Trail Making Test Part A - Psychological Aggression
Model 1
TMTAlog1 <- glm(PsychAggress ~ TrailsA, data=Allvars,family = "binomial")
summary(TMTAlog1)
##
## Call:
## glm(formula = PsychAggress ~ TrailsA, family = "binomial", data = Allvars)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.0612 0.5078 0.5190 0.5338 1.1300
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 2.117145 0.158671 13.343 <2e-16 ***
## TrailsA -0.006682 0.002709 -2.466 0.0136 *
## ---
## 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: 500.02 on 639 degrees of freedom
## AIC: 504.02
##
## Number of Fisher Scoring iterations: 4
confint(TMTAlog1)
## 2.5 % 97.5 %
## (Intercept) 1.81004605 2.43478018
## TrailsA -0.01211072 -0.00112323
exp(cbind(OR = coef(TMTAlog1), confint(TMTAlog1)))
## OR 2.5 % 97.5 %
## (Intercept) 8.3073870 6.1107288 11.4133096
## TrailsA 0.9933406 0.9879623 0.9988774
plot(predictorEffect("TrailsA",TMTAlog1))
Model 2
TMTAlog2 <- glm(PsychAggress ~ TrailsA + Age + Sex + PovStat + WRATtotal, data = Allvars, family = "binomial")
summary(TMTAlog2)
##
## Call:
## glm(formula = PsychAggress ~ TrailsA + Age + Sex + PovStat +
## WRATtotal, family = "binomial", data = Allvars)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.3328 0.4179 0.4977 0.5692 1.0358
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 2.239738 0.942059 2.377 0.0174 *
## TrailsA -0.005442 0.002800 -1.944 0.0519 .
## Age -0.019052 0.013150 -1.449 0.1474
## SexMen -0.414508 0.237194 -1.748 0.0805 .
## PovStatBelow 0.287057 0.272031 1.055 0.2913
## WRATtotal 0.020193 0.014685 1.375 0.1691
## ---
## 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: 491.19 on 635 degrees of freedom
## AIC: 503.19
##
## Number of Fisher Scoring iterations: 4
confint(TMTAlog2)
## 2.5 % 97.5 %
## (Intercept) 0.41738336 4.1183913807
## TrailsA -0.01092206 0.0004472425
## Age -0.04502990 0.0066403168
## SexMen -0.88490865 0.0476851129
## PovStatBelow -0.23280077 0.8382117419
## WRATtotal -0.00913341 0.0486223838
exp(cbind(OR = coef(TMTAlog2), confint(TMTAlog2)))
## OR 2.5 % 97.5 %
## (Intercept) 9.3908738 1.5179843 61.460296
## TrailsA 0.9945728 0.9891374 1.000447
## Age 0.9811286 0.9559689 1.006662
## SexMen 0.6606650 0.4127519 1.048840
## PovStatBelow 1.3324997 0.7923114 2.312228
## WRATtotal 1.0203987 0.9909082 1.049824
plot(predictorEffect("TrailsA",TMTAlog2))
Model 3
TMTAlog3 <- glm(PsychAggress ~ (TrailsA + Sex + PovStat)^3 + Age + WRATtotal, data = Allvars, family = "binomial")
summary(TMTAlog3)
##
## Call:
## glm(formula = PsychAggress ~ (TrailsA + Sex + PovStat)^3 + Age +
## WRATtotal, family = "binomial", data = Allvars)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.3050 0.4145 0.4876 0.5662 0.9371
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 2.3232837 0.9521720 2.440 0.0147 *
## TrailsA -0.0046246 0.0044733 -1.034 0.3012
## SexMen -0.5352512 0.3757272 -1.425 0.1543
## PovStatBelow -0.3211181 0.9165205 -0.350 0.7261
## Age -0.0211628 0.0133563 -1.584 0.1131
## WRATtotal 0.0213502 0.0147965 1.443 0.1490
## TrailsA:SexMen 0.0002853 0.0061339 0.047 0.9629
## TrailsA:PovStatBelow 0.0121948 0.0262325 0.465 0.6420
## SexMen:PovStatBelow 1.1973680 1.0734939 1.115 0.2647
## TrailsA:SexMen:PovStatBelow -0.0212556 0.0279137 -0.761 0.4464
## ---
## 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: 489.18 on 631 degrees of freedom
## AIC: 509.18
##
## Number of Fisher Scoring iterations: 5
confint(TMTAlog3)
## 2.5 % 97.5 %
## (Intercept) 0.481332779 4.221863340
## TrailsA -0.013299827 0.005857359
## SexMen -1.282198611 0.200258901
## PovStatBelow -2.259676235 1.362523862
## Age -0.047546389 0.004928338
## WRATtotal -0.008162031 0.050025754
## TrailsA:SexMen -0.012576295 0.012711360
## TrailsA:PovStatBelow -0.030481680 0.074099544
## SexMen:PovStatBelow -0.795772727 3.446700474
## TrailsA:SexMen:PovStatBelow -0.085997696 0.025273803
exp(cbind(OR = coef(TMTAlog3), confint(TMTAlog3)))
## OR 2.5 % 97.5 %
## (Intercept) 10.2091427 1.6182297 68.160372
## TrailsA 0.9953861 0.9867882 1.005875
## SexMen 0.5855222 0.2774267 1.221719
## PovStatBelow 0.7253376 0.1043843 3.906039
## Age 0.9790596 0.9535662 1.004941
## WRATtotal 1.0215797 0.9918712 1.051298
## TrailsA:SexMen 1.0002853 0.9875025 1.012792
## TrailsA:PovStatBelow 1.0122695 0.9699782 1.076914
## SexMen:PovStatBelow 3.3113900 0.4512324 31.396627
## TrailsA:SexMen:PovStatBelow 0.9789687 0.9175963 1.025596
Compare Models 1,2, & 3
anova(TMTAlog1,TMTAlog2,test = "LR")
## Analysis of Deviance Table
##
## Model 1: PsychAggress ~ TrailsA
## Model 2: PsychAggress ~ TrailsA + Age + Sex + PovStat + WRATtotal
## Resid. Df Resid. Dev Df Deviance Pr(>Chi)
## 1 639 500.02
## 2 635 491.19 4 8.8277 0.06555 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
anova(TMTAlog2,TMTAlog3,test = "LR")
## Analysis of Deviance Table
##
## Model 1: PsychAggress ~ TrailsA + Age + Sex + PovStat + WRATtotal
## Model 2: PsychAggress ~ (TrailsA + Sex + PovStat)^3 + Age + WRATtotal
## Resid. Df Resid. Dev Df Deviance Pr(>Chi)
## 1 635 491.19
## 2 631 489.18 4 2.0105 0.7338
anova(TMTAlog1,TMTAlog3,test = "LR")
## Analysis of Deviance Table
##
## Model 1: PsychAggress ~ TrailsA
## Model 2: PsychAggress ~ (TrailsA + Sex + PovStat)^3 + Age + WRATtotal
## Resid. Df Resid. Dev Df Deviance Pr(>Chi)
## 1 639 500.02
## 2 631 489.18 8 10.838 0.211
Suggested Model by predictors
anova(TMTAlog3, 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
## TrailsA 1 5.3815 639 500.02 0.02035 *
## Sex 1 3.3913 638 496.63 0.06554 .
## PovStat 1 1.3103 637 495.32 0.25234
## Age 1 2.2815 636 493.04 0.13092
## WRATtotal 1 1.8446 635 491.19 0.17441
## TrailsA:Sex 1 0.1610 634 491.03 0.68825
## TrailsA:PovStat 1 0.4359 633 490.60 0.50911
## Sex:PovStat 1 0.7356 632 489.86 0.39108
## TrailsA:Sex:PovStat 1 0.6780 631 489.18 0.41027
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
TMTAlog4 <- glm(PsychAggress ~ TrailsA + Sex, data = Allvars, family = "binomial")
summary(TMTAlog4)
##
## Call:
## glm(formula = PsychAggress ~ TrailsA + Sex, family = "binomial",
## data = Allvars)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.1553 0.4598 0.4829 0.5732 1.1918
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 2.335826 0.205687 11.356 <2e-16 ***
## TrailsA -0.006459 0.002729 -2.367 0.0179 *
## SexMen -0.431945 0.235930 -1.831 0.0671 .
## ---
## 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: 496.63 on 638 degrees of freedom
## AIC: 502.63
##
## Number of Fisher Scoring iterations: 4
plot(predictorEffect("TrailsA",TMTAlog3))
Trail Making Test Part A - Physical Assault
Model 1
TMTAlog1 <- glm(PhysAssault ~ TrailsA, data=Allvars,family = "binomial")
summary(TMTAlog1)
##
## Call:
## glm(formula = PhysAssault ~ TrailsA, family = "binomial", data = Allvars)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.5662 -0.5393 -0.5186 -0.4840 2.2518
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.602571 0.283815 -5.647 1.64e-08 ***
## TrailsA -0.010496 0.008213 -1.278 0.201
## ---
## 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: 479.87 on 639 degrees of freedom
## AIC: 483.87
##
## Number of Fisher Scoring iterations: 6
exp(cbind(OR = coef(TMTAlog1), confint(TMTAlog1)))
## OR 2.5 % 97.5 %
## (Intercept) 0.2013782 0.124961 0.3715058
## TrailsA 0.9895584 0.971045 1.0014763
Model 2
TMTAlog2 <- glm(PhysAssault ~ TrailsA + Age + Sex + PovStat + WRATtotal, data = Allvars, family = "binomial")
summary(TMTAlog2)
##
## Call:
## glm(formula = PhysAssault ~ TrailsA + Age + Sex + PovStat + WRATtotal,
## family = "binomial", data = Allvars)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.8280 -0.5544 -0.4735 -0.3948 2.4131
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.106171 1.056618 -1.047 0.2951
## TrailsA -0.005757 0.006888 -0.836 0.4033
## Age -0.031146 0.014013 -2.223 0.0262 *
## SexMen -0.133250 0.245234 -0.543 0.5869
## PovStatBelow 0.497935 0.253546 1.964 0.0495 *
## WRATtotal 0.014741 0.017431 0.846 0.3977
## ---
## 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: 468.44 on 635 degrees of freedom
## AIC: 480.44
##
## Number of Fisher Scoring iterations: 5
confint(TMTAlog2)
## 2.5 % 97.5 %
## (Intercept) -3.204878260 0.950549388
## TrailsA -0.023671777 0.004114868
## Age -0.058973070 -0.003903141
## SexMen -0.618366886 0.345925262
## PovStatBelow -0.004036407 0.992778921
## WRATtotal -0.018681019 0.049876074
exp(cbind(OR = coef(TMTAlog2), confint(TMTAlog2)))
## OR 2.5 % 97.5 %
## (Intercept) 0.3308234 0.04056384 2.5871306
## TrailsA 0.9942597 0.97660620 1.0041233
## Age 0.9693345 0.94273216 0.9961045
## SexMen 0.8752466 0.53882368 1.4132970
## PovStatBelow 1.6453197 0.99597173 2.6987236
## WRATtotal 1.0148506 0.98149239 1.0511408
Model 3
TMTAlog3 <- glm(PhysAssault ~ (TrailsA + Sex + PovStat)^3 + Age + WRATtotal, data = Allvars, family = "binomial")
summary(TMTAlog3)
##
## Call:
## glm(formula = PhysAssault ~ (TrailsA + Sex + PovStat)^3 + Age +
## WRATtotal, family = "binomial", data = Allvars)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.9896 -0.5444 -0.4687 -0.3914 2.4456
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.363e+00 1.058e+00 -1.289 0.1973
## TrailsA 7.282e-05 7.277e-03 0.010 0.9920
## SexMen 7.630e-02 5.733e-01 0.133 0.8941
## PovStatBelow 1.662e+00 8.945e-01 1.858 0.0632 .
## Age -2.951e-02 1.412e-02 -2.090 0.0366 *
## WRATtotal 1.521e-02 1.745e-02 0.872 0.3832
## TrailsA:SexMen -8.029e-03 1.526e-02 -0.526 0.5987
## TrailsA:PovStatBelow -4.104e-02 2.948e-02 -1.392 0.1638
## SexMen:PovStatBelow -1.192e+00 1.159e+00 -1.028 0.3040
## TrailsA:SexMen:PovStatBelow 4.372e-02 3.509e-02 1.246 0.2129
## ---
## 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: 466.00 on 631 degrees of freedom
## AIC: 486
##
## Number of Fisher Scoring iterations: 6
confint(TMTAlog3)
## 2.5 % 97.5 %
## (Intercept) -3.466733133 0.698550735
## TrailsA -0.022462644 0.010741157
## SexMen -0.973440421 1.399852489
## PovStatBelow 0.006201422 3.512932999
## Age -0.057593946 -0.002109772
## WRATtotal -0.018221155 0.050392858
## TrailsA:SexMen -0.048493721 0.019944784
## TrailsA:PovStatBelow -0.105757832 0.010049154
## SexMen:PovStatBelow -3.556398613 1.153705945
## TrailsA:SexMen:PovStatBelow -0.024980614 0.119436934
exp(cbind(OR = coef(TMTAlog3), confint(TMTAlog3)))
## OR 2.5 % 97.5 %
## (Intercept) 0.2557689 0.03121885 2.0108364
## TrailsA 1.0000728 0.97778776 1.0107991
## SexMen 1.0792863 0.37778107 4.0546018
## PovStatBelow 5.2680898 1.00622069 33.5465155
## Age 0.9709182 0.94403320 0.9978925
## WRATtotal 1.0153311 0.98194385 1.0516842
## TrailsA:SexMen 0.9920033 0.95266332 1.0201450
## TrailsA:PovStatBelow 0.9597892 0.89964249 1.0100998
## SexMen:PovStatBelow 0.3037608 0.02854143 3.1699187
## TrailsA:SexMen:PovStatBelow 1.0446857 0.97532882 1.1268622
Compare Models 1,2, & 3
anova(TMTAlog1,TMTAlog2,test = "LR")
## Analysis of Deviance Table
##
## Model 1: PhysAssault ~ TrailsA
## Model 2: PhysAssault ~ TrailsA + Age + Sex + PovStat + WRATtotal
## Resid. Df Resid. Dev Df Deviance Pr(>Chi)
## 1 639 479.87
## 2 635 468.44 4 11.427 0.02216 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
anova(TMTAlog2,TMTAlog3,test = "LR")
## Analysis of Deviance Table
##
## Model 1: PhysAssault ~ TrailsA + Age + Sex + PovStat + WRATtotal
## Model 2: PhysAssault ~ (TrailsA + Sex + PovStat)^3 + Age + WRATtotal
## Resid. Df Resid. Dev Df Deviance Pr(>Chi)
## 1 635 468.44
## 2 631 466.00 4 2.4366 0.656
anova(TMTAlog1,TMTAlog3,test = "LR")
## Analysis of Deviance Table
##
## Model 1: PhysAssault ~ TrailsA
## Model 2: PhysAssault ~ (TrailsA + Sex + PovStat)^3 + Age + WRATtotal
## Resid. Df Resid. Dev Df Deviance Pr(>Chi)
## 1 639 479.87
## 2 631 466.00 8 13.864 0.08538 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Suggested Model by predictors
anova(TMTAlog3, 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
## TrailsA 1 2.6649 639 479.87 0.10259
## Sex 1 0.5758 638 479.29 0.44795
## PovStat 1 5.0877 637 474.20 0.02410 *
## Age 1 5.0333 636 469.17 0.02486 *
## WRATtotal 1 0.7303 635 468.44 0.39278
## TrailsA:Sex 1 0.0169 634 468.42 0.89666
## TrailsA:PovStat 1 0.7216 633 467.70 0.39561
## Sex:PovStat 1 0.0962 632 467.61 0.75639
## TrailsA:Sex:PovStat 1 1.6019 631 466.00 0.20563
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
TMTAlog4 <- glm(PhysAssault ~ TrailsA + PovStat + Age, data = Allvars, family = "binomial")
summary(TMTAlog4)
##
## Call:
## glm(formula = PhysAssault ~ TrailsA + PovStat + Age, family = "binomial",
## data = Allvars)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.7820 -0.5546 -0.4799 -0.3925 2.3219
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.435029 0.650356 -0.669 0.5036
## TrailsA -0.007343 0.007395 -0.993 0.3208
## PovStatBelow 0.471055 0.249746 1.886 0.0593 .
## Age -0.031701 0.014071 -2.253 0.0243 *
## ---
## 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: 469.43 on 637 degrees of freedom
## AIC: 477.43
##
## Number of Fisher Scoring iterations: 5
Trail Making Test Part B - Psychological Aggression
Model 1
TMTBlog1 <- glm(PsychAggress ~ TrailsB, data=Allvars,family = "binomial")
summary(TMTBlog1)
##
## Call:
## glm(formula = PsychAggress ~ TrailsB, family = "binomial", data = Allvars)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.0179 0.5298 0.5317 0.5352 0.5784
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 1.9095515 0.1585263 12.046 <2e-16 ***
## TrailsB -0.0003434 0.0008116 -0.423 0.672
## ---
## 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: 505.23 on 639 degrees of freedom
## AIC: 509.23
##
## Number of Fisher Scoring iterations: 4
confint(TMTBlog1)
## 2.5 % 97.5 %
## (Intercept) 1.603528690 2.226283495
## TrailsB -0.001843453 0.001370329
exp(cbind(OR = coef(TMTBlog1), confint(TMTBlog1)))
## OR 2.5 % 97.5 %
## (Intercept) 6.7500606 4.9705410 9.265367
## TrailsB 0.9996567 0.9981582 1.001371
Model 2
TMTBlog2 <- glm(PsychAggress ~ TrailsB + Age + Sex + PovStat + WRATtotal, data = Allvars, family = "binomial")
summary(TMTBlog2)
##
## Call:
## glm(formula = PsychAggress ~ TrailsB + Age + Sex + PovStat +
## WRATtotal, family = "binomial", data = Allvars)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.3619 0.4150 0.5004 0.5760 0.8584
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 1.8020518 0.9778048 1.843 0.0653 .
## TrailsB 0.0006771 0.0009540 0.710 0.4778
## Age -0.0237146 0.0133422 -1.777 0.0755 .
## SexMen -0.4354463 0.2367389 -1.839 0.0659 .
## PovStatBelow 0.3039214 0.2721369 1.117 0.2641
## WRATtotal 0.0287737 0.0161637 1.780 0.0751 .
## ---
## 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: 493.99 on 635 degrees of freedom
## AIC: 505.99
##
## Number of Fisher Scoring iterations: 5
confint(TMTBlog2)
## 2.5 % 97.5 %
## (Intercept) -0.095930605 3.744570374
## TrailsB -0.001082010 0.002691322
## Age -0.050095754 0.002318790
## SexMen -0.905052950 0.025652999
## PovStatBelow -0.215831014 0.855443932
## WRATtotal -0.003227791 0.060312930
exp(cbind(OR = coef(TMTBlog2), confint(TMTBlog2)))
## OR 2.5 % 97.5 %
## (Intercept) 6.0620729 0.9085271 42.290834
## TrailsB 1.0006774 0.9989186 1.002695
## Age 0.9765644 0.9511383 1.002321
## SexMen 0.6469759 0.4045205 1.025985
## PovStatBelow 1.3551625 0.8058715 2.352418
## WRATtotal 1.0291916 0.9967774 1.062169
Model 3
TMTBlog3 <- glm(PsychAggress ~ (TrailsB + Sex + PovStat)^3 + Age + WRATtotal, data = Allvars, family = "binomial")
summary(TMTBlog3)
##
## Call:
## glm(formula = PsychAggress ~ (TrailsB + Sex + PovStat)^3 + Age +
## WRATtotal, family = "binomial", data = Allvars)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.3117 0.4143 0.4919 0.5758 0.9089
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 1.8470931 1.0170851 1.816 0.0694 .
## TrailsB 0.0009530 0.0016849 0.566 0.5716
## SexMen -0.3776448 0.3832676 -0.985 0.3245
## PovStatBelow 0.1350920 0.5644040 0.239 0.8108
## Age -0.0247057 0.0134045 -1.843 0.0653 .
## WRATtotal 0.0293987 0.0166197 1.769 0.0769 .
## TrailsB:SexMen -0.0011460 0.0019778 -0.579 0.5623
## TrailsB:PovStatBelow -0.0002675 0.0033941 -0.079 0.9372
## SexMen:PovStatBelow -0.4577984 0.8724902 -0.525 0.5998
## TrailsB:SexMen:PovStatBelow 0.0068832 0.0064134 1.073 0.2832
## ---
## 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: 490.68 on 631 degrees of freedom
## AIC: 510.68
##
## Number of Fisher Scoring iterations: 6
confint(TMTBlog3)
## 2.5 % 97.5 %
## (Intercept) -0.130044657 3.866276279
## TrailsB -0.001972185 0.004876943
## SexMen -1.136178955 0.373151205
## PovStatBelow -0.995653315 1.257161817
## Age -0.051214434 0.001446410
## WRATtotal -0.003491845 0.061838894
## TrailsB:SexMen -0.005444410 0.002553615
## TrailsB:PovStatBelow -0.006344026 0.008143077
## SexMen:PovStatBelow -2.277018711 1.194936381
## TrailsB:SexMen:PovStatBelow -0.004563296 0.022491683
exp(cbind(OR = coef(TMTBlog3), confint(TMTBlog3)))
## OR 2.5 % 97.5 %
## (Intercept) 6.3413588 0.8780562 47.764194
## TrailsB 1.0009535 0.9980298 1.004889
## SexMen 0.6854739 0.3210434 1.452304
## PovStatBelow 1.1446421 0.3694820 3.515430
## Age 0.9755970 0.9500749 1.001447
## WRATtotal 1.0298351 0.9965142 1.063791
## TrailsB:SexMen 0.9988547 0.9945704 1.002557
## TrailsB:PovStatBelow 0.9997325 0.9936761 1.008176
## SexMen:PovStatBelow 0.6326750 0.1025896 3.303348
## TrailsB:SexMen:PovStatBelow 1.0069070 0.9954471 1.022747
Compare Models 1,2, & 3
anova(TMTBlog1,TMTBlog2,test = "LR")
## Analysis of Deviance Table
##
## Model 1: PsychAggress ~ TrailsB
## Model 2: PsychAggress ~ TrailsB + Age + Sex + PovStat + WRATtotal
## Resid. Df Resid. Dev Df Deviance Pr(>Chi)
## 1 639 505.23
## 2 635 493.99 4 11.237 0.02403 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
anova(TMTBlog2,TMTBlog3,test = "LR")
## Analysis of Deviance Table
##
## Model 1: PsychAggress ~ TrailsB + Age + Sex + PovStat + WRATtotal
## Model 2: PsychAggress ~ (TrailsB + Sex + PovStat)^3 + Age + WRATtotal
## Resid. Df Resid. Dev Df Deviance Pr(>Chi)
## 1 635 493.99
## 2 631 490.68 4 3.3076 0.5077
anova(TMTBlog1,TMTBlog3,test = "LR")
## Analysis of Deviance Table
##
## Model 1: PsychAggress ~ TrailsB
## Model 2: PsychAggress ~ (TrailsB + Sex + PovStat)^3 + Age + WRATtotal
## Resid. Df Resid. Dev Df Deviance Pr(>Chi)
## 1 639 505.23
## 2 631 490.68 8 14.544 0.06864 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Suggested model by predictors
anova(TMTBlog3, 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
## TrailsB 1 0.1739 639 505.23 0.67667
## Sex 1 3.7458 638 501.48 0.05294 .
## PovStat 1 1.3850 637 500.10 0.23926
## Age 1 2.9924 636 497.10 0.08366 .
## WRATtotal 1 3.1135 635 493.99 0.07765 .
## TrailsB:Sex 1 0.0370 634 493.95 0.84749
## TrailsB:PovStat 1 1.5703 633 492.38 0.21017
## Sex:PovStat 1 0.3217 632 492.06 0.57058
## TrailsB:Sex:PovStat 1 1.3787 631 490.68 0.24033
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
TMTBlog4 <- glm(PsychAggress ~ Sex + Age + WRATtotal, data = Allvars, family = "binomial")
summary(TMTBlog4)
##
## Call:
## glm(formula = PsychAggress ~ Sex + Age + WRATtotal, family = "binomial",
## data = Allvars)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.2850 0.4317 0.4986 0.5734 0.8093
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 2.31934 0.89316 2.597 0.00941 **
## SexMen -0.43214 0.23609 -1.830 0.06718 .
## Age -0.02420 0.01290 -1.875 0.06073 .
## WRATtotal 0.02134 0.01444 1.478 0.13953
## ---
## 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: 495.76 on 637 degrees of freedom
## AIC: 503.76
##
## Number of Fisher Scoring iterations: 4
Trail Making Test Part B - Physical Assault
Model 1
TMTBlog1 <- glm(PhysAssault ~ TrailsB, data=Allvars,family = "binomial")
summary(TMTBlog1)
##
## Call:
## glm(formula = PhysAssault ~ TrailsB, family = "binomial", data = Allvars)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.5180 -0.5163 -0.5162 -0.5161 2.0407
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.950e+00 1.640e-01 -11.884 <2e-16 ***
## TrailsB 1.438e-05 8.734e-04 0.016 0.987
## ---
## 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: 482.53 on 639 degrees of freedom
## AIC: 486.53
##
## Number of Fisher Scoring iterations: 4
confint(TMTBlog1)
## 2.5 % 97.5 %
## (Intercept) -2.277053463 -1.632336504
## TrailsB -0.001853385 0.001610948
exp(cbind(OR = coef(TMTBlog1), confint(TMTBlog1)))
## OR 2.5 % 97.5 %
## (Intercept) 0.1423392 0.1025860 0.1954723
## TrailsB 1.0000144 0.9981483 1.0016122
Model 2
TMTBlog2 <- glm(PhysAssault ~ TrailsB + Age + Sex + PovStat + WRATtotal, data = Allvars, family = "binomial")
summary(TMTBlog2)
##
## Call:
## glm(formula = PhysAssault ~ TrailsB + Age + Sex + PovStat + WRATtotal,
## family = "binomial", data = Allvars)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.8296 -0.5503 -0.4703 -0.3853 2.4150
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.5644451 1.0563772 -1.481 0.1386
## TrailsB 0.0009794 0.0009424 1.039 0.2987
## Age -0.0361938 0.0140597 -2.574 0.0100 *
## SexMen -0.1672179 0.2444699 -0.684 0.4940
## PovStatBelow 0.4877010 0.2524719 1.932 0.0534 .
## WRATtotal 0.0236622 0.0180504 1.311 0.1899
## ---
## 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: 468.42 on 635 degrees of freedom
## AIC: 480.42
##
## Number of Fisher Scoring iterations: 5
confint(TMTBlog2)
## 2.5 % 97.5 %
## (Intercept) -3.672079915 0.479179128
## TrailsB -0.001004126 0.002734023
## Age -0.064203068 -0.008963590
## SexMen -0.651102761 0.310192287
## PovStatBelow -0.012396682 0.980167579
## WRATtotal -0.010869929 0.060084244
exp(cbind(OR = coef(TMTBlog2), confint(TMTBlog2)))
## OR 2.5 % 97.5 %
## (Intercept) 0.2092041 0.02542354 1.6147484
## TrailsB 1.0009799 0.99899638 1.0027378
## Age 0.9644534 0.93781454 0.9910765
## SexMen 0.8460153 0.52147040 1.3636873
## PovStatBelow 1.6285679 0.98767984 2.6649028
## WRATtotal 1.0239444 0.98918894 1.0619260
Model 3
TMTBlog3 <- glm(PhysAssault ~ (TrailsB + Sex + PovStat)^3 + Age + WRATtotal, data = Allvars, family = "binomial")
summary(TMTBlog3)
##
## Call:
## glm(formula = PhysAssault ~ (TrailsB + Sex + PovStat)^3 + Age +
## WRATtotal, family = "binomial", data = Allvars)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.8516 -0.5473 -0.4694 -0.3836 2.4223
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.5047593 1.0909002 -1.379 0.1678
## TrailsB 0.0006869 0.0016674 0.412 0.6804
## SexMen -0.2700783 0.4201321 -0.643 0.5203
## PovStatBelow 0.3317966 0.4541255 0.731 0.4650
## Age -0.0359140 0.0140777 -2.551 0.0107 *
## WRATtotal 0.0231490 0.0183290 1.263 0.2066
## TrailsB:SexMen 0.0005718 0.0021795 0.262 0.7930
## TrailsB:PovStatBelow 0.0009891 0.0024708 0.400 0.6889
## SexMen:PovStatBelow 0.3715339 0.6866325 0.541 0.5884
## TrailsB:SexMen:PovStatBelow -0.0022663 0.0036875 -0.615 0.5388
## ---
## 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: 467.99 on 631 degrees of freedom
## AIC: 487.99
##
## Number of Fisher Scoring iterations: 5
confint(TMTBlog3)
## 2.5 % 97.5 %
## (Intercept) -3.678000065 0.610271057
## TrailsB -0.003187691 0.003605599
## SexMen -1.104549024 0.551216412
## PovStatBelow -0.569590398 1.219846578
## Age -0.063961083 -0.008649270
## WRATtotal -0.011949379 0.060101998
## TrailsB:SexMen -0.003782969 0.005106246
## TrailsB:PovStatBelow -0.004013430 0.006037226
## SexMen:PovStatBelow -0.976968871 1.726534192
## TrailsB:SexMen:PovStatBelow -0.010127719 0.004822560
Compare Models 1,2, & 3
anova(TMTBlog1,TMTBlog2,test = "LR")
## Analysis of Deviance Table
##
## Model 1: PhysAssault ~ TrailsB
## Model 2: PhysAssault ~ TrailsB + Age + Sex + PovStat + WRATtotal
## Resid. Df Resid. Dev Df Deviance Pr(>Chi)
## 1 639 482.53
## 2 635 468.42 4 14.116 0.006935 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
anova(TMTBlog2,TMTBlog3,test = "LR")
## Analysis of Deviance Table
##
## Model 1: PhysAssault ~ TrailsB + Age + Sex + PovStat + WRATtotal
## Model 2: PhysAssault ~ (TrailsB + Sex + PovStat)^3 + Age + WRATtotal
## Resid. Df Resid. Dev Df Deviance Pr(>Chi)
## 1 635 468.42
## 2 631 467.99 4 0.43161 0.9798
anova(TMTBlog1,TMTBlog3,test = "LR")
## Analysis of Deviance Table
##
## Model 1: PhysAssault ~ TrailsB
## Model 2: PhysAssault ~ (TrailsB + Sex + PovStat)^3 + Age + WRATtotal
## Resid. Df Resid. Dev Df Deviance Pr(>Chi)
## 1 639 482.53
## 2 631 467.99 8 14.547 0.06857 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Sugested Model by predictors
anova(TMTBlog3, 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
## TrailsB 1 0.0003 639 482.53 0.986876
## Sex 1 0.7837 638 481.75 0.376008
## PovStat 1 4.8645 637 476.88 0.027415 *
## Age 1 6.6895 636 470.19 0.009698 **
## WRATtotal 1 1.7781 635 468.42 0.182388
## TrailsB:Sex 1 0.0191 634 468.40 0.890110
## TrailsB:PovStat 1 0.0005 633 468.40 0.981454
## Sex:PovStat 1 0.0250 632 468.37 0.874300
## TrailsB:Sex:PovStat 1 0.3870 631 467.99 0.533904
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
TMTBlog4 <- glm(PhysAssault ~ Age + PovStat, data = Allvars, family = "binomial")
summary(TMTBlog4)
##
## Call:
## glm(formula = PhysAssault ~ Age + PovStat, family = "binomial",
## data = Allvars)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.7492 -0.5539 -0.4770 -0.3956 2.3402
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.53355 0.64202 -0.831 0.4059
## Age -0.03471 0.01372 -2.530 0.0114 *
## PovStatBelow 0.45113 0.24862 1.815 0.0696 .
## ---
## 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: 470.90 on 638 degrees of freedom
## AIC: 476.9
##
## Number of Fisher Scoring iterations: 5
Digit Span Forward - Psychological Aggression
Model 1
DSFlog1 <- glm(PsychAggress ~ DigitSpanFwd, data=Allvars,family = "binomial")
summary(DSFlog1)
##
## Call:
## glm(formula = PsychAggress ~ DigitSpanFwd, family = "binomial",
## data = Allvars)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.2372 0.4749 0.5205 0.5697 0.6799
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 1.15245 0.39763 2.898 0.00375 **
## DigitSpanFwd 0.09729 0.05325 1.827 0.06768 .
## ---
## 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: 501.93 on 639 degrees of freedom
## AIC: 505.93
##
## Number of Fisher Scoring iterations: 4
confint(DSFlog1)
## 2.5 % 97.5 %
## (Intercept) 0.374377408 1.9358469
## DigitSpanFwd -0.004952948 0.2041858
exp(cbind(OR = coef(DSFlog1), confint(DSFlog1)))
## OR 2.5 % 97.5 %
## (Intercept) 3.165935 1.4540858 6.929910
## DigitSpanFwd 1.102177 0.9950593 1.226526
Model 2
DSFlog2 <- glm(PsychAggress ~ DigitSpanFwd + Age + Sex + PovStat + WRATtotal, data = Allvars, family = "binomial")
summary(DSFlog2)
##
## Call:
## glm(formula = PsychAggress ~ DigitSpanFwd + Age + Sex + PovStat +
## WRATtotal, family = "binomial", data = Allvars)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.3744 0.4082 0.4997 0.5858 0.8451
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 1.78187 0.95032 1.875 0.0608 .
## DigitSpanFwd 0.07980 0.06021 1.325 0.1850
## Age -0.02014 0.01315 -1.531 0.1257
## SexMen -0.44464 0.23712 -1.875 0.0608 .
## PovStatBelow 0.31763 0.27196 1.168 0.2428
## WRATtotal 0.01382 0.01643 0.841 0.4003
## ---
## 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: 492.71 on 635 degrees of freedom
## AIC: 504.71
##
## Number of Fisher Scoring iterations: 5
confint(DSFlog2)
## 2.5 % 97.5 %
## (Intercept) -0.05975028 3.672887543
## DigitSpanFwd -0.03597106 0.200534414
## Age -0.04613507 0.005539276
## SexMen -0.91505007 0.017156078
## PovStatBelow -0.20190212 0.868656506
## WRATtotal -0.01900186 0.045600639
exp(cbind(OR = coef(DSFlog2), confint(DSFlog2)))
## OR 2.5 % 97.5 %
## (Intercept) 5.9409367 0.9419997 39.365411
## DigitSpanFwd 1.0830707 0.9646682 1.222056
## Age 0.9800581 0.9549130 1.005555
## SexMen 0.6410525 0.4004966 1.017304
## PovStatBelow 1.3738701 0.8171749 2.383706
## WRATtotal 1.0139186 0.9811775 1.046656
Model 3
DSFlog3 <- glm(PsychAggress ~ (DigitSpanFwd + Sex + PovStat)^3 + Age + WRATtotal, data = Allvars, family = "binomial")
summary(DSFlog3)
##
## Call:
## glm(formula = PsychAggress ~ (DigitSpanFwd + Sex + PovStat)^3 +
## Age + WRATtotal, family = "binomial", data = Allvars)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.2630 0.4174 0.4864 0.5671 0.8936
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 2.05209 1.11257 1.844 0.0651 .
## DigitSpanFwd 0.04840 0.09789 0.494 0.6210
## SexMen -0.95692 0.94536 -1.012 0.3114
## PovStatBelow 0.19631 1.31460 0.149 0.8813
## Age -0.02016 0.01320 -1.527 0.1267
## WRATtotal 0.01412 0.01657 0.852 0.3939
## DigitSpanFwd:SexMen 0.05684 0.12434 0.457 0.6476
## DigitSpanFwd:PovStatBelow -0.01234 0.17920 -0.069 0.9451
## SexMen:PovStatBelow 0.20818 1.84272 0.113 0.9100
## DigitSpanFwd:SexMen:PovStatBelow 0.02781 0.25559 0.109 0.9134
## ---
## 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: 491.88 on 631 degrees of freedom
## AIC: 511.88
##
## Number of Fisher Scoring iterations: 5
confint(DSFlog3)
## 2.5 % 97.5 %
## (Intercept) -0.09264104 4.279188872
## DigitSpanFwd -0.13709894 0.248989556
## SexMen -2.82517597 0.891305947
## PovStatBelow -2.37156876 2.815163307
## Age -0.04624726 0.005614341
## WRATtotal -0.01894722 0.046169635
## DigitSpanFwd:SexMen -0.18968591 0.299953467
## DigitSpanFwd:PovStatBelow -0.35703285 0.351928834
## SexMen:PovStatBelow -3.43757513 3.815705044
## DigitSpanFwd:SexMen:PovStatBelow -0.47184254 0.537277577
exp(cbind(OR = coef(DSFlog3), confint(DSFlog3)))
## OR 2.5 % 97.5 %
## (Intercept) 7.7841672 0.91152064 72.181868
## DigitSpanFwd 1.0495926 0.87188396 1.282729
## SexMen 0.3840725 0.05929822 2.438312
## PovStatBelow 1.2169073 0.09333419 16.695902
## Age 0.9800401 0.95480585 1.005630
## WRATtotal 1.0142231 0.98123115 1.047252
## DigitSpanFwd:SexMen 1.0584906 0.82721892 1.349796
## DigitSpanFwd:PovStatBelow 0.9877360 0.69974951 1.421807
## SexMen:PovStatBelow 1.2314393 0.03214253 45.408760
## DigitSpanFwd:SexMen:PovStatBelow 1.0281966 0.62385173 1.711342
Compare Models 1,2, & 3
anova(DSFlog1,DSFlog2,test = "LR")
## Analysis of Deviance Table
##
## Model 1: PsychAggress ~ DigitSpanFwd
## Model 2: PsychAggress ~ DigitSpanFwd + Age + Sex + PovStat + WRATtotal
## Resid. Df Resid. Dev Df Deviance Pr(>Chi)
## 1 639 501.93
## 2 635 492.71 4 9.215 0.05594 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
anova(DSFlog2,DSFlog3,test = "LR")
## Analysis of Deviance Table
##
## Model 1: PsychAggress ~ DigitSpanFwd + Age + Sex + PovStat + WRATtotal
## Model 2: PsychAggress ~ (DigitSpanFwd + Sex + PovStat)^3 + Age + WRATtotal
## Resid. Df Resid. Dev Df Deviance Pr(>Chi)
## 1 635 492.71
## 2 631 491.88 4 0.83605 0.9336
anova(DSFlog1,DSFlog3,test = "LR")
## Analysis of Deviance Table
##
## Model 1: PsychAggress ~ DigitSpanFwd
## Model 2: PsychAggress ~ (DigitSpanFwd + Sex + PovStat)^3 + Age + WRATtotal
## Resid. Df Resid. Dev Df Deviance Pr(>Chi)
## 1 639 501.93
## 2 631 491.88 8 10.051 0.2615
Suggested Model by Predictors
anova(DSFlog3, 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
## DigitSpanFwd 1 3.4717 639 501.93 0.06243 .
## Sex 1 4.2385 638 497.69 0.03952 *
## PovStat 1 1.9145 637 495.78 0.16647
## Age 1 2.3652 636 493.41 0.12407
## WRATtotal 1 0.6969 635 492.71 0.40381
## DigitSpanFwd:Sex 1 0.2701 634 492.44 0.60330
## DigitSpanFwd:PovStat 1 0.0003 633 492.44 0.98737
## Sex:PovStat 1 0.5539 632 491.89 0.45673
## DigitSpanFwd:Sex:PovStat 1 0.0118 631 491.88 0.91332
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
DSFlog4 <- glm(PsychAggress ~ DigitSpanFwd + Sex, data = Allvars, family = "binomial")
summary(DSFlog4)
##
## Call:
## glm(formula = PsychAggress ~ DigitSpanFwd + Sex, family = "binomial",
## data = Allvars)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.3541 0.4389 0.5084 0.5776 0.7632
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 1.35916 0.41403 3.283 0.00103 **
## DigitSpanFwd 0.10361 0.05357 1.934 0.05310 .
## SexMen -0.48183 0.23569 -2.044 0.04092 *
## ---
## 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: 497.69 on 638 degrees of freedom
## AIC: 503.69
##
## Number of Fisher Scoring iterations: 4
plot(allEffects(DSFlog4))
Digit Span Forward - Physical Assault
Model 1
DSFlog1 <- glm(PhysAssault ~ DigitSpanFwd, data=Allvars,family = "binomial")
summary(DSFlog1)
##
## Call:
## glm(formula = PhysAssault ~ DigitSpanFwd, family = "binomial",
## data = Allvars)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.5494 -0.5236 -0.5136 -0.5038 2.0889
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -2.10336 0.40909 -5.141 2.73e-07 ***
## DigitSpanFwd 0.02063 0.05157 0.400 0.689
## ---
## 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: 482.37 on 639 degrees of freedom
## AIC: 486.37
##
## Number of Fisher Scoring iterations: 4
confint(DSFlog1)
## 2.5 % 97.5 %
## (Intercept) -2.91625120 -1.3096827
## DigitSpanFwd -0.08208486 0.1205407
exp(cbind(OR = coef(DSFlog1), confint(DSFlog1)))
## OR 2.5 % 97.5 %
## (Intercept) 0.1220461 0.05413625 0.2699057
## DigitSpanFwd 1.0208459 0.92119379 1.1281067
Model 2
DSFlog2 <- glm(PhysAssault ~ DigitSpanFwd + Age + Sex + PovStat + WRATtotal, data = Allvars, family = "binomial")
summary(DSFlog2)
##
## Call:
## glm(formula = PhysAssault ~ DigitSpanFwd + Age + Sex + PovStat +
## WRATtotal, family = "binomial", data = Allvars)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.8265 -0.5482 -0.4738 -0.3940 2.3908
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.298525 1.038992 -1.250 0.2114
## DigitSpanFwd -0.003763 0.059056 -0.064 0.9492
## Age -0.033411 0.013806 -2.420 0.0155 *
## SexMen -0.154818 0.244320 -0.634 0.5263
## PovStatBelow 0.486848 0.253372 1.921 0.0547 .
## WRATtotal 0.018102 0.018832 0.961 0.3364
## ---
## 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(DSFlog2)
## 2.5 % 97.5 %
## (Intercept) -3.37067738 0.71074063
## DigitSpanFwd -0.12148521 0.11053782
## Age -0.06090493 -0.00666178
## SexMen -0.63831369 0.32239763
## PovStatBelow -0.01492452 0.98116664
## WRATtotal -0.01785196 0.05611976
exp(cbind(OR = coef(DSFlog2), confint(DSFlog2)))
## OR 2.5 % 97.5 %
## (Intercept) 0.2729341 0.03436635 2.0354983
## DigitSpanFwd 0.9962436 0.88560415 1.1168786
## Age 0.9671408 0.94091269 0.9933604
## SexMen 0.8565711 0.52818235 1.3804336
## PovStatBelow 1.6271794 0.98518630 2.6675665
## WRATtotal 1.0182669 0.98230645 1.0577243
Model 3
DSFlog3 <- glm(PhysAssault ~ (DigitSpanFwd + Sex + PovStat)^3 + Age + WRATtotal, data = Allvars, family = "binomial")
summary(DSFlog3)
##
## Call:
## glm(formula = PhysAssault ~ (DigitSpanFwd + Sex + PovStat)^3 +
## Age + WRATtotal, family = "binomial", data = Allvars)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.8189 -0.5530 -0.4756 -0.3929 2.4095
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.19141 1.18123 -1.009 0.3132
## DigitSpanFwd -0.01788 0.09490 -0.188 0.8505
## SexMen -0.47145 1.09257 -0.432 0.6661
## PovStatBelow 0.30840 1.14433 0.270 0.7875
## Age -0.03343 0.01381 -2.421 0.0155 *
## WRATtotal 0.01844 0.01896 0.973 0.3306
## DigitSpanFwd:SexMen 0.03636 0.13301 0.273 0.7846
## DigitSpanFwd:PovStatBelow 0.01956 0.14872 0.131 0.8954
## SexMen:PovStatBelow 0.55778 1.74829 0.319 0.7497
## DigitSpanFwd:SexMen:PovStatBelow -0.06336 0.22405 -0.283 0.7773
## ---
## 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: 469.30 on 631 degrees of freedom
## AIC: 489.3
##
## Number of Fisher Scoring iterations: 5
confint(DSFlog3)
## 2.5 % 97.5 %
## (Intercept) -3.55771445 1.082542539
## DigitSpanFwd -0.21071775 0.163745918
## SexMen -2.63659095 1.663594559
## PovStatBelow -1.95436295 2.551997385
## Age -0.06093180 -0.006678362
## WRATtotal -0.01776098 0.056701218
## DigitSpanFwd:SexMen -0.22512922 0.298520850
## DigitSpanFwd:PovStatBelow -0.27523533 0.311318253
## SexMen:PovStatBelow -2.87426110 3.999016174
## DigitSpanFwd:SexMen:PovStatBelow -0.50694634 0.374844062
Compare Models 1,2, & 3
anova(DSFlog1,DSFlog2,test = "LR")
## Analysis of Deviance Table
##
## Model 1: PhysAssault ~ DigitSpanFwd
## Model 2: PhysAssault ~ DigitSpanFwd + Age + Sex + PovStat + WRATtotal
## Resid. Df Resid. Dev Df Deviance Pr(>Chi)
## 1 639 482.37
## 2 635 469.42 4 12.952 0.01151 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
anova(DSFlog2,DSFlog3,test = "LR")
## Analysis of Deviance Table
##
## Model 1: PhysAssault ~ DigitSpanFwd + Age + Sex + PovStat + WRATtotal
## Model 2: PhysAssault ~ (DigitSpanFwd + Sex + PovStat)^3 + Age + WRATtotal
## Resid. Df Resid. Dev Df Deviance Pr(>Chi)
## 1 635 469.42
## 2 631 469.30 4 0.12619 0.9981
anova(DSFlog1,DSFlog3,test = "LR")
## Analysis of Deviance Table
##
## Model 1: PhysAssault ~ DigitSpanFwd
## Model 2: PhysAssault ~ (DigitSpanFwd + Sex + PovStat)^3 + Age + WRATtotal
## Resid. Df Resid. Dev Df Deviance Pr(>Chi)
## 1 639 482.37
## 2 631 469.30 8 13.078 0.1092
Suggested Model by predictors
anova(DSFlog3, 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
## DigitSpanFwd 1 0.1591 639 482.37 0.68999
## Sex 1 0.8210 638 481.55 0.36490
## PovStat 1 5.1322 637 476.42 0.02349 *
## Age 1 6.0493 636 470.37 0.01391 *
## WRATtotal 1 0.9494 635 469.42 0.32988
## DigitSpanFwd:Sex 1 0.0134 634 469.41 0.90769
## DigitSpanFwd:PovStat 1 0.0047 633 469.40 0.94509
## Sex:PovStat 1 0.0279 632 469.38 0.86732
## DigitSpanFwd:Sex:PovStat 1 0.0801 631 469.30 0.77717
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
DSFlog4 <- glm(PhysAssault ~Sex + Age, data = Allvars, family = "binomial")
summary(DSFlog4)
##
## Call:
## glm(formula = PhysAssault ~ Sex + Age, family = "binomial", data = Allvars)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.6976 -0.5635 -0.4814 -0.4062 2.3320
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.14711 0.61516 -0.239 0.81100
## SexMen -0.16634 0.24267 -0.685 0.49306
## Age -0.03795 0.01357 -2.797 0.00516 **
## ---
## 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: 473.65 on 638 degrees of freedom
## AIC: 479.65
##
## Number of Fisher Scoring iterations: 5
Digit Span Backward - Psychological Aggression
Model 1
DSBlog1 <- glm(PsychAggress ~ DigitSpanBck, data=Allvars,family = "binomial")
summary(DSBlog1)
##
## Call:
## glm(formula = PsychAggress ~ DigitSpanBck, family = "binomial",
## data = Allvars)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.1240 0.5008 0.5328 0.5666 0.6395
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 1.48331 0.32547 4.557 5.18e-06 ***
## DigitSpanBck 0.06618 0.05387 1.228 0.219
## ---
## 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: 503.85 on 639 degrees of freedom
## AIC: 507.85
##
## Number of Fisher Scoring iterations: 4
confint(DSBlog1)
## 2.5 % 97.5 %
## (Intercept) 0.84949262 2.1271670
## DigitSpanBck -0.03708871 0.1744529
exp(cbind(OR = coef(DSBlog1), confint(DSBlog1)))
## OR 2.5 % 97.5 %
## (Intercept) 4.407523 2.3384601 8.391061
## DigitSpanBck 1.068418 0.9635906 1.190595
Model 2
DSBlog2 <- glm(PsychAggress ~ DigitSpanBck + Age + Sex + PovStat + WRATtotal, data = Allvars, family = "binomial")
summary(DSBlog2)
##
## Call:
## glm(formula = PsychAggress ~ DigitSpanBck + Age + Sex + PovStat +
## WRATtotal, family = "binomial", data = Allvars)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.3545 0.4188 0.4988 0.5806 0.8067
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 2.00097 0.93371 2.143 0.0321 *
## DigitSpanBck 0.03633 0.06183 0.588 0.5569
## Age -0.02133 0.01313 -1.625 0.1042
## SexMen -0.43391 0.23663 -1.834 0.0667 .
## PovStatBelow 0.30340 0.27136 1.118 0.2635
## WRATtotal 0.01871 0.01690 1.107 0.2681
## ---
## 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: 494.17 on 635 degrees of freedom
## AIC: 506.17
##
## Number of Fisher Scoring iterations: 4
confint(DSBlog2)
## 2.5 % 97.5 %
## (Intercept) 0.19390855 3.861708942
## DigitSpanBck -0.08303013 0.159844631
## Age -0.04727809 0.004313792
## SexMen -0.90331488 0.027038043
## PovStatBelow -0.21507588 0.853305067
## WRATtotal -0.01488368 0.051545135
exp(cbind(OR = coef(DSBlog2), confint(DSBlog2)))
## OR 2.5 % 97.5 %
## (Intercept) 7.3961984 1.2139853 47.546536
## DigitSpanBck 1.0369930 0.9203234 1.173329
## Age 0.9788925 0.9538221 1.004323
## SexMen 0.6479681 0.4052242 1.027407
## PovStatBelow 1.3544571 0.8064802 2.347392
## WRATtotal 1.0188890 0.9852265 1.052897
Model 3
DSBlog3 <- glm(PsychAggress ~ (DigitSpanBck + Sex + PovStat)^3 + Age + WRATtotal, data = Allvars, family = "binomial")
summary(DSBlog3)
##
## Call:
## glm(formula = PsychAggress ~ (DigitSpanBck + Sex + PovStat)^3 +
## Age + WRATtotal, family = "binomial", data = Allvars)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.3576 0.3941 0.4862 0.5675 0.9101
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 1.86140 1.04549 1.780 0.0750 .
## DigitSpanBck 0.09323 0.11541 0.808 0.4192
## SexMen -0.62912 0.78860 -0.798 0.4250
## PovStatBelow 1.65113 1.19085 1.387 0.1656
## Age -0.02285 0.01323 -1.728 0.0841 .
## WRATtotal 0.01775 0.01714 1.036 0.3003
## DigitSpanBck:SexMen 0.01376 0.13382 0.103 0.9181
## DigitSpanBck:PovStatBelow -0.27451 0.19329 -1.420 0.1555
## SexMen:PovStatBelow 0.18041 1.54837 0.117 0.9072
## DigitSpanBck:SexMen:PovStatBelow 0.03102 0.24940 0.124 0.9010
## ---
## 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: 489.29 on 631 degrees of freedom
## AIC: 509.29
##
## Number of Fisher Scoring iterations: 5
confint(DSBlog3)
## 2.5 % 97.5 %
## (Intercept) -0.15637002 3.95271949
## DigitSpanBck -0.12406374 0.32908387
## SexMen -2.18681354 0.91147349
## PovStatBelow -0.63382657 4.07184741
## Age -0.04900142 0.00297188
## WRATtotal -0.01629415 0.05105948
## DigitSpanBck:SexMen -0.25325179 0.27307703
## DigitSpanBck:PovStatBelow -0.65478254 0.10995568
## SexMen:PovStatBelow -2.88490444 3.21551659
## DigitSpanBck:SexMen:PovStatBelow -0.45930083 0.52358557
exp(cbind(OR = coef(DSBlog3), confint(DSBlog3)))
## OR 2.5 % 97.5 %
## (Intercept) 6.4327376 0.85524267 52.076797
## DigitSpanBck 1.0977113 0.88332354 1.389694
## SexMen 0.5330596 0.11227394 2.487986
## PovStatBelow 5.2128734 0.53055769 58.665242
## Age 0.9774056 0.95217978 1.002976
## WRATtotal 1.0179052 0.98383788 1.052385
## DigitSpanBck:SexMen 1.0138573 0.77627240 1.314001
## DigitSpanBck:PovStatBelow 0.7599464 0.51955503 1.116229
## SexMen:PovStatBelow 1.1977140 0.05586013 24.916160
## DigitSpanBck:SexMen:PovStatBelow 1.0315050 0.63172517 1.688070
Compare Models 1,2, & 3
anova(DSBlog1,DSBlog2,test = "LR")
## Analysis of Deviance Table
##
## Model 1: PsychAggress ~ DigitSpanBck
## Model 2: PsychAggress ~ DigitSpanBck + Age + Sex + PovStat + WRATtotal
## Resid. Df Resid. Dev Df Deviance Pr(>Chi)
## 1 639 503.85
## 2 635 494.17 4 9.6769 0.04624 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
anova(DSBlog2,DSBlog3,test = "LR")
## Analysis of Deviance Table
##
## Model 1: PsychAggress ~ DigitSpanBck + Age + Sex + PovStat + WRATtotal
## Model 2: PsychAggress ~ (DigitSpanBck + Sex + PovStat)^3 + Age + WRATtotal
## Resid. Df Resid. Dev Df Deviance Pr(>Chi)
## 1 635 494.17
## 2 631 489.29 4 4.8844 0.2994
anova(DSBlog1,DSBlog3,test = "LR")
## Analysis of Deviance Table
##
## Model 1: PsychAggress ~ DigitSpanBck
## Model 2: PsychAggress ~ (DigitSpanBck + Sex + PovStat)^3 + Age + WRATtotal
## Resid. Df Resid. Dev Df Deviance Pr(>Chi)
## 1 639 503.85
## 2 631 489.29 8 14.561 0.06826 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Suggested Model by Predictors
anova(DSBlog3, 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
## DigitSpanBck 1 1.5535 639 503.85 0.21263
## Sex 1 4.0437 638 499.80 0.04434 *
## PovStat 1 1.7275 637 498.08 0.18874
## Age 1 2.6974 636 495.38 0.10051
## WRATtotal 1 1.2083 635 494.17 0.27168
## DigitSpanBck:Sex 1 0.1052 634 494.07 0.74568
## DigitSpanBck:PovStat 1 4.3122 633 489.75 0.03784 *
## Sex:PovStat 1 0.4516 632 489.30 0.50158
## DigitSpanBck:Sex:PovStat 1 0.0155 631 489.29 0.90101
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
DSBlog4 <- glm(PsychAggress ~ (DigitSpanBck + PovStat)^2 + Sex, data = Allvars, family = "binomial")
summary(DSBlog4)
##
## Call:
## glm(formula = PsychAggress ~ (DigitSpanBck + PovStat)^2 + Sex,
## family = "binomial", data = Allvars)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.3307 0.4135 0.4907 0.5743 0.7848
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 1.21783 0.39486 3.084 0.00204 **
## DigitSpanBck 0.13974 0.06319 2.211 0.02701 *
## PovStatBelow 1.77644 0.75525 2.352 0.01867 *
## SexMen -0.47738 0.23636 -2.020 0.04342 *
## DigitSpanBck:PovStatBelow -0.25530 0.12136 -2.104 0.03540 *
## ---
## 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: 493.8 on 636 degrees of freedom
## AIC: 503.8
##
## Number of Fisher Scoring iterations: 4
sim_slopes(DSBlog4 , pred = DigitSpanBck, modx = PovStat, centered = "all",jnplot = TRUE)
## Warning: Johnson-Neyman intervals are not available for factor moderators.
## SIMPLE SLOPES ANALYSIS
##
## Slope of DigitSpanBck when PovStat = Below:
##
## Est. S.E. z val. p
## ------- ------ -------- ------
## -0.12 0.10 -1.12 0.26
##
## Slope of DigitSpanBck when PovStat = Above:
##
## Est. S.E. z val. p
## ------ ------ -------- ------
## 0.14 0.06 2.21 0.03
interact_plot(model = DSBlog4 , pred = DigitSpanBck, modx = PovStat)
Digit Span Backward - Physical Assault
Model 1
DSBlog1 <- glm(PhysAssault ~ DigitSpanBck, data=Allvars,family = "binomial")
summary(DSBlog1)
##
## Call:
## glm(formula = PhysAssault ~ DigitSpanBck, family = "binomial",
## data = Allvars)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.6461 -0.5332 -0.4995 -0.4834 2.2166
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -2.36700 0.33815 -7.000 2.56e-12 ***
## DigitSpanBck 0.06971 0.05143 1.355 0.175
## ---
## 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: 480.73 on 639 degrees of freedom
## AIC: 484.73
##
## Number of Fisher Scoring iterations: 4
confint(DSBlog1)
## 2.5 % 97.5 %
## (Intercept) -3.04161374 -1.7138403
## DigitSpanBck -0.03277605 0.1693352
exp(cbind(OR = coef(DSBlog1), confint(DSBlog1)))
## OR 2.5 % 97.5 %
## (Intercept) 0.09376143 0.04775776 0.1801725
## DigitSpanBck 1.07220115 0.96775526 1.1845171
plot(allEffects(DSBlog1))
Model 2
DSBlog2 <- glm(PhysAssault ~ DigitSpanBck + Age + Sex + PovStat + WRATtotal, data = Allvars, family = "binomial")
summary(DSBlog2)
##
## Call:
## glm(formula = PhysAssault ~ DigitSpanBck + Age + Sex + PovStat +
## WRATtotal, family = "binomial", data = Allvars)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.8505 -0.5507 -0.4663 -0.3877 2.4000
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.345694 1.024586 -1.313 0.1890
## DigitSpanBck 0.059014 0.060632 0.973 0.3304
## Age -0.031851 0.013777 -2.312 0.0208 *
## SexMen -0.170212 0.244791 -0.695 0.4868
## PovStatBelow 0.502315 0.253971 1.978 0.0479 *
## WRATtotal 0.008856 0.019220 0.461 0.6450
## ---
## 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: 468.49 on 635 degrees of freedom
## AIC: 480.49
##
## Number of Fisher Scoring iterations: 5
confint(DSBlog2)
## 2.5 % 97.5 %
## (Intercept) -3.391665037 0.633726337
## DigitSpanBck -0.061320692 0.176938898
## Age -0.059299715 -0.005165805
## SexMen -0.654805236 0.307756720
## PovStatBelow -0.000432283 0.998046083
## WRATtotal -0.027993557 0.047521446
exp(cbind(OR = coef(DSBlog2), confint(DSBlog2)))
## OR 2.5 % 97.5 %
## (Intercept) 0.2603591 0.0336526 1.8846202
## DigitSpanBck 1.0607902 0.9405216 1.1935582
## Age 0.9686509 0.9424243 0.9948475
## SexMen 0.8434860 0.5195432 1.3603700
## PovStatBelow 1.6525430 0.9995678 2.7129757
## WRATtotal 1.0088955 0.9723946 1.0486687
Model 3
DSBlog3 <- glm(PhysAssault ~ (DigitSpanBck + Sex + PovStat)^3 + Age + WRATtotal, data = Allvars, family = "binomial")
summary(DSBlog3)
##
## Call:
## glm(formula = PhysAssault ~ (DigitSpanBck + Sex + PovStat)^3 +
## Age + WRATtotal, family = "binomial", data = Allvars)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.8330 -0.5503 -0.4685 -0.3884 2.4253
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.255613 1.120611 -1.120 0.2625
## DigitSpanBck 0.044520 0.107315 0.415 0.6782
## SexMen -0.455390 0.908308 -0.501 0.6161
## PovStatBelow 0.408942 1.027509 0.398 0.6906
## Age -0.031862 0.013778 -2.313 0.0207 *
## WRATtotal 0.009189 0.019285 0.476 0.6337
## DigitSpanBck:SexMen 0.038519 0.133091 0.289 0.7723
## DigitSpanBck:PovStatBelow 0.008996 0.165178 0.054 0.9566
## SexMen:PovStatBelow 0.440727 1.437951 0.306 0.7592
## DigitSpanBck:SexMen:PovStatBelow -0.056578 0.225189 -0.251 0.8016
## ---
## 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: 468.32 on 631 degrees of freedom
## AIC: 488.32
##
## Number of Fisher Scoring iterations: 5
confint(DSBlog3)
## 2.5 % 97.5 %
## (Intercept) -3.50061722 0.901710583
## DigitSpanBck -0.17306222 0.249958353
## SexMen -2.25539642 1.319990640
## PovStatBelow -1.63003035 2.417495430
## Age -0.05931304 -0.005176632
## WRATtotal -0.02780734 0.047973668
## DigitSpanBck:SexMen -0.22130482 0.302664130
## DigitSpanBck:PovStatBelow -0.31741438 0.334113806
## SexMen:PovStatBelow -2.38215259 3.271157548
## DigitSpanBck:SexMen:PovStatBelow -0.50186032 0.384406408
exp(cbind(OR = coef(DSBlog3), confint(DSBlog3)))
## OR 2.5 % 97.5 %
## (Intercept) 0.2849011 0.03017875 2.4638141
## DigitSpanBck 1.0455263 0.84108528 1.2839719
## SexMen 0.6342003 0.10483198 3.7433863
## PovStatBelow 1.5052248 0.19592363 11.2177285
## Age 0.9686402 0.94241171 0.9948367
## WRATtotal 1.0092314 0.97257573 1.0491430
## DigitSpanBck:SexMen 1.0392708 0.80147234 1.3534598
## DigitSpanBck:PovStatBelow 1.0090362 0.72802901 1.3967021
## SexMen:PovStatBelow 1.5538360 0.09235157 26.3418136
## DigitSpanBck:SexMen:PovStatBelow 0.9449929 0.60540337 1.4687422
Compare Models 1,2, & 3
anova(DSBlog1,DSBlog2,test = "LR")
## Analysis of Deviance Table
##
## Model 1: PhysAssault ~ DigitSpanBck
## Model 2: PhysAssault ~ DigitSpanBck + Age + Sex + PovStat + WRATtotal
## Resid. Df Resid. Dev Df Deviance Pr(>Chi)
## 1 639 480.73
## 2 635 468.49 4 12.246 0.01561 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
anova(DSBlog2,DSBlog3,test = "LR")
## Analysis of Deviance Table
##
## Model 1: PhysAssault ~ DigitSpanBck + Age + Sex + PovStat + WRATtotal
## Model 2: PhysAssault ~ (DigitSpanBck + Sex + PovStat)^3 + Age + WRATtotal
## Resid. Df Resid. Dev Df Deviance Pr(>Chi)
## 1 635 468.49
## 2 631 468.32 4 0.16486 0.9968
anova(DSBlog1,DSBlog3,test = "LR")
## Analysis of Deviance Table
##
## Model 1: PhysAssault ~ DigitSpanBck
## Model 2: PhysAssault ~ (DigitSpanBck + Sex + PovStat)^3 + Age + WRATtotal
## Resid. Df Resid. Dev Df Deviance Pr(>Chi)
## 1 639 480.73
## 2 631 468.32 8 12.411 0.1338
Suggested Model by Predictors
anova(DSBlog3, 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
## DigitSpanBck 1 1.7978 639 480.73 0.17998
## Sex 1 0.9489 638 479.79 0.33001
## PovStat 1 5.5965 637 474.19 0.01800 *
## Age 1 5.4858 636 468.70 0.01917 *
## WRATtotal 1 0.2147 635 468.49 0.64310
## DigitSpanBck:Sex 1 0.0270 634 468.46 0.86958
## DigitSpanBck:PovStat 1 0.0336 633 468.43 0.85458
## Sex:PovStat 1 0.0411 632 468.39 0.83932
## DigitSpanBck:Sex:PovStat 1 0.0632 631 468.32 0.80152
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
DSBlog4 <- glm(PhysAssault ~ PovStat + Age, data = Allvars, family = "binomial")
summary(DSBlog4)
##
## Call:
## glm(formula = PhysAssault ~ PovStat + Age, family = "binomial",
## data = Allvars)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.7492 -0.5539 -0.4770 -0.3956 2.3402
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.53355 0.64202 -0.831 0.4059
## PovStatBelow 0.45113 0.24862 1.815 0.0696 .
## Age -0.03471 0.01372 -2.530 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: 482.53 on 640 degrees of freedom
## Residual deviance: 470.90 on 638 degrees of freedom
## AIC: 476.9
##
## Number of Fisher Scoring iterations: 5
Animal Naming Test - Psychological Aggression
Model 1
ANlog1 <- glm(PsychAggress ~ FluencyWord, data=Allvars,family = "binomial")
summary(ANlog1)
##
## Call:
## glm(formula = PsychAggress ~ FluencyWord, family = "binomial",
## data = Allvars)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.1217 0.4953 0.5325 0.5587 0.6215
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 1.36584 0.42838 3.188 0.00143 **
## FluencyWord 0.02579 0.02163 1.192 0.23321
## ---
## 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: 503.95 on 639 degrees of freedom
## AIC: 507.95
##
## Number of Fisher Scoring iterations: 4
confint(ANlog1)
## 2.5 % 97.5 %
## (Intercept) 0.5308810 2.21242735
## FluencyWord -0.0159852 0.06893414
exp(cbind(OR = coef(ANlog1), confint(ANlog1)))
## OR 2.5 % 97.5 %
## (Intercept) 3.919008 1.7004297 9.137870
## FluencyWord 1.026123 0.9841419 1.071366
Model 2
ANlog2 <- glm(PsychAggress ~ FluencyWord + Age + Sex + PovStat + WRATtotal, data = Allvars, family = "binomial")
summary(ANlog2)
##
## Call:
## glm(formula = PsychAggress ~ FluencyWord + Age + Sex + PovStat +
## WRATtotal, family = "binomial", data = Allvars)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.3876 0.4129 0.4958 0.5797 0.8605
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 1.76445 0.96978 1.819 0.0688 .
## FluencyWord 0.02273 0.02416 0.941 0.3468
## Age -0.02055 0.01318 -1.559 0.1189
## SexMen -0.46955 0.24092 -1.949 0.0513 .
## PovStatBelow 0.30851 0.27181 1.135 0.2564
## WRATtotal 0.01839 0.01566 1.175 0.2400
## ---
## 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: 493.62 on 635 degrees of freedom
## AIC: 505.62
##
## Number of Fisher Scoring iterations: 5
confint(ANlog2)
## 2.5 % 97.5 %
## (Intercept) -0.11687212 3.6921775664
## FluencyWord -0.02410873 0.0707277787
## Age -0.04657233 0.0051886613
## SexMen -0.94753111 -0.0005008865
## PovStatBelow -0.21071033 0.8592819822
## WRATtotal -0.01283072 0.0487244784
exp(cbind(OR = coef(ANlog2), confint(ANlog2)))
## OR 2.5 % 97.5 %
## (Intercept) 5.8383698 0.8896990 40.1321423
## FluencyWord 1.0229911 0.9761796 1.0732890
## Age 0.9796646 0.9544955 1.0052021
## SexMen 0.6252811 0.3876970 0.9994992
## PovStatBelow 1.3613906 0.8100087 2.3614645
## WRATtotal 1.0185651 0.9872512 1.0499310
Model 3
ANlog3 <- glm(PsychAggress ~ (FluencyWord + Sex + PovStat)^3 + Age + WRATtotal, data = Allvars, family = "binomial")
summary(ANlog3)
##
## Call:
## glm(formula = PsychAggress ~ (FluencyWord + Sex + PovStat)^3 +
## Age + WRATtotal, family = "binomial", data = Allvars)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.3284 0.4143 0.4835 0.5639 1.0062
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 2.008377 1.153266 1.741 0.0816 .
## FluencyWord 0.011820 0.038971 0.303 0.7617
## SexMen -1.424781 1.027353 -1.387 0.1655
## PovStatBelow -0.019326 1.426951 -0.014 0.9892
## Age -0.020953 0.013261 -1.580 0.1141
## WRATtotal 0.019146 0.015830 1.209 0.2265
## FluencyWord:SexMen 0.043940 0.052092 0.844 0.3989
## FluencyWord:PovStatBelow 0.007169 0.077334 0.093 0.9261
## SexMen:PovStatBelow 2.731876 2.084764 1.310 0.1901
## FluencyWord:SexMen:PovStatBelow -0.119742 0.105978 -1.130 0.2585
## ---
## 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: 490.61 on 631 degrees of freedom
## AIC: 510.61
##
## Number of Fisher Scoring iterations: 5
confint(ANlog3)
## 2.5 % 97.5 %
## (Intercept) -0.23228008 4.29821884
## FluencyWord -0.06215661 0.09117398
## SexMen -3.44849510 0.58663361
## PovStatBelow -2.81316385 2.80853892
## Age -0.04715221 0.00494707
## WRATtotal -0.01238725 0.04984477
## FluencyWord:SexMen -0.05919918 0.14563277
## FluencyWord:PovStatBelow -0.14070102 0.16460308
## SexMen:PovStatBelow -1.31542020 6.89273664
## FluencyWord:SexMen:PovStatBelow -0.33129450 0.08587307
exp(cbind(OR = coef(ANlog3), confint(ANlog3)))
## OR 2.5 % 97.5 %
## (Intercept) 7.4512169 0.79272407 73.568640
## FluencyWord 1.0118901 0.93973570 1.095460
## SexMen 0.2405612 0.03179345 1.797926
## PovStatBelow 0.9808595 0.06001481 16.585667
## Age 0.9792650 0.95394219 1.004959
## WRATtotal 1.0193306 0.98768916 1.051108
## FluencyWord:SexMen 1.0449201 0.94251902 1.156771
## FluencyWord:PovStatBelow 1.0071944 0.86874901 1.178925
## SexMen:PovStatBelow 15.3616755 0.26836153 985.093576
## FluencyWord:SexMen:PovStatBelow 0.8871492 0.71799369 1.089668
Compare Models 1,2, & 3
anova(ANlog1,ANlog2,test = "LR")
## Analysis of Deviance Table
##
## Model 1: PsychAggress ~ FluencyWord
## Model 2: PsychAggress ~ FluencyWord + Age + Sex + PovStat + WRATtotal
## Resid. Df Resid. Dev Df Deviance Pr(>Chi)
## 1 639 503.95
## 2 635 493.62 4 10.329 0.03524 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
anova(ANlog2,ANlog3,test = "LR")
## Analysis of Deviance Table
##
## Model 1: PsychAggress ~ FluencyWord + Age + Sex + PovStat + WRATtotal
## Model 2: PsychAggress ~ (FluencyWord + Sex + PovStat)^3 + Age + WRATtotal
## Resid. Df Resid. Dev Df Deviance Pr(>Chi)
## 1 635 493.62
## 2 631 490.61 4 3.0114 0.5559
anova(ANlog1,ANlog3,test = "LR")
## Analysis of Deviance Table
##
## Model 1: PsychAggress ~ FluencyWord
## Model 2: PsychAggress ~ (FluencyWord + Sex + PovStat)^3 + Age + WRATtotal
## Resid. Df Resid. Dev Df Deviance Pr(>Chi)
## 1 639 503.95
## 2 631 490.61 8 13.34 0.1007
Suggested Model by Predictors
anova(ANlog3, 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
## FluencyWord 1 1.4483 639 503.95 0.22880
## Sex 1 4.7909 638 499.16 0.02861 *
## PovStat 1 1.7025 637 497.46 0.19196
## Age 1 2.4815 636 494.98 0.11519
## WRATtotal 1 1.3539 635 493.62 0.24461
## FluencyWord:Sex 1 0.0965 634 493.53 0.75604
## FluencyWord:PovStat 1 0.9028 633 492.63 0.34204
## Sex:PovStat 1 0.7176 632 491.91 0.39692
## FluencyWord:Sex:PovStat 1 1.2944 631 490.61 0.25523
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
ANlog4 <- glm(PsychAggress ~ Sex, data = Allvars, family = "binomial")
summary(ANlog4)
##
## Call:
## glm(formula = PsychAggress ~ Sex, family = "binomial", data = Allvars)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.1065 0.4799 0.4799 0.5931 0.5931
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 2.1035 0.1765 11.915 <2e-16 ***
## SexMen -0.4548 0.2345 -1.939 0.0525 .
## ---
## 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: 501.59 on 639 degrees of freedom
## AIC: 505.59
##
## Number of Fisher Scoring iterations: 4
Animal Naming Test - Physical Assault
Model 1
ANlog1 <- glm(PhysAssault ~ FluencyWord, data=Allvars,family = "binomial")
summary(ANlog1)
##
## Call:
## glm(formula = PhysAssault ~ FluencyWord, family = "binomial",
## data = Allvars)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.5534 -0.5265 -0.5149 -0.4979 2.1386
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.71589 0.44187 -3.883 0.000103 ***
## FluencyWord -0.01189 0.02198 -0.541 0.588385
## ---
## 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: 482.24 on 639 degrees of freedom
## AIC: 486.24
##
## Number of Fisher Scoring iterations: 4
confint(ANlog1)
## 2.5 % 97.5 %
## (Intercept) -2.59122387 -0.85664979
## FluencyWord -0.05568067 0.03060649
exp(cbind(OR = coef(ANlog1), confint(ANlog1)))
## OR 2.5 % 97.5 %
## (Intercept) 0.1798035 0.07492828 0.4245821
## FluencyWord 0.9881767 0.94584113 1.0310797
Model 2
ANlog2 <- glm(PhysAssault ~ FluencyWord + Age + Sex + PovStat + WRATtotal, data = Allvars, family = "binomial")
summary(ANlog2)
##
## Call:
## glm(formula = PhysAssault ~ FluencyWord + Age + Sex + PovStat +
## WRATtotal, family = "binomial", data = Allvars)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.8546 -0.5507 -0.4755 -0.3870 2.4488
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.04609 1.06198 -0.985 0.3246
## FluencyWord -0.02460 0.02437 -1.009 0.3129
## Age -0.03521 0.01391 -2.531 0.0114 *
## SexMen -0.11481 0.24751 -0.464 0.6427
## PovStatBelow 0.47095 0.25358 1.857 0.0633 .
## WRATtotal 0.02420 0.01833 1.320 0.1868
## ---
## 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: 468.39 on 635 degrees of freedom
## AIC: 480.39
##
## Number of Fisher Scoring iterations: 5
confint(ANlog2)
## 2.5 % 97.5 %
## (Intercept) -3.16048185 1.011381105
## FluencyWord -0.07308124 0.022627659
## Age -0.06293818 -0.008280836
## SexMen -0.60420415 0.368987112
## PovStatBelow -0.03137323 0.965505520
## WRATtotal -0.01073217 0.061288899
exp(cbind(OR = coef(ANlog2), confint(ANlog2)))
## OR 2.5 % 97.5 %
## (Intercept) 0.3513075 0.0424053 2.7493956
## FluencyWord 0.9757021 0.9295253 1.0228856
## Age 0.9654027 0.9390015 0.9917534
## SexMen 0.8915333 0.5465092 1.4462690
## PovStatBelow 1.6015097 0.9691138 2.6261149
## WRATtotal 1.0244978 0.9893252 1.0632060
Model 3
ANlog3 <- glm(PhysAssault ~ (FluencyWord + Sex + PovStat)^3 + Age + WRATtotal, data = Allvars, family = "binomial")
summary(ANlog3)
##
## Call:
## glm(formula = PhysAssault ~ (FluencyWord + Sex + PovStat)^3 +
## Age + WRATtotal, family = "binomial", data = Allvars)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.8525 -0.5493 -0.4681 -0.3801 2.5158
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.600446 1.212631 -1.320 0.1869
## FluencyWord 0.003624 0.036624 0.099 0.9212
## SexMen 0.975863 1.161915 0.840 0.4010
## PovStatBelow 1.495936 1.229964 1.216 0.2239
## Age -0.034897 0.013937 -2.504 0.0123 *
## WRATtotal 0.024426 0.018371 1.330 0.1837
## FluencyWord:SexMen -0.056565 0.056061 -1.009 0.3130
## FluencyWord:PovStatBelow -0.057081 0.064744 -0.882 0.3780
## SexMen:PovStatBelow -1.816965 1.938739 -0.937 0.3487
## FluencyWord:SexMen:PovStatBelow 0.098700 0.097335 1.014 0.3106
## ---
## 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: 466.97 on 631 degrees of freedom
## AIC: 486.97
##
## Number of Fisher Scoring iterations: 5
confint(ANlog3)
## 2.5 % 97.5 %
## (Intercept) -4.01506723 0.747960669
## FluencyWord -0.06998106 0.074381636
## SexMen -1.31267722 3.255391216
## PovStatBelow -0.90815521 3.931751771
## Age -0.06267745 -0.007917641
## WRATtotal -0.01061634 0.061555509
## FluencyWord:SexMen -0.16772931 0.052793528
## FluencyWord:PovStatBelow -0.18772678 0.067587070
## SexMen:PovStatBelow -5.66030108 1.961694248
## FluencyWord:SexMen:PovStatBelow -0.09128825 0.291518237
exp(cbind(OR = coef(ANlog3), confint(ANlog3)))
## OR 2.5 % 97.5 %
## (Intercept) 0.2018065 0.018041742 2.1126872
## FluencyWord 1.0036301 0.932411483 1.0772178
## SexMen 2.6534555 0.269098655 25.9297567
## PovStatBelow 4.4635102 0.403267482 50.9962332
## Age 0.9657049 0.939246381 0.9921136
## WRATtotal 1.0247266 0.989439812 1.0634895
## FluencyWord:SexMen 0.9450048 0.845582691 1.0542120
## FluencyWord:PovStatBelow 0.9445178 0.828841128 1.0699234
## SexMen:PovStatBelow 0.1625182 0.003481469 7.1113653
## FluencyWord:SexMen:PovStatBelow 1.1037354 0.912754575 1.3384580
Compare Models 1,2, & 3
anova(ANlog1,ANlog2,test = "LR")
## Analysis of Deviance Table
##
## Model 1: PhysAssault ~ FluencyWord
## Model 2: PhysAssault ~ FluencyWord + Age + Sex + PovStat + WRATtotal
## Resid. Df Resid. Dev Df Deviance Pr(>Chi)
## 1 639 482.24
## 2 635 468.39 4 13.843 0.007812 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
anova(ANlog2,ANlog3,test = "LR")
## Analysis of Deviance Table
##
## Model 1: PhysAssault ~ FluencyWord + Age + Sex + PovStat + WRATtotal
## Model 2: PhysAssault ~ (FluencyWord + Sex + PovStat)^3 + Age + WRATtotal
## Resid. Df Resid. Dev Df Deviance Pr(>Chi)
## 1 635 468.39
## 2 631 466.97 4 1.4275 0.8394
anova(ANlog1,ANlog3,test = "LR")
## Analysis of Deviance Table
##
## Model 1: PhysAssault ~ FluencyWord
## Model 2: PhysAssault ~ (FluencyWord + Sex + PovStat)^3 + Age + WRATtotal
## Resid. Df Resid. Dev Df Deviance Pr(>Chi)
## 1 639 482.24
## 2 631 466.97 8 15.271 0.05409 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Suggested Model by Predictors
anova(ANlog3, 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
## FluencyWord 1 0.2952 639 482.24 0.58688
## Sex 1 0.6488 638 481.59 0.42053
## PovStat 1 4.7487 637 476.84 0.02932 *
## Age 1 6.6326 636 470.21 0.01001 *
## WRATtotal 1 1.8132 635 468.39 0.17812
## FluencyWord:Sex 1 0.2920 634 468.10 0.58895
## FluencyWord:PovStat 1 0.0765 633 468.03 0.78207
## Sex:PovStat 1 0.0245 632 468.00 0.87570
## FluencyWord:Sex:PovStat 1 1.0345 631 466.97 0.30910
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
ANlog4 <- glm(PsychAggress ~ PovStat + Age, data = Allvars, family = "binomial")
summary(ANlog4)
##
## 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
Stroop Color-Word Test - Psychological Aggression
Model 1
SCWTlog1 <- glm(PsychAggress ~ StroopMixed, data=StroopMixed,family = "binomial")
summary(SCWTlog1)
##
## Call:
## glm(formula = PsychAggress ~ StroopMixed, family = "binomial",
## data = StroopMixed)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.3248 0.4332 0.5120 0.5923 0.8944
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.55268 0.38541 1.434 0.15157
## StroopMixed 0.03925 0.01221 3.213 0.00131 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 452.04 on 547 degrees of freedom
## Residual deviance: 441.36 on 546 degrees of freedom
## AIC: 445.36
##
## Number of Fisher Scoring iterations: 4
confint(SCWTlog1)
## 2.5 % 97.5 %
## (Intercept) -0.19647466 1.31824277
## StroopMixed 0.01558201 0.06356161
exp(cbind(OR = coef(SCWTlog1), confint(SCWTlog1)))
## OR 2.5 % 97.5 %
## (Intercept) 1.737909 0.8216221 3.736849
## StroopMixed 1.040030 1.0157040 1.065625
plot(predictorEffect("StroopMixed",SCWTlog1))
Model 2
SCWTlog2 <- glm(PsychAggress ~ StroopMixed + Age + Sex + PovStat + WRATtotal, data = StroopMixed, family = "binomial")
summary(SCWTlog2)
##
## Call:
## glm(formula = PsychAggress ~ StroopMixed + Age + Sex + PovStat +
## WRATtotal, family = "binomial", data = StroopMixed)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.3956 0.4030 0.5069 0.5948 0.9438
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.99698 1.12674 0.885 0.3762
## StroopMixed 0.02973 0.01419 2.095 0.0361 *
## Age -0.01789 0.01502 -1.191 0.2338
## SexMen -0.32165 0.24818 -1.296 0.1950
## PovStatBelow 0.28470 0.28846 0.987 0.3237
## WRATtotal 0.01800 0.01818 0.990 0.3222
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 452.04 on 547 degrees of freedom
## Residual deviance: 435.89 on 542 degrees of freedom
## AIC: 447.89
##
## Number of Fisher Scoring iterations: 5
confint(SCWTlog2)
## 2.5 % 97.5 %
## (Intercept) -1.202319724 3.22403985
## StroopMixed 0.002045792 0.05778365
## Age -0.047571215 0.01146232
## SexMen -0.811751656 0.16397948
## PovStatBelow -0.267438077 0.86796109
## WRATtotal -0.018211855 0.05328156
exp(cbind(OR = coef(SCWTlog2), confint(SCWTlog2)))
## OR 2.5 % 97.5 %
## (Intercept) 2.7100891 0.3004963 25.129435
## StroopMixed 1.0301771 1.0020479 1.059486
## Age 0.9822704 0.9535426 1.011528
## SexMen 0.7249506 0.4440795 1.178190
## PovStatBelow 1.3293567 0.7653377 2.382049
## WRATtotal 1.0181610 0.9819530 1.054727
plot(allEffects(SCWTlog2))
Model 3
SCWTlog3 <- glm(PsychAggress ~ (StroopMixed + Sex + PovStat)^3 + Age + WRATtotal, data = StroopMixed, family = "binomial")
summary(SCWTlog3)
##
## Call:
## glm(formula = PsychAggress ~ (StroopMixed + Sex + PovStat)^3 +
## Age + WRATtotal, family = "binomial", data = StroopMixed)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.3828 0.3929 0.5067 0.6024 0.9833
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.51878 1.25468 0.413 0.6793
## StroopMixed 0.04568 0.02265 2.016 0.0438 *
## SexMen 0.36679 0.92214 0.398 0.6908
## PovStatBelow 0.47415 1.32994 0.357 0.7215
## Age -0.01746 0.01506 -1.159 0.2463
## WRATtotal 0.01894 0.01836 1.032 0.3022
## StroopMixed:SexMen -0.02635 0.02888 -0.912 0.3615
## StroopMixed:PovStatBelow -0.01309 0.04323 -0.303 0.7621
## SexMen:PovStatBelow -0.12800 1.76637 -0.072 0.9422
## StroopMixed:SexMen:PovStatBelow 0.01670 0.05833 0.286 0.7747
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 452.04 on 547 degrees of freedom
## Residual deviance: 434.45 on 538 degrees of freedom
## AIC: 454.45
##
## Number of Fisher Scoring iterations: 5
confint(SCWTlog3)
## 2.5 % 97.5 %
## (Intercept) -1.933308023 2.99882242
## StroopMixed 0.002187172 0.09153845
## SexMen -1.446357087 2.18326044
## PovStatBelow -2.110003807 3.15042319
## Age -0.047217545 0.01196513
## WRATtotal -0.017581384 0.05460431
## StroopMixed:SexMen -0.083643507 0.02999360
## StroopMixed:PovStatBelow -0.096984597 0.07394836
## SexMen:PovStatBelow -3.603769499 3.36101186
## StroopMixed:SexMen:PovStatBelow -0.098817961 0.13091968
exp(cbind(OR = coef(SCWTlog3), confint(SCWTlog3)))
## OR 2.5 % 97.5 %
## (Intercept) 1.6799779 0.14466884 20.061899
## StroopMixed 1.0467372 1.00218957 1.095859
## SexMen 1.4430962 0.23542637 8.875196
## PovStatBelow 1.6066418 0.12123750 23.345942
## Age 0.9826880 0.95387986 1.012037
## WRATtotal 1.0191218 0.98257227 1.056123
## StroopMixed:SexMen 0.9739945 0.91975909 1.030448
## StroopMixed:PovStatBelow 0.9869981 0.90756999 1.076751
## SexMen:PovStatBelow 0.8798572 0.02722092 28.818336
## StroopMixed:SexMen:PovStatBelow 1.0168376 0.90590760 1.139876
plot(predictorEffect("StroopMixed",SCWTlog3))
Compare Models 1,2, & 3
anova(SCWTlog1,SCWTlog2,test = "LR")
## Analysis of Deviance Table
##
## Model 1: PsychAggress ~ StroopMixed
## Model 2: PsychAggress ~ StroopMixed + Age + Sex + PovStat + WRATtotal
## Resid. Df Resid. Dev Df Deviance Pr(>Chi)
## 1 546 441.36
## 2 542 435.89 4 5.4703 0.2424
anova(SCWTlog2,SCWTlog3,test = "LR")
## Analysis of Deviance Table
##
## Model 1: PsychAggress ~ StroopMixed + Age + Sex + PovStat + WRATtotal
## Model 2: PsychAggress ~ (StroopMixed + Sex + PovStat)^3 + Age + WRATtotal
## Resid. Df Resid. Dev Df Deviance Pr(>Chi)
## 1 542 435.89
## 2 538 434.45 4 1.4343 0.8382
anova(SCWTlog1,SCWTlog3,test = "LR")
## Analysis of Deviance Table
##
## Model 1: PsychAggress ~ StroopMixed
## Model 2: PsychAggress ~ (StroopMixed + Sex + PovStat)^3 + Age + WRATtotal
## Resid. Df Resid. Dev Df Deviance Pr(>Chi)
## 1 546 441.36
## 2 538 434.45 8 6.9046 0.547
Suggested Model by Predictors
anova(SCWTlog3, 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 547 452.04
## StroopMixed 1 10.6840 546 441.36 0.001081 **
## Sex 1 1.8894 545 439.47 0.169269
## PovStat 1 1.3364 544 438.13 0.247664
## Age 1 1.2788 543 436.85 0.258129
## WRATtotal 1 0.9657 542 435.89 0.325757
## StroopMixed:Sex 1 0.9346 541 434.95 0.333666
## StroopMixed:PovStat 1 0.0253 540 434.93 0.873703
## Sex:PovStat 1 0.3926 539 434.53 0.530935
## StroopMixed:Sex:PovStat 1 0.0818 538 434.45 0.774897
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
SCWTlog4 <- glm(PsychAggress ~ StroopMixed, data = StroopMixed, family = "binomial")
summary(SCWTlog4)
##
## Call:
## glm(formula = PsychAggress ~ StroopMixed, family = "binomial",
## data = StroopMixed)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.3248 0.4332 0.5120 0.5923 0.8944
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.55268 0.38541 1.434 0.15157
## StroopMixed 0.03925 0.01221 3.213 0.00131 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 452.04 on 547 degrees of freedom
## Residual deviance: 441.36 on 546 degrees of freedom
## AIC: 445.36
##
## Number of Fisher Scoring iterations: 4
plot(allEffects(SCWTlog4))
Stroop Color-Word Test - Physical Assault
Model 1
SCWTlog1 <- glm(PhysAssault ~ StroopMixed, data=StroopMixed,family = "binomial")
summary(SCWTlog1)
##
## Call:
## glm(formula = PhysAssault ~ StroopMixed, family = "binomial",
## data = StroopMixed)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.5817 -0.5063 -0.4888 -0.4695 2.1974
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -2.39524 0.45602 -5.252 1.5e-07 ***
## StroopMixed 0.01067 0.01296 0.823 0.41
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 391.02 on 547 degrees of freedom
## Residual deviance: 390.34 on 546 degrees of freedom
## AIC: 394.34
##
## Number of Fisher Scoring iterations: 4
confint(SCWTlog1)
## 2.5 % 97.5 %
## (Intercept) -3.31626204 -1.52583659
## StroopMixed -0.01475071 0.03614618
exp(cbind(OR = coef(SCWTlog1), confint(SCWTlog1)))
## OR 2.5 % 97.5 %
## (Intercept) 0.09115035 0.03628822 0.2174391
## StroopMixed 1.01072943 0.98535755 1.0368074
Model 2
SCWTlog2 <- glm(PhysAssault ~ StroopMixed + Age + Sex + PovStat + WRATtotal, data = StroopMixed, family = "binomial")
summary(SCWTlog2)
##
## Call:
## glm(formula = PhysAssault ~ StroopMixed + Age + Sex + PovStat +
## WRATtotal, family = "binomial", data = StroopMixed)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.8575 -0.5252 -0.4387 -0.3595 2.4818
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.923475 1.308109 -1.470 0.1414
## StroopMixed -0.002239 0.015751 -0.142 0.8870
## Age -0.030718 0.016570 -1.854 0.0638 .
## SexMen -0.340829 0.279464 -1.220 0.2226
## PovStatBelow 0.670720 0.289539 2.317 0.0205 *
## WRATtotal 0.028233 0.022555 1.252 0.2107
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 391.02 on 547 degrees of freedom
## Residual deviance: 376.77 on 542 degrees of freedom
## AIC: 388.77
##
## Number of Fisher Scoring iterations: 5
confint(SCWTlog2)
## 2.5 % 97.5 %
## (Intercept) -4.52828769 0.613429348
## StroopMixed -0.03313450 0.028730582
## Age -0.06373628 0.001399406
## SexMen -0.89963957 0.200617507
## PovStatBelow 0.09865659 1.237696041
## WRATtotal -0.01471822 0.073934791
exp(cbind(OR = coef(SCWTlog2), confint(SCWTlog2)))
## OR 2.5 % 97.5 %
## (Intercept) 0.1460984 0.01079915 1.846754
## StroopMixed 0.9977633 0.96740843 1.029147
## Age 0.9697488 0.93825241 1.001400
## SexMen 0.7111802 0.40671622 1.222157
## PovStatBelow 1.9556456 1.10368721 3.447661
## WRATtotal 1.0286353 0.98538956 1.076737
Model 3
SCWTlog3 <- glm(PhysAssault ~ (StroopMixed + Sex + PovStat)^3 + Age + WRATtotal, data = StroopMixed, family = "binomial")
summary(SCWTlog3)
##
## Call:
## glm(formula = PhysAssault ~ (StroopMixed + Sex + PovStat)^3 +
## Age + WRATtotal, family = "binomial", data = StroopMixed)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.8174 -0.5564 -0.4236 -0.3232 2.5111
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -2.78652 1.43745 -1.939 0.0526 .
## StroopMixed 0.02099 0.02257 0.930 0.3524
## SexMen 1.84015 1.24297 1.480 0.1388
## PovStatBelow 1.24604 1.35126 0.922 0.3565
## Age -0.02924 0.01667 -1.754 0.0794 .
## WRATtotal 0.02940 0.02237 1.314 0.1888
## StroopMixed:SexMen -0.07248 0.03617 -2.004 0.0451 *
## StroopMixed:PovStatBelow -0.02215 0.03823 -0.579 0.5623
## SexMen:PovStatBelow -2.03683 1.95253 -1.043 0.2969
## StroopMixed:SexMen:PovStatBelow 0.07915 0.05825 1.359 0.1742
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 391.02 on 547 degrees of freedom
## Residual deviance: 371.47 on 538 degrees of freedom
## AIC: 391.47
##
## Number of Fisher Scoring iterations: 5
confint(SCWTlog3)
## 2.5 % 97.5 %
## (Intercept) -5.66663487 -0.016991478
## StroopMixed -0.02299517 0.065885642
## SexMen -0.63428162 4.271705151
## PovStatBelow -1.46094828 3.874539036
## Age -0.06242361 0.003099514
## WRATtotal -0.01319397 0.074756970
## StroopMixed:SexMen -0.14463657 -0.002266140
## StroopMixed:PovStatBelow -0.09735843 0.053418320
## SexMen:PovStatBelow -5.89405066 1.798465502
## StroopMixed:SexMen:PovStatBelow -0.03451700 0.194549935
exp(cbind(OR = coef(SCWTlog3), confint(SCWTlog3)))
## OR 2.5 % 97.5 %
## (Intercept) 0.06163505 0.003459487 0.9831521
## StroopMixed 1.02121051 0.977267205 1.0681046
## SexMen 6.29748378 0.530316321 71.6436948
## PovStatBelow 3.47654803 0.232016154 48.1604929
## Age 0.97118439 0.939484830 1.0031043
## WRATtotal 1.02983659 0.986892686 1.0776222
## StroopMixed:SexMen 0.93008782 0.865336727 0.9977364
## StroopMixed:PovStatBelow 0.97809470 0.907230769 1.0548708
## SexMen:PovStatBelow 0.13044162 0.002755791 6.0403714
## StroopMixed:SexMen:PovStatBelow 1.08236894 0.966071912 1.2147641
interact_plot(model = SCWTlog3, pred = StroopMixed, modx = Sex)
Compare Models 1,2, & 3
anova(SCWTlog1,SCWTlog2,test = "LR")
## Analysis of Deviance Table
##
## Model 1: PhysAssault ~ StroopMixed
## Model 2: PhysAssault ~ StroopMixed + Age + Sex + PovStat + WRATtotal
## Resid. Df Resid. Dev Df Deviance Pr(>Chi)
## 1 546 390.34
## 2 542 376.77 4 13.566 0.008816 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
anova(SCWTlog2,SCWTlog3,test = "LR")
## Analysis of Deviance Table
##
## Model 1: PhysAssault ~ StroopMixed + Age + Sex + PovStat + WRATtotal
## Model 2: PhysAssault ~ (StroopMixed + Sex + PovStat)^3 + Age + WRATtotal
## Resid. Df Resid. Dev Df Deviance Pr(>Chi)
## 1 542 376.77
## 2 538 371.47 4 5.3085 0.2571
anova(SCWTlog1,SCWTlog3,test = "LR")
## Analysis of Deviance Table
##
## Model 1: PhysAssault ~ StroopMixed
## Model 2: PhysAssault ~ (StroopMixed + Sex + PovStat)^3 + Age + WRATtotal
## Resid. Df Resid. Dev Df Deviance Pr(>Chi)
## 1 546 390.34
## 2 538 371.47 8 18.875 0.01554 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Suggested Model by Predictors
anova(SCWTlog3, 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 547 391.02
## StroopMixed 1 0.6780 546 390.34 0.410267
## Sex 1 1.9635 545 388.38 0.161145
## PovStat 1 6.8160 544 381.56 0.009034 **
## Age 1 3.1583 543 378.40 0.075543 .
## WRATtotal 1 1.6286 542 376.77 0.201893
## StroopMixed:Sex 1 2.6238 541 374.15 0.105273
## StroopMixed:PovStat 1 0.0702 540 374.08 0.791079
## Sex:PovStat 1 0.7544 539 373.33 0.385085
## StroopMixed:Sex:PovStat 1 1.8601 538 371.47 0.172609
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
SCWTlog4 <- glm(PhysAssault ~ PovStat + Age, data = StroopMixed, family = "binomial")
summary(SCWTlog4)
##
## Call:
## glm(formula = PhysAssault ~ PovStat + Age, family = "binomial",
## data = StroopMixed)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.7410 -0.5281 -0.4482 -0.3757 2.3858
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.75584 0.72639 -1.041 0.2981
## PovStatBelow 0.59571 0.27780 2.144 0.0320 *
## Age -0.03296 0.01544 -2.134 0.0328 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 391.02 on 547 degrees of freedom
## Residual deviance: 379.98 on 545 degrees of freedom
## AIC: 385.98
##
## Number of Fisher Scoring iterations: 5
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))
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
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 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))
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
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 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))
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))
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 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
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
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
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))
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
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 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))
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
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
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)
