16
## IPVstatus 1 5.55 2.9972 6.38 <2e-16
## Sex Women 4.31 1.6291 4.46 0.0001
## Sex Men 6.48 3.5025 6.63 <2e-16
## PovStat Above 6.93 2.9965 5.43 <2e-16
## PovStat Below 4.47 2.1512 5.64 <2e-16
## IPVstatus:Sex 0 Women 4.70 2.2111 5.50 <2e-16
## IPVstatus:Sex 1 Women 2.02 0.0133 4.46 0.0487
## IPVstatus:Sex 0 Men 3.40 1.2325 4.75 0.0012
## IPVstatus:Sex 1 Men 5.67 4.6201 9.66 <2e-16
##
## Differences of LSMEANS:
## Estimate Standard Error DF t-value
## IPVstatus 0-1 -1.3 1.008 56.2 -1.26
## Sex Women-Men -2.0 1.020 57.4 -1.98
## PovStat Above-Below 0.3 1.040 58.7 0.31
## IPVstatus:Sex 0 Women- 1 Women 1.6 1.345 56.4 1.20
## IPVstatus:Sex 0 Women- 0 Men 0.9 1.163 57.0 0.74
## IPVstatus:Sex 0 Women- 1 Men -3.3 1.490 56.6 -2.21
## IPVstatus:Sex 1 Women- 0 Men -0.8 1.375 57.1 -0.55
## IPVstatus:Sex 1 Women- 1 Men -4.9 1.667 56.7 -2.94
## IPVstatus:Sex 0 Men- 1 Men -4.1 1.508 56.2 -2.75
## Lower CI Upper CI p-value
## IPVstatus 0-1 -3.28 0.7532 0.214
## Sex Women-Men -4.06 0.0207 0.052
## PovStat Above-Below -1.76 2.3990 0.761
## IPVstatus:Sex 0 Women- 1 Women -1.08 4.3129 0.234
## IPVstatus:Sex 0 Women- 0 Men -1.47 3.1911 0.462
## IPVstatus:Sex 0 Women- 1 Men -6.27 -0.3028 0.032
## IPVstatus:Sex 1 Women- 0 Men -3.51 1.9976 0.584
## IPVstatus:Sex 1 Women- 1 Men -8.25 -1.5668 0.005
## IPVstatus:Sex 0 Men- 1 Men -7.17 -1.1294 0.008
##
## 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$"TrailsB:A" ~ Age + IPVstatus + Sex + PovStat + (Age | HNDid) + (1 | subclass) + Age:PovStat + IPVstatus:Sex
## Data: IPVandCognitionDataSet2
## REML criterion at convergence: 606.6
## Random effects:
## Groups Name Std.Dev. Corr
## HNDid (Intercept) 4.88e+00
## Age 1.71e-01 0.95
## subclass (Intercept) 1.10e-06
## Residual 1.42e+00
## Number of obs: 126, groups: HNDid, 63; subclass, 21
## Fixed Effects:
## (Intercept) Age IPVstatus1
## 3.6907 -0.0347 -1.3308
## SexMen PovStatBelow Age:PovStatBelow
## -1.0897 1.8562 0.2881
## IPVstatus1:SexMen
## 6.5609
summary(mm2)
## Warning: number of observations <= rank(Z); variance-covariance matrix will be unidentifiable
## Warning: number of observations <= rank(Z); variance-covariance matrix will be unidentifiable
## Linear mixed model fit by REML ['merModLmerTest']
## Formula: IPVandCognitionDataSet2$"TrailsB:A" ~ Age + IPVstatus + Sex + PovStat + (Age | HNDid) + (1 | subclass) + Age:PovStat + IPVstatus:Sex
## Data: IPVandCognitionDataSet2
##
## REML criterion at convergence: 606.6
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## HNDid (Intercept) 2.39e+01 4.88e+00
## Age 2.94e-02 1.71e-01 0.95
## subclass (Intercept) 1.22e-12 1.10e-06
## Residual 2.02e+00 1.42e+00
## Number of obs: 126, groups: HNDid, 63; subclass, 21
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 3.6907 1.1101 53.9000 3.32 0.0016
## Age -0.0347 0.0608 48.2000 -0.57 0.5711
## IPVstatus1 -1.3308 1.2244 49.8000 -1.09 0.2823
## SexMen -1.0897 1.0537 46.0000 -1.03 0.3065
## PovStatBelow 1.8562 1.5362 24.7000 1.21 0.2384
## Age:PovStatBelow 0.2881 0.1033 36.9000 2.79 0.0083
## IPVstatus1:SexMen 6.5609 1.8723 47.8000 3.50 0.0010
##
## Correlation of Fixed Effects:
## (Intr) Age IPVst1 SexMen PvSttB Ag:PSB
## Age 0.739
## IPVstatus1 -0.423 -0.089
## SexMen -0.457 -0.031 0.375
## PovStatBelw -0.511 -0.512 0.043 0.002
## Ag:PvSttBlw -0.382 -0.585 0.010 -0.095 0.774
## IPVstts1:SM 0.304 0.060 -0.660 -0.569 -0.066 0.018
plot(st)
plot(mm2)