California Verbal Learning Test (Total Correct Trial A) - Psychological Aggression
Model 1
CVLTlog1 <- glm(PsychAggress ~ CVLtca, data=Allvars,family = "binomial")
summary(CVLTlog1)
##
## Call:
## glm(formula = PsychAggress ~ CVLtca, family = "binomial", data = Allvars)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.2397 0.4639 0.5139 0.5604 0.7019
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 1.27530 0.28686 4.446 8.76e-06 ***
## CVLtca 0.03103 0.01435 2.162 0.0306 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 505.40 on 640 degrees of freedom
## Residual deviance: 500.73 on 639 degrees of freedom
## AIC: 504.73
##
## Number of Fisher Scoring iterations: 4
confint(CVLTlog1)
## 2.5 % 97.5 %
## (Intercept) 0.726387845 1.85354798
## CVLtca 0.002905058 0.05927112
exp(cbind(OR = coef(CVLTlog1), confint(CVLTlog1)))
## OR 2.5 % 97.5 %
## (Intercept) 3.579779 2.067599 6.382424
## CVLtca 1.031512 1.002909 1.061063
plot(allEffects(CVLTlog1))
########Compare to null model
#Difference in Deviance
with(CVLTlog1,null.deviance - deviance)
## [1] 4.674706
#Degrees of freedom for the difference between two models
with(CVLTlog1,df.null - df.residual)
## [1] 1
#p-value
with(CVLTlog1,pchisq(null.deviance-deviance,df.null-df.residual,lower.tail=FALSE))
## [1] 0.03060994
Model 2
CVLTlog2 <- glm(PsychAggress ~ CVLtca + Age + Sex + PovStat + WRATtotal, data = Allvars, family = "binomial")
summary(CVLTlog2)
##
## Call:
## glm(formula = PsychAggress ~ CVLtca + Age + Sex + PovStat + WRATtotal,
## family = "binomial", data = Allvars)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.4013 0.4156 0.4938 0.5810 0.8756
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 1.69988 0.96538 1.761 0.0783 .
## CVLtca 0.01946 0.01611 1.208 0.2269
## Age -0.01842 0.01339 -1.375 0.1690
## SexMen -0.37455 0.24039 -1.558 0.1192
## PovStatBelow 0.33199 0.27315 1.215 0.2242
## WRATtotal 0.01787 0.01537 1.162 0.2450
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 505.40 on 640 degrees of freedom
## Residual deviance: 493.07 on 635 degrees of freedom
## AIC: 505.07
##
## Number of Fisher Scoring iterations: 5
confint(CVLTlog2)
## 2.5 % 97.5 %
## (Intercept) -0.17016237 3.622026457
## CVLtca -0.01231726 0.050931166
## Age -0.04485800 0.007752946
## SexMen -0.85096655 0.094038699
## PovStatBelow -0.18985492 0.885330955
## WRATtotal -0.01277707 0.047655994
exp(cbind(OR = coef(CVLTlog2), confint(CVLTlog2)))
## OR 2.5 % 97.5 %
## (Intercept) 5.4732962 0.8435278 37.413308
## CVLtca 1.0196525 0.9877583 1.052250
## Age 0.9817498 0.9561332 1.007783
## SexMen 0.6875991 0.4270020 1.098602
## PovStatBelow 1.3937334 0.8270791 2.423786
## WRATtotal 1.0180281 0.9873042 1.048810
########Compare to null model
#Difference in Deviance
with(CVLTlog2,null.deviance - deviance)
## [1] 12.33094
#Degrees of freedom for the difference between two models
with(CVLTlog2,df.null - df.residual)
## [1] 5
#p-value
with(CVLTlog2,pchisq(null.deviance-deviance,df.null-df.residual,lower.tail=FALSE))
## [1] 0.03052402
Model 3
CVLTlog3 <- glm(PsychAggress ~ (CVLtca + Sex + PovStat)^3 + Age + WRATtotal, data = Allvars, family = "binomial")
summary(CVLTlog3)
##
## Call:
## glm(formula = PsychAggress ~ (CVLtca + Sex + PovStat)^3 + Age +
## WRATtotal, family = "binomial", data = Allvars)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.3337 0.4129 0.4814 0.5690 0.9980
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 1.507921 1.041509 1.448 0.148
## CVLtca 0.032917 0.027323 1.205 0.228
## SexMen -0.490835 0.717735 -0.684 0.494
## PovStatBelow 1.064531 1.054751 1.009 0.313
## Age -0.018731 0.013525 -1.385 0.166
## WRATtotal 0.017976 0.015501 1.160 0.246
## CVLtca:SexMen 0.002083 0.035676 0.058 0.953
## CVLtca:PovStatBelow -0.047366 0.048002 -0.987 0.324
## SexMen:PovStatBelow 0.365349 1.389997 0.263 0.793
## CVLtca:SexMen:PovStatBelow -0.009220 0.068861 -0.134 0.893
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 505.40 on 640 degrees of freedom
## Residual deviance: 490.18 on 631 degrees of freedom
## AIC: 510.18
##
## Number of Fisher Scoring iterations: 5
confint(CVLTlog3)
## 2.5 % 97.5 %
## (Intercept) -0.49988121 3.595142572
## CVLtca -0.02103625 0.086641647
## SexMen -1.93022742 0.897900681
## PovStatBelow -0.91709632 3.263924014
## Age -0.04543934 0.007698432
## WRATtotal -0.01291089 0.048037702
## CVLtca:SexMen -0.06781452 0.072447800
## CVLtca:PovStatBelow -0.14316689 0.046005026
## SexMen:PovStatBelow -2.39711923 3.102579238
## CVLtca:SexMen:PovStatBelow -0.14502772 0.125832049
exp(cbind(OR = coef(CVLTlog3), confint(CVLTlog3)))
## OR 2.5 % 97.5 %
## (Intercept) 4.5173308 0.60660271 36.420892
## CVLtca 1.0334647 0.97918347 1.090506
## SexMen 0.6121152 0.14511519 2.454445
## PovStatBelow 2.8994777 0.39967789 26.151957
## Age 0.9814433 0.95557757 1.007728
## WRATtotal 1.0181383 0.98717210 1.049210
## CVLtca:SexMen 1.0020853 0.93443378 1.075137
## CVLtca:PovStatBelow 0.9537384 0.86660943 1.047080
## SexMen:PovStatBelow 1.4410172 0.09097967 22.255279
## CVLtca:SexMen:PovStatBelow 0.9908219 0.86499831 1.134092
########Compare to null model
#Difference in Deviance
with(CVLTlog3,null.deviance - deviance)
## [1] 15.22463
#Degrees of freedom for the difference between two models
with(CVLTlog3,df.null - df.residual)
## [1] 9
#p-value
with(CVLTlog3,pchisq(null.deviance-deviance,df.null-df.residual,lower.tail=FALSE))
## [1] 0.08494771
Compare Models 1,2, & 3
anova(CVLTlog1,CVLTlog2,test = "LR")
## Analysis of Deviance Table
##
## Model 1: PsychAggress ~ CVLtca
## Model 2: PsychAggress ~ CVLtca + Age + Sex + PovStat + WRATtotal
## Resid. Df Resid. Dev Df Deviance Pr(>Chi)
## 1 639 500.73
## 2 635 493.07 4 7.6562 0.105
anova(CVLTlog2,CVLTlog3,test = "LR")
## Analysis of Deviance Table
##
## Model 1: PsychAggress ~ CVLtca + Age + Sex + PovStat + WRATtotal
## Model 2: PsychAggress ~ (CVLtca + Sex + PovStat)^3 + Age + WRATtotal
## Resid. Df Resid. Dev Df Deviance Pr(>Chi)
## 1 635 493.07
## 2 631 490.18 4 2.8937 0.5758
anova(CVLTlog1,CVLTlog3,test = "LR")
## Analysis of Deviance Table
##
## Model 1: PsychAggress ~ CVLtca
## Model 2: PsychAggress ~ (CVLtca + Sex + PovStat)^3 + Age + WRATtotal
## Resid. Df Resid. Dev Df Deviance Pr(>Chi)
## 1 639 500.73
## 2 631 490.18 8 10.55 0.2285
Suggested Model by Predictors
anova(CVLTlog3, test="Chisq")
## Analysis of Deviance Table
##
## Model: binomial, link: logit
##
## Response: PsychAggress
##
## Terms added sequentially (first to last)
##
##
## Df Deviance Resid. Df Resid. Dev Pr(>Chi)
## NULL 640 505.40
## CVLtca 1 4.6747 639 500.73 0.03061 *
## Sex 1 2.5316 638 498.20 0.11159
## PovStat 1 1.9644 637 496.23 0.16104
## Age 1 1.8340 636 494.40 0.17566
## WRATtotal 1 1.3262 635 493.07 0.24947
## CVLtca:Sex 1 0.0025 634 493.07 0.95979
## CVLtca:PovStat 1 2.7495 633 490.32 0.09729 .
## Sex:PovStat 1 0.1237 632 490.19 0.72503
## CVLtca:Sex:PovStat 1 0.0179 631 490.18 0.89346
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
CVLTlog4 <- glm(PsychAggress ~ (CVLtca + PovStat)^2, data = Allvars, family = "binomial")
summary(CVLTlog4)
##
## Call:
## glm(formula = PsychAggress ~ (CVLtca + PovStat)^2, family = "binomial",
## data = Allvars)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.2600 0.4611 0.4842 0.5633 0.8407
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.85831 0.34111 2.516 0.0119 *
## CVLtca 0.04749 0.01718 2.765 0.0057 **
## PovStatBelow 1.30441 0.66183 1.971 0.0487 *
## CVLtca:PovStatBelow -0.05105 0.03263 -1.564 0.1178
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 505.40 on 640 degrees of freedom
## Residual deviance: 496.05 on 637 degrees of freedom
## AIC: 504.05
##
## Number of Fisher Scoring iterations: 4
plot(predictorEffect("CVLtca",CVLTlog4))
California Verbal Learning Test (Total Correct Trial A) - Physical Assault
Model 1
CVLTlog1 <- glm(PhysAssault ~ CVLtca, data=Allvars,family = "binomial")
summary(CVLTlog1)
##
## Call:
## glm(formula = PhysAssault ~ CVLtca, family = "binomial", data = Allvars)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.6067 -0.5309 -0.5087 -0.4873 2.1695
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -2.25338 0.33030 -6.822 8.97e-12 ***
## CVLtca 0.01521 0.01505 1.011 0.312
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 482.53 on 640 degrees of freedom
## Residual deviance: 481.50 on 639 degrees of freedom
## AIC: 485.5
##
## Number of Fisher Scoring iterations: 4
confint(CVLTlog1)
## 2.5 % 97.5 %
## (Intercept) -2.92543925 -1.62855334
## CVLtca -0.01407327 0.04501825
exp(cbind(OR = coef(CVLTlog1), confint(CVLTlog1)))
## OR 2.5 % 97.5 %
## (Intercept) 0.1050432 0.05364113 0.1962132
## CVLtca 1.0153312 0.98602529 1.0460469
plot(allEffects(CVLTlog1))
########Compare to null model
#Difference in Deviance
with(CVLTlog1,null.deviance - deviance)
## [1] 1.030224
#Degrees of freedom for the difference between two models
with(CVLTlog1,df.null - df.residual)
## [1] 1
#p-value
with(CVLTlog1,pchisq(null.deviance-deviance,df.null-df.residual,lower.tail=FALSE))
## [1] 0.3101061
Model 2
CVLTlog2 <- glm(PhysAssault ~ CVLtca + Age + Sex + PovStat + WRATtotal, data = Allvars, family = "binomial")
summary(CVLTlog2)
##
## Call:
## glm(formula = PhysAssault ~ CVLtca + Age + Sex + PovStat + WRATtotal,
## family = "binomial", data = Allvars)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.8203 -0.5516 -0.4716 -0.3932 2.3909
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.369388 1.063372 -1.288 0.1978
## CVLtca 0.003692 0.016615 0.222 0.8241
## Age -0.032616 0.014098 -2.313 0.0207 *
## SexMen -0.143840 0.249702 -0.576 0.5646
## PovStatBelow 0.494611 0.254675 1.942 0.0521 .
## WRATtotal 0.016375 0.017888 0.915 0.3600
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 482.53 on 640 degrees of freedom
## Residual deviance: 469.38 on 635 degrees of freedom
## AIC: 481.38
##
## Number of Fisher Scoring iterations: 5
confint(CVLTlog2)
## 2.5 % 97.5 %
## (Intercept) -3.491971077 0.685248919
## CVLtca -0.028593810 0.036641979
## Age -0.060680847 -0.005287059
## SexMen -0.637616292 0.344169067
## PovStatBelow -0.009561475 0.991649370
## WRATtotal -0.017785200 0.052499542
exp(cbind(OR = coef(CVLTlog2), confint(CVLTlog2)))
## OR 2.5 % 97.5 %
## (Intercept) 0.2542626 0.03044081 1.9842657
## CVLtca 1.0036992 0.97181112 1.0373216
## Age 0.9679098 0.94112355 0.9947269
## SexMen 0.8660259 0.52855083 1.4108171
## PovStatBelow 1.6398605 0.99048409 2.6956770
## WRATtotal 1.0165096 0.98237202 1.0539021
########Compare to null model
#Difference in Deviance
with(CVLTlog2,null.deviance - deviance)
## [1] 13.15643
#Degrees of freedom for the difference between two models
with(CVLTlog2,df.null - df.residual)
## [1] 5
#p-value
with(CVLTlog2,pchisq(null.deviance-deviance,df.null-df.residual,lower.tail=FALSE))
## [1] 0.02195614
Model 3
CVLTlog3 <- glm(PhysAssault ~ (CVLtca + Sex + PovStat)^3 + Age + WRATtotal, data = Allvars, family = "binomial")
summary(CVLTlog3)
##
## Call:
## glm(formula = PhysAssault ~ (CVLtca + Sex + PovStat)^3 + Age +
## WRATtotal, family = "binomial", data = Allvars)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.8807 -0.5521 -0.4699 -0.3868 2.3692
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.56944 1.17685 -1.334 0.1823
## CVLtca 0.01507 0.02827 0.533 0.5939
## SexMen 0.31859 0.90423 0.352 0.7246
## PovStatBelow 1.19628 0.93802 1.275 0.2022
## Age -0.03285 0.01415 -2.321 0.0203 *
## WRATtotal 0.01566 0.01802 0.869 0.3847
## CVLtca:SexMen -0.02338 0.04019 -0.582 0.5608
## CVLtca:PovStatBelow -0.03456 0.04091 -0.845 0.3982
## SexMen:PovStatBelow -1.59191 1.38680 -1.148 0.2510
## CVLtca:SexMen:PovStatBelow 0.08675 0.06569 1.321 0.1867
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 482.53 on 640 degrees of freedom
## Residual deviance: 467.48 on 631 degrees of freedom
## AIC: 487.48
##
## Number of Fisher Scoring iterations: 5
confint(CVLTlog3)
## 2.5 % 97.5 %
## (Intercept) -3.94186703 0.682851594
## CVLtca -0.03911156 0.072130045
## SexMen -1.45623021 2.114702092
## PovStatBelow -0.64898891 3.058400756
## Age -0.06103090 -0.005420145
## WRATtotal -0.01877807 0.052007524
## CVLtca:SexMen -0.10277124 0.055227806
## CVLtca:PovStatBelow -0.11546733 0.045572506
## SexMen:PovStatBelow -4.37034666 1.095833308
## CVLtca:SexMen:PovStatBelow -0.04073430 0.217556779
exp(cbind(OR = coef(CVLTlog3), confint(CVLTlog3)))
## OR 2.5 % 97.5 %
## (Intercept) 0.2081609 0.01941194 1.9795145
## CVLtca 1.0151877 0.96164343 1.0747951
## SexMen 1.3751903 0.23311341 8.2871166
## PovStatBelow 3.3077874 0.52257388 21.2934765
## Age 0.9676869 0.94079417 0.9945945
## WRATtotal 1.0157839 0.98139714 1.0533837
## CVLtca:SexMen 0.9768927 0.90233337 1.0567813
## CVLtca:PovStatBelow 0.9660316 0.89094968 1.0466269
## SexMen:PovStatBelow 0.2035359 0.01264686 2.9916746
## CVLtca:SexMen:PovStatBelow 1.0906242 0.96008419 1.2430360
########Compare to null model
#Difference in Deviance
with(CVLTlog3,null.deviance - deviance)
## [1] 15.04792
#Degrees of freedom for the difference between two models
with(CVLTlog3,df.null - df.residual)
## [1] 9
#p-value
with(CVLTlog3,pchisq(null.deviance-deviance,df.null-df.residual,lower.tail=FALSE))
## [1] 0.08962819
Compare Models 1,2, & 3
anova(CVLTlog1,CVLTlog2,test = "LR")
## Analysis of Deviance Table
##
## Model 1: PhysAssault ~ CVLtca
## Model 2: PhysAssault ~ CVLtca + Age + Sex + PovStat + WRATtotal
## Resid. Df Resid. Dev Df Deviance Pr(>Chi)
## 1 639 481.50
## 2 635 469.38 4 12.126 0.01644 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
anova(CVLTlog2,CVLTlog3,test = "LR")
## Analysis of Deviance Table
##
## Model 1: PhysAssault ~ CVLtca + Age + Sex + PovStat + WRATtotal
## Model 2: PhysAssault ~ (CVLtca + Sex + PovStat)^3 + Age + WRATtotal
## Resid. Df Resid. Dev Df Deviance Pr(>Chi)
## 1 635 469.38
## 2 631 467.48 4 1.8915 0.7557
anova(CVLTlog1,CVLTlog3,test = "LR")
## Analysis of Deviance Table
##
## Model 1: PhysAssault ~ CVLtca
## Model 2: PhysAssault ~ (CVLtca + Sex + PovStat)^3 + Age + WRATtotal
## Resid. Df Resid. Dev Df Deviance Pr(>Chi)
## 1 639 481.50
## 2 631 467.48 8 14.018 0.08131 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Suggested Model by Predictors
anova(CVLTlog3, test="Chisq")
## Analysis of Deviance Table
##
## Model: binomial, link: logit
##
## Response: PhysAssault
##
## Terms added sequentially (first to last)
##
##
## Df Deviance Resid. Df Resid. Dev Pr(>Chi)
## NULL 640 482.53
## CVLtca 1 1.0302 639 481.50 0.31011
## Sex 1 0.4987 638 481.00 0.48006
## PovStat 1 5.3563 637 475.65 0.02065 *
## Age 1 5.4114 636 470.24 0.02001 *
## WRATtotal 1 0.8598 635 469.38 0.35379
## CVLtca:Sex 1 0.0796 634 469.30 0.77790
## CVLtca:PovStat 1 0.0033 633 469.29 0.95451
## Sex:PovStat 1 0.0390 632 469.25 0.84345
## CVLtca:Sex:PovStat 1 1.7697 631 467.48 0.18342
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
CVLTlog4 <- glm(PsychAggress ~ PovStat + Age, data = Allvars, family = "binomial")
summary(CVLTlog4)
##
## Call:
## glm(formula = PsychAggress ~ PovStat + Age, family = "binomial",
## data = Allvars)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.2470 0.4626 0.5111 0.5704 0.6795
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 2.93498 0.64793 4.530 5.9e-06 ***
## PovStatBelow 0.24765 0.26716 0.927 0.3539
## Age -0.02408 0.01292 -1.864 0.0623 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 505.4 on 640 degrees of freedom
## Residual deviance: 500.3 on 638 degrees of freedom
## AIC: 506.3
##
## Number of Fisher Scoring iterations: 4
California Verbal Learning Test (Long Delayed Free Recall) - Psychological Aggression
Model 1
CVLTlog1 <- glm(PsychAggress ~ CVLfrl, data=Allvars,family = "binomial")
summary(CVLTlog1)
##
## Call:
## glm(formula = PsychAggress ~ CVLfrl, family = "binomial", data = Allvars)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.4382 0.4088 0.5132 0.5737 0.7125
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 1.24154 0.20846 5.956 2.59e-09 ***
## CVLfrl 0.11988 0.03651 3.284 0.00102 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 505.40 on 640 degrees of freedom
## Residual deviance: 494.01 on 639 degrees of freedom
## AIC: 498.01
##
## Number of Fisher Scoring iterations: 5
confint(CVLTlog1)
## 2.5 % 97.5 %
## (Intercept) 0.84167993 1.6603766
## CVLfrl 0.04955222 0.1929286
exp(cbind(OR = coef(CVLTlog1), confint(CVLTlog1)))
## OR 2.5 % 97.5 %
## (Intercept) 3.460942 2.320262 5.261292
## CVLfrl 1.127367 1.050800 1.212796
plot(allEffects(CVLTlog1))
########Compare to null model
#Difference in Deviance
with(CVLTlog1,null.deviance - deviance)
## [1] 11.39152
#Degrees of freedom for the difference between two models
with(CVLTlog1,df.null - df.residual)
## [1] 1
#p-value
with(CVLTlog1,pchisq(null.deviance-deviance,df.null-df.residual,lower.tail=FALSE))
## [1] 0.0007377998
Model 2
CVLTlog2 <- glm(PsychAggress ~ CVLfrl + Age + Sex + PovStat + WRATtotal, data = Allvars, family = "binomial")
summary(CVLTlog2)
##
## Call:
## glm(formula = PsychAggress ~ CVLfrl + Age + Sex + PovStat + WRATtotal,
## family = "binomial", data = Allvars)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.4604 0.3885 0.4867 0.5876 0.8772
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 1.48555 0.95470 1.556 0.1197
## CVLfrl 0.09764 0.03976 2.456 0.0141 *
## Age -0.01359 0.01351 -1.006 0.3143
## SexMen -0.32575 0.24041 -1.355 0.1754
## PovStatBelow 0.34545 0.27276 1.266 0.2053
## WRATtotal 0.01367 0.01512 0.904 0.3662
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 505.40 on 640 degrees of freedom
## Residual deviance: 488.38 on 635 degrees of freedom
## AIC: 500.38
##
## Number of Fisher Scoring iterations: 5
confint(CVLTlog2)
## 2.5 % 97.5 %
## (Intercept) -0.36542427 3.38494073
## CVLfrl 0.02029872 0.17648432
## Age -0.04025046 0.01282705
## SexMen -0.80195754 0.14310289
## PovStatBelow -0.17561150 0.89805025
## WRATtotal -0.01652091 0.04294846
exp(cbind(OR = coef(CVLTlog2), confint(CVLTlog2)))
## OR 2.5 % 97.5 %
## (Intercept) 4.4174118 0.6939022 29.516243
## CVLfrl 1.1025658 1.0205061 1.193016
## Age 0.9864970 0.9605488 1.012910
## SexMen 0.7219892 0.4484502 1.153849
## PovStatBelow 1.4126221 0.8389438 2.454812
## WRATtotal 1.0137609 0.9836148 1.043884
plot(predictorEffect("CVLfrl",CVLTlog2))
########Compare to null model
#Difference in Deviance
with(CVLTlog2,null.deviance - deviance)
## [1] 17.02069
#Degrees of freedom for the difference between two models
with(CVLTlog2,df.null - df.residual)
## [1] 5
#p-value
with(CVLTlog2,pchisq(null.deviance-deviance,df.null-df.residual,lower.tail=FALSE))
## [1] 0.004460721
Model 3
CVLTlog3 <- glm(PsychAggress ~ (CVLfrl + Sex + PovStat)^3 + Age + WRATtotal, data = Allvars, family = "binomial")
summary(CVLTlog3)
##
## Call:
## glm(formula = PsychAggress ~ (CVLfrl + Sex + PovStat)^3 + Age +
## WRATtotal, family = "binomial", data = Allvars)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.4092 0.3891 0.4796 0.5819 0.9066
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 1.594364 0.995644 1.601 0.109
## CVLfrl 0.081954 0.064865 1.263 0.206
## SexMen -0.539601 0.519371 -1.039 0.299
## PovStatBelow 0.034576 0.728639 0.047 0.962
## Age -0.013555 0.013553 -1.000 0.317
## WRATtotal 0.014485 0.015217 0.952 0.341
## CVLfrl:SexMen 0.020872 0.087237 0.239 0.811
## CVLfrl:PovStatBelow 0.019247 0.118391 0.163 0.871
## SexMen:PovStatBelow 0.403466 0.951778 0.424 0.672
## CVLfrl:SexMen:PovStatBelow 0.007243 0.175255 0.041 0.967
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 505.4 on 640 degrees of freedom
## Residual deviance: 487.7 on 631 degrees of freedom
## AIC: 507.7
##
## Number of Fisher Scoring iterations: 5
confint(CVLTlog3)
## 2.5 % 97.5 %
## (Intercept) -0.33012860 3.58191681
## CVLfrl -0.04333937 0.21245834
## SexMen -1.58108983 0.46411696
## PovStatBelow -1.35788726 1.53077967
## Age -0.04029900 0.01294495
## WRATtotal -0.01586669 0.04396449
## CVLfrl:SexMen -0.15057415 0.19264865
## CVLfrl:PovStatBelow -0.21063952 0.25722477
## SexMen:PovStatBelow -1.48466750 2.27285707
## CVLfrl:SexMen:PovStatBelow -0.33464295 0.35634530
exp(cbind(OR = coef(CVLTlog3), confint(CVLTlog3)))
## OR 2.5 % 97.5 %
## (Intercept) 4.9251943 0.7188313 35.942369
## CVLfrl 1.0854060 0.9575864 1.236715
## SexMen 0.5829807 0.2057507 1.590609
## PovStatBelow 1.0351811 0.2572036 4.621779
## Age 0.9865361 0.9605022 1.013029
## WRATtotal 1.0145904 0.9842585 1.044945
## CVLfrl:SexMen 1.0210918 0.8602139 1.212457
## CVLfrl:PovStatBelow 1.0194336 0.8100660 1.293336
## SexMen:PovStatBelow 1.4970042 0.2265777 9.707095
## CVLfrl:SexMen:PovStatBelow 1.0072689 0.7155935 1.428101
########Compare to null model
#Difference in Deviance
with(CVLTlog3,null.deviance - deviance)
## [1] 17.70202
#Degrees of freedom for the difference between two models
with(CVLTlog3,df.null - df.residual)
## [1] 9
#p-value
with(CVLTlog3,pchisq(null.deviance-deviance,df.null-df.residual,lower.tail=FALSE))
## [1] 0.03879225
Compare Models 1,2, & 3
anova(CVLTlog1,CVLTlog2,test = "LR")
## Analysis of Deviance Table
##
## Model 1: PsychAggress ~ CVLfrl
## Model 2: PsychAggress ~ CVLfrl + Age + Sex + PovStat + WRATtotal
## Resid. Df Resid. Dev Df Deviance Pr(>Chi)
## 1 639 494.01
## 2 635 488.38 4 5.6292 0.2286
anova(CVLTlog2,CVLTlog3,test = "LR")
## Analysis of Deviance Table
##
## Model 1: PsychAggress ~ CVLfrl + Age + Sex + PovStat + WRATtotal
## Model 2: PsychAggress ~ (CVLfrl + Sex + PovStat)^3 + Age + WRATtotal
## Resid. Df Resid. Dev Df Deviance Pr(>Chi)
## 1 635 488.38
## 2 631 487.70 4 0.68133 0.9536
anova(CVLTlog1,CVLTlog3,test = "LR")
## Analysis of Deviance Table
##
## Model 1: PsychAggress ~ CVLfrl
## Model 2: PsychAggress ~ (CVLfrl + Sex + PovStat)^3 + Age + WRATtotal
## Resid. Df Resid. Dev Df Deviance Pr(>Chi)
## 1 639 494.01
## 2 631 487.70 8 6.3105 0.6125
Suggested Model by Predictors
anova(CVLTlog3, test="Chisq")
## Analysis of Deviance Table
##
## Model: binomial, link: logit
##
## Response: PsychAggress
##
## Terms added sequentially (first to last)
##
##
## Df Deviance Resid. Df Resid. Dev Pr(>Chi)
## NULL 640 505.40
## CVLfrl 1 11.3915 639 494.01 0.0007378 ***
## Sex 1 1.9347 638 492.08 0.1642450
## PovStat 1 1.9131 637 490.16 0.1666241
## Age 1 0.9772 636 489.18 0.3228861
## WRATtotal 1 0.8042 635 488.38 0.3698439
## CVLfrl:Sex 1 0.0535 634 488.33 0.8170136
## CVLfrl:PovStat 1 0.0071 633 488.32 0.9330413
## Sex:PovStat 1 0.6190 632 487.70 0.4314100
## CVLfrl:Sex:PovStat 1 0.0017 631 487.70 0.9670288
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
CVLTlog4 <- glm(PsychAggress ~ CVLfrl, data = Allvars, family = "binomial")
summary(CVLTlog4)
##
## Call:
## glm(formula = PsychAggress ~ CVLfrl, family = "binomial", data = Allvars)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.4382 0.4088 0.5132 0.5737 0.7125
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 1.24154 0.20846 5.956 2.59e-09 ***
## CVLfrl 0.11988 0.03651 3.284 0.00102 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 505.40 on 640 degrees of freedom
## Residual deviance: 494.01 on 639 degrees of freedom
## AIC: 498.01
##
## Number of Fisher Scoring iterations: 5
plot(allEffects(CVLTlog3))
California Verbal Learning Test (Long Delayed Free Recall) - Physical Assault
Model 1
CVLTlog1 <- glm(PhysAssault ~ CVLfrl, data=Allvars,family = "binomial")
summary(CVLTlog1)
##
## Call:
## glm(formula = PhysAssault ~ CVLfrl, family = "binomial", data = Allvars)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.6037 -0.5267 -0.5088 -0.4830 2.1318
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -2.16353 0.24189 -8.944 <2e-16 ***
## CVLfrl 0.03691 0.03500 1.055 0.292
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 482.53 on 640 degrees of freedom
## Residual deviance: 481.42 on 639 degrees of freedom
## AIC: 485.42
##
## Number of Fisher Scoring iterations: 4
confint(CVLTlog1)
## 2.5 % 97.5 %
## (Intercept) -2.65528125 -1.7053017
## CVLfrl -0.03197077 0.1055069
exp(cbind(OR = coef(CVLTlog1), confint(CVLTlog1)))
## OR 2.5 % 97.5 %
## (Intercept) 0.1149186 0.07027907 0.1817175
## CVLfrl 1.0375994 0.96853490 1.1112738
########Compare to null model
#Difference in Deviance
with(CVLTlog1,null.deviance - deviance)
## [1] 1.108404
#Degrees of freedom for the difference between two models
with(CVLTlog1,df.null - df.residual)
## [1] 1
#p-value
with(CVLTlog1,pchisq(null.deviance-deviance,df.null-df.residual,lower.tail=FALSE))
## [1] 0.2924291
Model 2
CVLTlog2 <- glm(PhysAssault ~ CVLfrl + Age + Sex + PovStat + WRATtotal, data = Allvars, family = "binomial")
summary(CVLTlog2)
##
## Call:
## glm(formula = PhysAssault ~ CVLfrl + Age + Sex + PovStat + WRATtotal,
## family = "binomial", data = Allvars)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.8222 -0.5505 -0.4723 -0.3947 2.3860
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.326022 1.048693 -1.264 0.2061
## CVLfrl 0.003212 0.038750 0.083 0.9339
## Age -0.033009 0.014255 -2.316 0.0206 *
## SexMen -0.151689 0.248636 -0.610 0.5418
## PovStatBelow 0.489547 0.253616 1.930 0.0536 .
## WRATtotal 0.017181 0.017742 0.968 0.3329
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 482.53 on 640 degrees of freedom
## Residual deviance: 469.42 on 635 degrees of freedom
## AIC: 481.42
##
## Number of Fisher Scoring iterations: 5
confint(CVLTlog2)
## 2.5 % 97.5 %
## (Intercept) -3.41939734 0.700297485
## CVLfrl -0.07270854 0.079504533
## Age -0.06136406 -0.005354469
## SexMen -0.64344728 0.334149873
## PovStatBelow -0.01264032 0.984415506
## WRATtotal -0.01669413 0.053017533
exp(cbind(OR = coef(CVLTlog2), confint(CVLTlog2)))
## OR 2.5 % 97.5 %
## (Intercept) 0.2655314 0.03273216 2.0143519
## CVLfrl 1.0032176 0.92987181 1.0827505
## Age 0.9675298 0.94048079 0.9946598
## SexMen 0.8592554 0.52547783 1.3967525
## PovStatBelow 1.6315765 0.98743923 2.6762472
## WRATtotal 1.0173294 0.98344444 1.0544481
########Compare to null model
#Difference in Deviance
with(CVLTlog2,null.deviance - deviance)
## [1] 13.1138
#Degrees of freedom for the difference between two models
with(CVLTlog2,df.null - df.residual)
## [1] 5
#p-value
with(CVLTlog2,pchisq(null.deviance-deviance,df.null-df.residual,lower.tail=FALSE))
## [1] 0.02233536
Model 3
CVLTlog3 <- glm(PhysAssault ~ (CVLfrl + Sex + PovStat)^3 + Age + WRATtotal, data = Allvars, family = "binomial")
summary(CVLTlog3)
##
## Call:
## glm(formula = PhysAssault ~ (CVLfrl + Sex + PovStat)^3 + Age +
## WRATtotal, family = "binomial", data = Allvars)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.9608 -0.5456 -0.4611 -0.3756 2.3880
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.54116 1.11091 -1.387 0.1654
## CVLfrl 0.05847 0.06311 0.926 0.3542
## SexMen 0.16453 0.66352 0.248 0.8042
## PovStatBelow 1.74987 0.69446 2.520 0.0117 *
## Age -0.03334 0.01429 -2.333 0.0196 *
## WRATtotal 0.01402 0.01794 0.782 0.4345
## CVLfrl:SexMen -0.04768 0.09093 -0.524 0.6000
## CVLfrl:PovStatBelow -0.21185 0.10025 -2.113 0.0346 *
## SexMen:PovStatBelow -1.68383 1.02211 -1.647 0.0995 .
## CVLfrl:SexMen:PovStatBelow 0.30140 0.15435 1.953 0.0509 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 482.53 on 640 degrees of freedom
## Residual deviance: 463.48 on 631 degrees of freedom
## AIC: 483.48
##
## Number of Fisher Scoring iterations: 5
confint(CVLTlog3)
## 2.5 % 97.5 %
## (Intercept) -3.768138754 0.596098027
## CVLfrl -0.063914094 0.184956139
## SexMen -1.134460897 1.487606633
## PovStatBelow 0.394379812 3.136825838
## Age -0.061778689 -0.005627172
## WRATtotal -0.020308030 0.050188403
## CVLfrl:SexMen -0.228410112 0.129351707
## CVLfrl:PovStatBelow -0.412916523 -0.018096870
## SexMen:PovStatBelow -3.734693589 0.293286423
## CVLfrl:SexMen:PovStatBelow 0.001638781 0.608694441
exp(cbind(OR = coef(CVLTlog2), confint(CVLTlog3)))
## Warning in cbind(OR = coef(CVLTlog2), confint(CVLTlog3)): number of rows of
## result is not a multiple of vector length (arg 1)
## OR 2.5 % 97.5 %
## (Intercept) 0.2655314 0.02309501 1.8150228
## CVLfrl 1.0032176 0.93808558 1.2031657
## SexMen 0.9675298 0.32159545 4.4264886
## PovStatBelow 0.8592554 1.48346388 23.0306477
## Age 1.6315765 0.94009092 0.9943886
## WRATtotal 1.0173294 0.97989679 1.0514692
## CVLfrl:SexMen 0.2655314 0.79579783 1.1380903
## CVLfrl:PovStatBelow 1.0032176 0.66171752 0.9820659
## SexMen:PovStatBelow 0.9675298 0.02388049 1.3408268
## CVLfrl:SexMen:PovStatBelow 0.8592554 1.00164012 1.8380302
########Compare to null model
#Difference in Deviance
with(CVLTlog3,null.deviance - deviance)
## [1] 19.05303
#Degrees of freedom for the difference between two models
with(CVLTlog3,df.null - df.residual)
## [1] 9
#p-value
with(CVLTlog3,pchisq(null.deviance-deviance,df.null-df.residual,lower.tail=FALSE))
## [1] 0.02474572
interact_plot(model = CVLTlog3, pred = CVLfrl, modx = PovStat)
sim_slopes(CVLTlog3, pred = CVLfrl, modx = PovStat, centered = "all",jnplot = TRUE)
## Warning: Johnson-Neyman intervals are not available for factor moderators.
## SIMPLE SLOPES ANALYSIS
##
## Slope of CVLfrl when PovStat = Below:
##
## Est. S.E. z val. p
## ------- ------ -------- ------
## -0.03 0.06 -0.48 0.63
##
## Slope of CVLfrl when PovStat = Above:
##
## Est. S.E. z val. p
## ------ ------ -------- ------
## 0.04 0.05 0.74 0.46
Compare Models 1,2, & 3
anova(CVLTlog1,CVLTlog2,test = "LR")
## Analysis of Deviance Table
##
## Model 1: PhysAssault ~ CVLfrl
## Model 2: PhysAssault ~ CVLfrl + Age + Sex + PovStat + WRATtotal
## Resid. Df Resid. Dev Df Deviance Pr(>Chi)
## 1 639 481.42
## 2 635 469.42 4 12.005 0.01731 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
anova(CVLTlog2,CVLTlog3,test = "LR")
## Analysis of Deviance Table
##
## Model 1: PhysAssault ~ CVLfrl + Age + Sex + PovStat + WRATtotal
## Model 2: PhysAssault ~ (CVLfrl + Sex + PovStat)^3 + Age + WRATtotal
## Resid. Df Resid. Dev Df Deviance Pr(>Chi)
## 1 635 469.42
## 2 631 463.48 4 5.9392 0.2037
anova(CVLTlog1,CVLTlog3,test = "LR")
## Analysis of Deviance Table
##
## Model 1: PhysAssault ~ CVLfrl
## Model 2: PhysAssault ~ (CVLfrl + Sex + PovStat)^3 + Age + WRATtotal
## Resid. Df Resid. Dev Df Deviance Pr(>Chi)
## 1 639 481.42
## 2 631 463.48 8 17.945 0.02165 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Suggested Model by Predictors
anova(CVLTlog3, test="Chisq")
## Analysis of Deviance Table
##
## Model: binomial, link: logit
##
## Response: PhysAssault
##
## Terms added sequentially (first to last)
##
##
## Df Deviance Resid. Df Resid. Dev Pr(>Chi)
## NULL 640 482.53
## CVLfrl 1 1.1084 639 481.42 0.29243
## Sex 1 0.4968 638 480.93 0.48093
## PovStat 1 5.1657 637 475.76 0.02304 *
## Age 1 5.3792 636 470.38 0.02038 *
## WRATtotal 1 0.9638 635 469.42 0.32624
## CVLfrl:Sex 1 0.7056 634 468.71 0.40092
## CVLfrl:PovStat 1 1.3469 633 467.37 0.24582
## Sex:PovStat 1 0.0029 632 467.36 0.95673
## CVLfrl:Sex:PovStat 1 3.8838 631 463.48 0.04875 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
CVLTlog4 <- glm(PhysAssault ~ (CVLfrl + Sex + PovStat)^3 + Age, data = Allvars, family = "binomial")
summary(CVLTlog4)
##
## Call:
## glm(formula = PhysAssault ~ (CVLfrl + Sex + PovStat)^3 + Age,
## family = "binomial", data = Allvars)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.9864 -0.5483 -0.4612 -0.3796 2.3782
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.99969 0.86316 -1.158 0.2468
## CVLfrl 0.06886 0.06197 1.111 0.2665
## SexMen 0.19959 0.66423 0.300 0.7638
## PovStatBelow 1.76865 0.69517 2.544 0.0110 *
## Age -0.03318 0.01431 -2.319 0.0204 *
## CVLfrl:SexMen -0.04885 0.09132 -0.535 0.5927
## CVLfrl:PovStatBelow -0.21716 0.10022 -2.167 0.0302 *
## SexMen:PovStatBelow -1.76553 1.02113 -1.729 0.0838 .
## CVLfrl:SexMen:PovStatBelow 0.31076 0.15463 2.010 0.0445 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 482.53 on 640 degrees of freedom
## Residual deviance: 464.10 on 632 degrees of freedom
## AIC: 482.1
##
## Number of Fisher Scoring iterations: 5
sim_slopes(CVLTlog4, pred = CVLfrl, modx = Sex,mod2 = PovStat,centered = "all",jnplot = TRUE)
## Warning: Johnson-Neyman intervals are not available for factor moderators.
## █████████████████████ While PovStat (2nd moderator) = Above ████████████████████
##
## SIMPLE SLOPES ANALYSIS
##
## Slope of CVLfrl when Sex = Women:
##
## Est. S.E. z val. p
## ------ ------ -------- ------
## 0.07 0.06 1.11 0.27
##
## Slope of CVLfrl when Sex = Men:
##
## Est. S.E. z val. p
## ------ ------ -------- ------
## 0.02 0.07 0.29 0.77
##
## █████████████████████ While PovStat (2nd moderator) = Below ████████████████████
##
## SIMPLE SLOPES ANALYSIS
##
## Slope of CVLfrl when Sex = Women:
##
## Est. S.E. z val. p
## ------- ------ -------- ------
## -0.15 0.08 -1.85 0.06
##
## Slope of CVLfrl when Sex = Men:
##
## Est. S.E. z val. p
## ------ ------ -------- ------
## 0.11 0.10 1.17 0.24
##
## NULL
California Verbal Learning Test (Short Delayed Free Recall) - Psychological Aggression
Model 1
CVLTlog1 <- glm(PsychAggress ~ CVLfrs, data=Allvars,family = "binomial")
summary(CVLTlog1)
##
## Call:
## glm(formula = PsychAggress ~ CVLfrs, family = "binomial", data = Allvars)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.3304 0.4406 0.5232 0.5693 0.6717
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 1.37418 0.21530 6.383 1.74e-10 ***
## CVLfrs 0.09092 0.03595 2.529 0.0114 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 505.40 on 640 degrees of freedom
## Residual deviance: 498.81 on 639 degrees of freedom
## AIC: 502.81
##
## Number of Fisher Scoring iterations: 4
confint(CVLTlog1)
## 2.5 % 97.5 %
## (Intercept) 0.96232301 1.8079210
## CVLfrs 0.02130092 0.1624842
exp(cbind(OR = coef(CVLTlog1), confint(CVLTlog1)))
## OR 2.5 % 97.5 %
## (Intercept) 3.951827 2.617771 6.097757
## CVLfrs 1.095180 1.021529 1.176430
plot(allEffects(CVLTlog1))
########Compare to null model
#Difference in Deviance
with(CVLTlog1,null.deviance - deviance)
## [1] 6.590249
#Degrees of freedom for the difference between two models
with(CVLTlog1,df.null - df.residual)
## [1] 1
#p-value
with(CVLTlog1,pchisq(null.deviance-deviance,df.null-df.residual,lower.tail=FALSE))
## [1] 0.01025388
Model 2
CVLTlog2 <- glm(PsychAggress ~ CVLfrs + Age + Sex + PovStat + WRATtotal, data = Allvars, family = "binomial")
summary(CVLTlog2)
##
## Call:
## glm(formula = PsychAggress ~ CVLfrs + Age + Sex + PovStat + WRATtotal,
## family = "binomial", data = Allvars)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.4022 0.4033 0.4940 0.5796 0.8528
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 1.68965 0.95005 1.778 0.0753 .
## CVLfrs 0.06416 0.03919 1.637 0.1016
## Age -0.01655 0.01349 -1.227 0.2198
## SexMen -0.37563 0.23872 -1.574 0.1156
## PovStatBelow 0.32174 0.27222 1.182 0.2372
## WRATtotal 0.01668 0.01517 1.100 0.2715
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 505.40 on 640 degrees of freedom
## Residual deviance: 491.82 on 635 degrees of freedom
## AIC: 503.82
##
## Number of Fisher Scoring iterations: 5
confint(CVLTlog2)
## 2.5 % 97.5 %
## (Intercept) -0.15074295 3.581577715
## CVLfrs -0.01239184 0.141526010
## Age -0.04317355 0.009825015
## SexMen -0.84877074 0.089687004
## PovStatBelow -0.19825756 0.873317859
## WRATtotal -0.01356017 0.046079222
exp(cbind(OR = coef(CVLTlog2), confint(CVLTlog2)))
## OR 2.5 % 97.5 %
## (Intercept) 5.4175632 0.8600688 35.930184
## CVLfrs 1.0662659 0.9876846 1.152030
## Age 0.9835833 0.9577452 1.009873
## SexMen 0.6868532 0.4279407 1.093832
## PovStatBelow 1.3795323 0.8201586 2.394843
## WRATtotal 1.0168195 0.9865314 1.047157
########Compare to null model
#Difference in Deviance
with(CVLTlog2,null.deviance - deviance)
## [1] 13.57884
#Degrees of freedom for the difference between two models
with(CVLTlog2,df.null - df.residual)
## [1] 5
#p-value
with(CVLTlog2,pchisq(null.deviance-deviance,df.null-df.residual,lower.tail=FALSE))
## [1] 0.01851807
Model 3
CVLTlog3 <- glm(PsychAggress ~ (CVLfrs + Sex + PovStat)^3 + Age + WRATtotal, data = Allvars, family = "binomial")
summary(CVLTlog3)
##
## Call:
## glm(formula = PsychAggress ~ (CVLfrs + Sex + PovStat)^3 + Age +
## WRATtotal, family = "binomial", data = Allvars)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.4191 0.4137 0.4819 0.5750 0.8827
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 1.746423 0.985751 1.772 0.0764 .
## CVLfrs 0.061861 0.064502 0.959 0.3375
## SexMen -0.521850 0.526470 -0.991 0.3216
## PovStatBelow 0.451159 0.777362 0.580 0.5617
## Age -0.016645 0.013563 -1.227 0.2197
## WRATtotal 0.017110 0.015252 1.122 0.2619
## CVLfrs:SexMen 0.009237 0.085199 0.108 0.9137
## CVLfrs:PovStatBelow -0.058364 0.117321 -0.497 0.6189
## SexMen:PovStatBelow -0.178900 1.012604 -0.177 0.8598
## CVLfrs:SexMen:PovStatBelow 0.114186 0.174686 0.654 0.5133
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 505.40 on 640 degrees of freedom
## Residual deviance: 490.65 on 631 degrees of freedom
## AIC: 510.65
##
## Number of Fisher Scoring iterations: 5
confint(CVLTlog3)
## 2.5 % 97.5 %
## (Intercept) -0.15644107 3.717405147
## CVLfrs -0.06328045 0.191168570
## SexMen -1.57745880 0.496345881
## PovStatBelow -1.01346251 2.072883547
## Age -0.04341788 0.009867831
## WRATtotal -0.01327224 0.046698314
## CVLfrs:SexMen -0.15853628 0.176616872
## CVLfrs:PovStatBelow -0.29008092 0.173430402
## SexMen:PovStatBelow -2.20399826 1.799787040
## CVLfrs:SexMen:PovStatBelow -0.22596721 0.462209001
exp(cbind(OR = coef(CVLTlog3), confint(CVLTlog3)))
## OR 2.5 % 97.5 %
## (Intercept) 5.7340536 0.8551819 41.157458
## CVLfrs 1.0638144 0.9386802 1.210664
## SexMen 0.5934216 0.2064992 1.642708
## PovStatBelow 1.5701307 0.3629600 7.947708
## Age 0.9834927 0.9575112 1.009917
## WRATtotal 1.0172574 0.9868154 1.047806
## CVLfrs:SexMen 1.0092796 0.8533920 1.193174
## CVLfrs:PovStatBelow 0.9433069 0.7482030 1.189378
## SexMen:PovStatBelow 0.8361892 0.1103610 6.048359
## CVLfrs:SexMen:PovStatBelow 1.1209605 0.7977443 1.587577
########Compare to null model
#Difference in Deviance
with(CVLTlog3,null.deviance - deviance)
## [1] 14.75029
#Degrees of freedom for the difference between two models
with(CVLTlog3,df.null - df.residual)
## [1] 9
#p-value
with(CVLTlog3,pchisq(null.deviance-deviance,df.null-df.residual,lower.tail=FALSE))
## [1] 0.09802732
Compare Models 1,2, & 3
anova(CVLTlog1,CVLTlog2,test = "LR")
## Analysis of Deviance Table
##
## Model 1: PsychAggress ~ CVLfrs
## Model 2: PsychAggress ~ CVLfrs + Age + Sex + PovStat + WRATtotal
## Resid. Df Resid. Dev Df Deviance Pr(>Chi)
## 1 639 498.81
## 2 635 491.82 4 6.9886 0.1365
anova(CVLTlog2,CVLTlog3,test = "LR")
## Analysis of Deviance Table
##
## Model 1: PsychAggress ~ CVLfrs + Age + Sex + PovStat + WRATtotal
## Model 2: PsychAggress ~ (CVLfrs + Sex + PovStat)^3 + Age + WRATtotal
## Resid. Df Resid. Dev Df Deviance Pr(>Chi)
## 1 635 491.82
## 2 631 490.65 4 1.1715 0.8828
anova(CVLTlog1,CVLTlog3,test = "LR")
## Analysis of Deviance Table
##
## Model 1: PsychAggress ~ CVLfrs
## Model 2: PsychAggress ~ (CVLfrs + Sex + PovStat)^3 + Age + WRATtotal
## Resid. Df Resid. Dev Df Deviance Pr(>Chi)
## 1 639 498.81
## 2 631 490.65 8 8.16 0.418
Suggested Model by Predictors
anova(CVLTlog3, test="Chisq")
## Analysis of Deviance Table
##
## Model: binomial, link: logit
##
## Response: PsychAggress
##
## Terms added sequentially (first to last)
##
##
## Df Deviance Resid. Df Resid. Dev Pr(>Chi)
## NULL 640 505.40
## CVLfrs 1 6.5902 639 498.81 0.01025 *
## Sex 1 2.6495 638 496.16 0.10358
## PovStat 1 1.7011 637 494.46 0.19214
## Age 1 1.4498 636 493.01 0.22856
## WRATtotal 1 1.1882 635 491.82 0.27569
## CVLfrs:Sex 1 0.2197 634 491.60 0.63924
## CVLfrs:PovStat 1 0.0451 633 491.56 0.83176
## Sex:PovStat 1 0.4765 632 491.08 0.49000
## CVLfrs:Sex:PovStat 1 0.4301 631 490.65 0.51196
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
CVLTlog4 <- glm(PsychAggress ~ CVLfrs, data = Allvars, family = "binomial")
summary(CVLTlog4)
##
## Call:
## glm(formula = PsychAggress ~ CVLfrs, family = "binomial", data = Allvars)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.3304 0.4406 0.5232 0.5693 0.6717
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 1.37418 0.21530 6.383 1.74e-10 ***
## CVLfrs 0.09092 0.03595 2.529 0.0114 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 505.40 on 640 degrees of freedom
## Residual deviance: 498.81 on 639 degrees of freedom
## AIC: 502.81
##
## Number of Fisher Scoring iterations: 4
plot(allEffects(CVLTlog4))
California Verbal Learning Test (Short Delayed Free Recall) - Physical Assault
Model 1
CVLTlog1 <- glm(PhysAssault ~ CVLfrs, data=Allvars,family = "binomial")
summary(CVLTlog1)
##
## Call:
## glm(formula = PhysAssault ~ CVLfrs, family = "binomial", data = Allvars)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.5613 -0.5279 -0.5118 -0.4962 2.0950
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -2.07647 0.24361 -8.524 <2e-16 ***
## CVLfrs 0.02201 0.03571 0.616 0.538
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 482.53 on 640 degrees of freedom
## Residual deviance: 482.15 on 639 degrees of freedom
## AIC: 486.15
##
## Number of Fisher Scoring iterations: 4
confint(CVLTlog1)
## 2.5 % 97.5 %
## (Intercept) -2.57185249 -1.61505512
## CVLfrs -0.04822674 0.09204515
exp(cbind(OR = coef(CVLTlog1), confint(CVLTlog1)))
## OR 2.5 % 97.5 %
## (Intercept) 0.1253721 0.0763939 0.1988797
## CVLfrs 1.0222498 0.9529177 1.0964143
plot(predictorEffect("CVLfrs",CVLTlog1))
########Compare to null model
#Difference in Deviance
with(CVLTlog1,null.deviance - deviance)
## [1] 0.3792601
#Degrees of freedom for the difference between two models
with(CVLTlog1,df.null - df.residual)
## [1] 1
#p-value
with(CVLTlog1,pchisq(null.deviance-deviance,df.null-df.residual,lower.tail=FALSE))
## [1] 0.5379995
Model 2
CVLTlog2 <- glm(PhysAssault ~ CVLfrs + Age + Sex + PovStat + WRATtotal, data = Allvars, family = "binomial")
summary(CVLTlog2)
##
## Call:
## glm(formula = PhysAssault ~ CVLfrs + Age + Sex + PovStat + WRATtotal,
## family = "binomial", data = Allvars)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.8204 -0.5499 -0.4749 -0.3933 2.3918
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.24393 1.04408 -1.191 0.2335
## CVLfrs -0.01332 0.03923 -0.339 0.7342
## Age -0.03454 0.01421 -2.431 0.0150 *
## SexMen -0.16754 0.24647 -0.680 0.4967
## PovStatBelow 0.48208 0.25340 1.902 0.0571 .
## WRATtotal 0.01933 0.01779 1.086 0.2773
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 482.53 on 640 degrees of freedom
## Residual deviance: 469.31 on 635 degrees of freedom
## AIC: 481.31
##
## Number of Fisher Scoring iterations: 5
confint(CVLTlog2)
## 2.5 % 97.5 %
## (Intercept) -3.32802792 0.773449568
## CVLfrs -0.09020012 0.063887421
## Age -0.06282922 -0.007012407
## SexMen -0.65525937 0.313848030
## PovStatBelow -0.01980692 0.976408787
## WRATtotal -0.01464108 0.055273015
exp(cbind(OR = coef(CVLTlog2), confint(CVLTlog2)))
## OR 2.5 % 97.5 %
## (Intercept) 0.2882480 0.03586376 2.1672294
## CVLfrs 0.9867715 0.91374831 1.0659724
## Age 0.9660500 0.93910384 0.9930121
## SexMen 0.8457455 0.51930735 1.3686817
## PovStatBelow 1.6194445 0.98038795 2.6549048
## WRATtotal 1.0195179 0.98546558 1.0568291
########Compare to null model
#Difference in Deviance
with(CVLTlog2,null.deviance - deviance)
## [1] 13.22208
#Degrees of freedom for the difference between two models
with(CVLTlog2,df.null - df.residual)
## [1] 5
#p-value
with(CVLTlog2,pchisq(null.deviance-deviance,df.null-df.residual,lower.tail=FALSE))
## [1] 0.02138416
Model 3
CVLTlog3 <- glm(PhysAssault ~ (CVLfrs + Sex + PovStat)^3 + Age + WRATtotal, data = Allvars, family = "binomial")
summary(CVLTlog3)
##
## Call:
## glm(formula = PhysAssault ~ (CVLfrs + Sex + PovStat)^3 + Age +
## WRATtotal, family = "binomial", data = Allvars)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.0298 -0.5418 -0.4576 -0.3739 2.5096
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.75276 1.12368 -1.560 0.11880
## CVLfrs 0.07310 0.06396 1.143 0.25305
## SexMen 0.42285 0.67236 0.629 0.52942
## PovStatBelow 2.04485 0.69990 2.922 0.00348 **
## Age -0.03437 0.01427 -2.408 0.01602 *
## WRATtotal 0.01754 0.01814 0.967 0.33363
## CVLfrs:SexMen -0.09053 0.09203 -0.984 0.32529
## CVLfrs:PovStatBelow -0.26603 0.10348 -2.571 0.01014 *
## SexMen:PovStatBelow -1.90367 1.03608 -1.837 0.06615 .
## CVLfrs:SexMen:PovStatBelow 0.33770 0.15904 2.123 0.03372 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 482.53 on 640 degrees of freedom
## Residual deviance: 461.85 on 631 degrees of freedom
## AIC: 481.85
##
## Number of Fisher Scoring iterations: 5
confint(CVLTlog3)
## 2.5 % 97.5 %
## (Intercept) -4.00877842 0.40460004
## CVLfrs -0.05036067 0.20195206
## SexMen -0.89138897 1.76734247
## PovStatBelow 0.68639107 3.45020200
## Age -0.06278525 -0.00671841
## WRATtotal -0.01713924 0.05412889
## CVLfrs:SexMen -0.27352409 0.08858167
## CVLfrs:PovStatBelow -0.47447559 -0.06691616
## SexMen:PovStatBelow -3.98657186 0.09702919
## CVLfrs:SexMen:PovStatBelow 0.02909837 0.65460576
exp(cbind(OR = coef(CVLTlog3), confint(CVLTlog3)))
## OR 2.5 % 97.5 %
## (Intercept) 0.1732950 0.01815556 1.4987030
## CVLfrs 1.0758389 0.95088640 1.2237893
## SexMen 1.5262979 0.41008576 5.8552721
## PovStatBelow 7.7279951 1.98653331 31.5067561
## Age 0.9662183 0.93914513 0.9933041
## WRATtotal 1.0176916 0.98300680 1.0556207
## CVLfrs:SexMen 0.9134514 0.76069401 1.0926235
## CVLfrs:PovStatBelow 0.7664193 0.62221126 0.9352736
## SexMen:PovStatBelow 0.1490209 0.01856324 1.1018925
## CVLfrs:SexMen:PovStatBelow 1.4017152 1.02952586 1.9243837
########Compare to null model
#Difference in Deviance
with(CVLTlog3,null.deviance - deviance)
## [1] 20.67999
#Degrees of freedom for the difference between two models
with(CVLTlog3,df.null - df.residual)
## [1] 9
#p-value
with(CVLTlog3,pchisq(null.deviance-deviance,df.null-df.residual,lower.tail=FALSE))
## [1] 0.01414967
interact_plot(model = CVLTlog3, pred = CVLfrs, modx = Sex,mod2 = PovStat)
sim_slopes(CVLTlog3, pred = CVLfrs, modx = Sex, mod2 = PovStat, centered = "all",jnplot = TRUE)
## Warning: Johnson-Neyman intervals are not available for factor moderators.
## █████████████████████ While PovStat (2nd moderator) = Above ████████████████████
##
## SIMPLE SLOPES ANALYSIS
##
## Slope of CVLfrs when Sex = Women:
##
## Est. S.E. z val. p
## ------ ------ -------- ------
## 0.07 0.06 1.14 0.25
##
## Slope of CVLfrs when Sex = Men:
##
## Est. S.E. z val. p
## ------- ------ -------- ------
## -0.02 0.07 -0.25 0.80
##
## █████████████████████ While PovStat (2nd moderator) = Below ████████████████████
##
## SIMPLE SLOPES ANALYSIS
##
## Slope of CVLfrs when Sex = Women:
##
## Est. S.E. z val. p
## ------- ------ -------- ------
## -0.19 0.08 -2.30 0.02
##
## Slope of CVLfrs when Sex = Men:
##
## Est. S.E. z val. p
## ------ ------ -------- ------
## 0.05 0.10 0.53 0.59
##
## NULL
Compare Models 1,2, & 3
anova(CVLTlog1,CVLTlog2,test = "LR")
## Analysis of Deviance Table
##
## Model 1: PhysAssault ~ CVLfrs
## Model 2: PhysAssault ~ CVLfrs + Age + Sex + PovStat + WRATtotal
## Resid. Df Resid. Dev Df Deviance Pr(>Chi)
## 1 639 482.15
## 2 635 469.31 4 12.843 0.01207 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
anova(CVLTlog2,CVLTlog3,test = "LR")
## Analysis of Deviance Table
##
## Model 1: PhysAssault ~ CVLfrs + Age + Sex + PovStat + WRATtotal
## Model 2: PhysAssault ~ (CVLfrs + Sex + PovStat)^3 + Age + WRATtotal
## Resid. Df Resid. Dev Df Deviance Pr(>Chi)
## 1 635 469.31
## 2 631 461.85 4 7.4579 0.1136
anova(CVLTlog1,CVLTlog3,test = "LR")
## Analysis of Deviance Table
##
## Model 1: PhysAssault ~ CVLfrs
## Model 2: PhysAssault ~ (CVLfrs + Sex + PovStat)^3 + Age + WRATtotal
## Resid. Df Resid. Dev Df Deviance Pr(>Chi)
## 1 639 482.15
## 2 631 461.85 8 20.301 0.009256 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Suggested Model by Predictors
anova(CVLTlog3, test="Chisq")
## Analysis of Deviance Table
##
## Model: binomial, link: logit
##
## Response: PhysAssault
##
## Terms added sequentially (first to last)
##
##
## Df Deviance Resid. Df Resid. Dev Pr(>Chi)
## NULL 640 482.53
## CVLfrs 1 0.3793 639 482.15 0.53800
## Sex 1 0.6493 638 481.50 0.42035
## PovStat 1 5.0214 637 476.48 0.02504 *
## Age 1 5.9552 636 470.53 0.01467 *
## WRATtotal 1 1.2169 635 469.31 0.26998
## CVLfrs:Sex 1 0.1598 634 469.15 0.68934
## CVLfrs:PovStat 1 2.6917 633 466.46 0.10087
## Sex:PovStat 1 0.0008 632 466.46 0.97769
## CVLfrs:Sex:PovStat 1 4.6056 631 461.85 0.03187 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
CVLTlog4 <- glm(PhysAssault ~ (CVLfrs + Sex + PovStat)^3 + Age, data = Allvars, family = "binomial")
summary(CVLTlog4)
##
## Call:
## glm(formula = PhysAssault ~ (CVLfrs + Sex + PovStat)^3 + Age,
## family = "binomial", data = Allvars)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.0423 -0.5394 -0.4560 -0.3780 2.4097
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.06510 0.86278 -1.235 0.21701
## CVLfrs 0.08561 0.06278 1.364 0.17266
## SexMen 0.46806 0.67280 0.696 0.48663
## PovStatBelow 2.04632 0.69952 2.925 0.00344 **
## Age -0.03426 0.01429 -2.398 0.01647 *
## CVLfrs:SexMen -0.09293 0.09244 -1.005 0.31475
## CVLfrs:PovStatBelow -0.26936 0.10345 -2.604 0.00922 **
## SexMen:PovStatBelow -1.99570 1.03499 -1.928 0.05383 .
## CVLfrs:SexMen:PovStatBelow 0.34794 0.15931 2.184 0.02896 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 482.53 on 640 degrees of freedom
## Residual deviance: 462.81 on 632 degrees of freedom
## AIC: 480.81
##
## Number of Fisher Scoring iterations: 5
sim_slopes(CVLTlog4, pred = CVLfrs, modx = Sex, mod2 = PovStat, centered = "all",jnplot = TRUE)
## Warning: Johnson-Neyman intervals are not available for factor moderators.
## █████████████████████ While PovStat (2nd moderator) = Above ████████████████████
##
## SIMPLE SLOPES ANALYSIS
##
## Slope of CVLfrs when Sex = Women:
##
## Est. S.E. z val. p
## ------ ------ -------- ------
## 0.09 0.06 1.36 0.17
##
## Slope of CVLfrs when Sex = Men:
##
## Est. S.E. z val. p
## ------- ------ -------- ------
## -0.01 0.07 -0.11 0.92
##
## █████████████████████ While PovStat (2nd moderator) = Below ████████████████████
##
## SIMPLE SLOPES ANALYSIS
##
## Slope of CVLfrs when Sex = Women:
##
## Est. S.E. z val. p
## ------- ------ -------- ------
## -0.18 0.08 -2.20 0.03
##
## Slope of CVLfrs when Sex = Men:
##
## Est. S.E. z val. p
## ------ ------ -------- ------
## 0.07 0.10 0.71 0.48
##
## NULL
interact_plot(model = CVLTlog4, pred = CVLfrs, modx = Sex,mod2 = PovStat)
