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
########Compare to null model
#Difference in Deviance
with(ANlog1,null.deviance - deviance)
## [1] 1.448277
#Degrees of freedom for the difference between two models
with(ANlog1,df.null - df.residual)
## [1] 1
#p-value
with(ANlog1,pchisq(null.deviance-deviance,df.null-df.residual,lower.tail=FALSE))
## [1] 0.2288045
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
########Compare to null model
#Difference in Deviance
with(ANlog2,null.deviance - deviance)
## [1] 11.77697
#Degrees of freedom for the difference between two models
with(ANlog2,df.null - df.residual)
## [1] 5
#p-value
with(ANlog2,pchisq(null.deviance-deviance,df.null-df.residual,lower.tail=FALSE))
## [1] 0.03797502
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 to null model
#Difference in Deviance
with(ANlog3,null.deviance - deviance)
## [1] 14.78833
#Degrees of freedom for the difference between two models
with(ANlog3,df.null - df.residual)
## [1] 9
#p-value
with(ANlog3,pchisq(null.deviance-deviance,df.null-df.residual,lower.tail=FALSE))
## [1] 0.09691678
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
########Compare to null model
#Difference in Deviance
with(ANlog1,null.deviance - deviance)
## [1] 0.2952494
#Degrees of freedom for the difference between two models
with(ANlog1,df.null - df.residual)
## [1] 1
#p-value
with(ANlog1,pchisq(null.deviance-deviance,df.null-df.residual,lower.tail=FALSE))
## [1] 0.586876
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
########Compare to null model
#Difference in Deviance
with(ANlog2,null.deviance - deviance)
## [1] 14.13855
#Degrees of freedom for the difference between two models
with(ANlog2,df.null - df.residual)
## [1] 5
#p-value
with(ANlog2,pchisq(null.deviance-deviance,df.null-df.residual,lower.tail=FALSE))
## [1] 0.01475288
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 to null model
#Difference in Deviance
with(ANlog3,null.deviance - deviance)
## [1] 15.56602
#Degrees of freedom for the difference between two models
with(ANlog3,df.null - df.residual)
## [1] 9
#p-value
with(ANlog3,pchisq(null.deviance-deviance,df.null-df.residual,lower.tail=FALSE))
## [1] 0.07651624
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