Digit Span Forward - Psychological Aggression
Model 1
DSFlog1 <- glm(PsychAggress ~ DigitSpanFwd + WRATtotal, data=Allvars,family = "binomial")
summary(DSFlog1)
##
## Call:
## glm(formula = PsychAggress ~ DigitSpanFwd + WRATtotal, family = "binomial",
## data = Allvars)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.2313 0.4682 0.5210 0.5643 0.7291
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.78702 0.64531 1.220 0.223
## DigitSpanFwd 0.07846 0.05929 1.323 0.186
## WRATtotal 0.01168 0.01645 0.710 0.478
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 505.40 on 640 degrees of freedom
## Residual deviance: 501.43 on 638 degrees of freedom
## AIC: 507.43
##
## Number of Fisher Scoring iterations: 4
confint(DSFlog1)
## 2.5 % 97.5 %
## (Intercept) -0.44898892 2.08935880
## DigitSpanFwd -0.03553314 0.19736569
## WRATtotal -0.02121329 0.04346352
exp(cbind(OR = coef(DSFlog1), confint(DSFlog1)))
## OR 2.5 % 97.5 %
## (Intercept) 2.196839 0.6382732 8.079733
## DigitSpanFwd 1.081621 0.9650907 1.218189
## WRATtotal 1.011748 0.9790101 1.044422
#Wald chi-square Test
Anova(DSFlog1, type="II", test="Wald")
## Analysis of Deviance Table (Type II tests)
##
## Response: PsychAggress
## Df Chisq Pr(>Chisq)
## DigitSpanFwd 1 1.7511 0.1857
## WRATtotal 1 0.5038 0.4778
########Compare to null model
#Difference in Deviance
with(DSFlog1,null.deviance - deviance)
## [1] 3.968932
#Degrees of freedom for the difference between two models
with(DSFlog1,df.null - df.residual)
## [1] 2
#p-value
with(DSFlog1,pchisq(null.deviance-deviance,df.null-df.residual,lower.tail=FALSE))
## [1] 0.137454
#Pseudo R-Squared
nagelkerke(DSFlog1)
## $Models
##
## Model: "glm, PsychAggress ~ DigitSpanFwd + WRATtotal, binomial, Allvars"
## Null: "glm, PsychAggress ~ 1, binomial, Allvars"
##
## $Pseudo.R.squared.for.model.vs.null
## Pseudo.R.squared
## McFadden 0.00785303
## Cox and Snell (ML) 0.00617265
## Nagelkerke (Cragg and Uhler) 0.01131650
##
## $Likelihood.ratio.test
## Df.diff LogLik.diff Chisq p.value
## -2 -1.9845 3.9689 0.13745
##
## $Number.of.observations
##
## Model: 641
## Null: 641
##
## $Messages
## [1] "Note: For models fit with REML, these statistics are based on refitting with ML"
##
## $Warnings
## [1] "None"
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
#Wald chi-square Test
Anova(DSFlog3, type="II", test="Wald")
## Analysis of Deviance Table (Type II tests)
##
## Response: PsychAggress
## Df Chisq Pr(>Chisq)
## DigitSpanFwd 1 1.7222 0.1894
## Sex 1 3.4831 0.0620 .
## PovStat 1 1.3259 0.2495
## Age 1 2.3326 0.1267
## WRATtotal 1 0.7267 0.3939
## DigitSpanFwd:Sex 1 0.3398 0.5600
## DigitSpanFwd:PovStat 1 0.0001 0.9917
## Sex:PovStat 1 0.5522 0.4574
## DigitSpanFwd:Sex:PovStat 1 0.0118 0.9134
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
########Compare to null model
#Difference in Deviance
with(DSFlog3,null.deviance - deviance)
## [1] 13.52276
#Degrees of freedom for the difference between two models
with(DSFlog3,df.null - df.residual)
## [1] 9
#p-value
with(DSFlog3,pchisq(null.deviance-deviance,df.null-df.residual,lower.tail=FALSE))
## [1] 0.1403431
#Pseudo R-Squared
nagelkerke(DSFlog3)
## $Models
##
## Model: "glm, PsychAggress ~ (DigitSpanFwd + Sex + PovStat)^3 + Age + WRATtotal, binomial, Allvars"
## Null: "glm, PsychAggress ~ 1, binomial, Allvars"
##
## $Pseudo.R.squared.for.model.vs.null
## Pseudo.R.squared
## McFadden 0.0267565
## Cox and Snell (ML) 0.0208754
## Nagelkerke (Cragg and Uhler) 0.0382715
##
## $Likelihood.ratio.test
## Df.diff LogLik.diff Chisq p.value
## -9 -6.7614 13.523 0.14034
##
## $Number.of.observations
##
## Model: 641
## Null: 641
##
## $Messages
## [1] "Note: For models fit with REML, these statistics are based on refitting with ML"
##
## $Warnings
## [1] "None"
Compare Models 1 & 3
anova(DSFlog1,DSFlog3,test = "LR")
## Analysis of Deviance Table
##
## Model 1: PsychAggress ~ DigitSpanFwd + WRATtotal
## Model 2: PsychAggress ~ (DigitSpanFwd + Sex + PovStat)^3 + Age + WRATtotal
## Resid. Df Resid. Dev Df Deviance Pr(>Chi)
## 1 638 501.43
## 2 631 491.88 7 9.5538 0.2153
Digit Span Forward - Physical Assault
Model 1
DSFlog1 <- glm(PhysAssault ~ DigitSpanFwd + WRATtotal, data=Allvars,family = "binomial")
summary(DSFlog1)
##
## Call:
## glm(formula = PhysAssault ~ DigitSpanFwd + WRATtotal, family = "binomial",
## data = Allvars)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.5618 -0.5322 -0.5148 -0.4907 2.1702
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -2.532311 0.732701 -3.456 0.000548 ***
## DigitSpanFwd 0.001784 0.058017 0.031 0.975469
## WRATtotal 0.013030 0.018186 0.716 0.473706
## ---
## 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: 481.85 on 638 degrees of freedom
## AIC: 487.85
##
## Number of Fisher Scoring iterations: 4
confint(DSFlog1)
## 2.5 % 97.5 %
## (Intercept) -4.03059046 -1.15077404
## DigitSpanFwd -0.11393207 0.11399047
## WRATtotal -0.02173272 0.04970933
exp(cbind(OR = coef(DSFlog1), confint(DSFlog1)))
## OR 2.5 % 97.5 %
## (Intercept) 0.07947516 0.01776384 0.3163918
## DigitSpanFwd 1.00178564 0.89231857 1.1207414
## WRATtotal 1.01311505 0.97850173 1.0509656
#Wald chi-square Test
Anova(DSFlog1, type="II", test="Wald")
## Analysis of Deviance Table (Type II tests)
##
## Response: PhysAssault
## Df Chisq Pr(>Chisq)
## DigitSpanFwd 1 0.0009 0.9755
## WRATtotal 1 0.5133 0.4737
########Compare to null model
#Difference in Deviance
with(DSFlog1,null.deviance - deviance)
## [1] 0.6824734
#Degrees of freedom for the difference between two models
with(DSFlog1,df.null - df.residual)
## [1] 2
#p-value
with(DSFlog1,pchisq(null.deviance-deviance,df.null-df.residual,lower.tail=FALSE))
## [1] 0.7108906
#Pseudo R-Squared
nagelkerke(DSFlog1)
## $Models
##
## Model: "glm, PhysAssault ~ DigitSpanFwd + WRATtotal, binomial, Allvars"
## Null: "glm, PhysAssault ~ 1, binomial, Allvars"
##
## $Pseudo.R.squared.for.model.vs.null
## Pseudo.R.squared
## McFadden 0.00141436
## Cox and Snell (ML) 0.00106413
## Nagelkerke (Cragg and Uhler) 0.00201180
##
## $Likelihood.ratio.test
## Df.diff LogLik.diff Chisq p.value
## -2 -0.34124 0.68247 0.71089
##
## $Number.of.observations
##
## Model: 641
## Null: 641
##
## $Messages
## [1] "Note: For models fit with REML, these statistics are based on refitting with ML"
##
## $Warnings
## [1] "None"
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
#Wald chi-square Test
Anova(DSFlog3, type="II", test="Wald")
## Analysis of Deviance Table (Type II tests)
##
## Response: PhysAssault
## Df Chisq Pr(>Chisq)
## DigitSpanFwd 1 0.0039 0.95044
## Sex 1 0.4005 0.52682
## PovStat 1 3.6971 0.05451 .
## Age 1 5.8628 0.01546 *
## WRATtotal 1 0.9466 0.33058
## DigitSpanFwd:Sex 1 0.0173 0.89542
## DigitSpanFwd:PovStat 1 0.0058 0.93944
## Sex:PovStat 1 0.0281 0.86690
## DigitSpanFwd:Sex:PovStat 1 0.0800 0.77734
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
########Compare to null model
#Difference in Deviance
with(DSFlog3,null.deviance - deviance)
## [1] 13.23718
#Degrees of freedom for the difference between two models
with(DSFlog3,df.null - df.residual)
## [1] 9
#p-value
with(DSFlog3,pchisq(null.deviance-deviance,df.null-df.residual,lower.tail=FALSE))
## [1] 0.1521644
#Pseudo R-Squared
nagelkerke(DSFlog3)
## $Models
##
## Model: "glm, PhysAssault ~ (DigitSpanFwd + Sex + PovStat)^3 + Age + WRATtotal, binomial, Allvars"
## Null: "glm, PhysAssault ~ 1, binomial, Allvars"
##
## $Pseudo.R.squared.for.model.vs.null
## Pseudo.R.squared
## McFadden 0.0274327
## Cox and Snell (ML) 0.0204391
## Nagelkerke (Cragg and Uhler) 0.0386412
##
## $Likelihood.ratio.test
## Df.diff LogLik.diff Chisq p.value
## -9 -6.6186 13.237 0.15216
##
## $Number.of.observations
##
## Model: 641
## Null: 641
##
## $Messages
## [1] "Note: For models fit with REML, these statistics are based on refitting with ML"
##
## $Warnings
## [1] "None"
Compare Models 1 & 3
anova(DSFlog1,DSFlog3,test = "LR")
## Analysis of Deviance Table
##
## Model 1: PhysAssault ~ DigitSpanFwd + WRATtotal
## Model 2: PhysAssault ~ (DigitSpanFwd + Sex + PovStat)^3 + Age + WRATtotal
## Resid. Df Resid. Dev Df Deviance Pr(>Chi)
## 1 638 481.85
## 2 631 469.30 7 12.555 0.08373 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Digit Span Backward - Psychological Aggression
Model 1
DSBlog1 <- glm(PsychAggress ~ DigitSpanBck + WRATtotal, data=Allvars,family = "binomial")
summary(DSBlog1)
##
## Call:
## glm(formula = PsychAggress ~ DigitSpanBck + WRATtotal, family = "binomial",
## data = Allvars)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.1328 0.4853 0.5220 0.5539 0.7214
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.93837 0.63316 1.482 0.138
## DigitSpanBck 0.03600 0.06181 0.582 0.560
## WRATtotal 0.01668 0.01691 0.987 0.324
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 505.40 on 640 degrees of freedom
## Residual deviance: 502.89 on 638 degrees of freedom
## AIC: 508.89
##
## Number of Fisher Scoring iterations: 4
confint(DSBlog1)
## 2.5 % 97.5 %
## (Intercept) -0.26947081 2.22135098
## DigitSpanBck -0.08306945 0.15965500
## WRATtotal -0.01699755 0.04947187
exp(cbind(OR = coef(DSBlog1), confint(DSBlog1)))
## OR 2.5 % 97.5 %
## (Intercept) 2.555822 0.7637836 9.219778
## DigitSpanBck 1.036652 0.9202872 1.173106
## WRATtotal 1.016825 0.9831461 1.050716
#Wald chi-square Test
Anova(DSBlog1, type="II", test="Wald")
## Analysis of Deviance Table (Type II tests)
##
## Response: PsychAggress
## Df Chisq Pr(>Chisq)
## DigitSpanBck 1 0.3391 0.5603
## WRATtotal 1 0.9735 0.3238
########Compare to null model
#Difference in Deviance
with(DSBlog1,null.deviance - deviance)
## [1] 2.512546
#Degrees of freedom for the difference between two models
with(DSBlog1,df.null - df.residual)
## [1] 2
#p-value
with(DSBlog1,pchisq(null.deviance-deviance,df.null-df.residual,lower.tail=FALSE))
## [1] 0.2847132
#Pseudo R-Squared
nagelkerke(DSBlog1)
## $Models
##
## Model: "glm, PsychAggress ~ DigitSpanBck + WRATtotal, binomial, Allvars"
## Null: "glm, PsychAggress ~ 1, binomial, Allvars"
##
## $Pseudo.R.squared.for.model.vs.null
## Pseudo.R.squared
## McFadden 0.00497139
## Cox and Snell (ML) 0.00391206
## Nagelkerke (Cragg and Uhler) 0.00717210
##
## $Likelihood.ratio.test
## Df.diff LogLik.diff Chisq p.value
## -2 -1.2563 2.5125 0.28471
##
## $Number.of.observations
##
## Model: 641
## Null: 641
##
## $Messages
## [1] "Note: For models fit with REML, these statistics are based on refitting with ML"
##
## $Warnings
## [1] "None"
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
Anova(DSBlog3, type="II", test="Wald")
## Analysis of Deviance Table (Type II tests)
##
## Response: PsychAggress
## Df Chisq Pr(>Chisq)
## DigitSpanBck 1 0.2200 0.63901
## Sex 1 3.6005 0.05776 .
## PovStat 1 0.8586 0.35413
## Age 1 2.9847 0.08406 .
## WRATtotal 1 1.0726 0.30035
## DigitSpanBck:Sex 1 0.0404 0.84061
## DigitSpanBck:PovStat 1 4.3546 0.03691 *
## Sex:PovStat 1 0.4509 0.50189
## DigitSpanBck:Sex:PovStat 1 0.0155 0.90102
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
########Compare to null model
#Difference in Deviance
with(DSBlog3,null.deviance - deviance)
## [1] 16.11477
#Degrees of freedom for the difference between two models
with(DSBlog3,df.null - df.residual)
## [1] 9
#p-value
with(DSBlog3,pchisq(null.deviance-deviance,df.null-df.residual,lower.tail=FALSE))
## [1] 0.06452314
#Pseudo R-Squared
nagelkerke(DSBlog3)
## $Models
##
## Model: "glm, PsychAggress ~ (DigitSpanBck + Sex + PovStat)^3 + Age + WRATtotal, binomial, Allvars"
## Null: "glm, PsychAggress ~ 1, binomial, Allvars"
##
## $Pseudo.R.squared.for.model.vs.null
## Pseudo.R.squared
## McFadden 0.0318851
## Cox and Snell (ML) 0.0248267
## Nagelkerke (Cragg and Uhler) 0.0455156
##
## $Likelihood.ratio.test
## Df.diff LogLik.diff Chisq p.value
## -9 -8.0574 16.115 0.064523
##
## $Number.of.observations
##
## Model: 641
## Null: 641
##
## $Messages
## [1] "Note: For models fit with REML, these statistics are based on refitting with ML"
##
## $Warnings
## [1] "None"
Compare Models 1 & 3
anova(DSBlog1,DSBlog3,test = "LR")
## Analysis of Deviance Table
##
## Model 1: PsychAggress ~ DigitSpanBck + WRATtotal
## Model 2: PsychAggress ~ (DigitSpanBck + Sex + PovStat)^3 + Age + WRATtotal
## Resid. Df Resid. Dev Df Deviance Pr(>Chi)
## 1 638 502.89
## 2 631 489.29 7 13.602 0.05873 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Digit Span Backward - Physical Assault
Model 1
DSBlog1 <- glm(PhysAssault ~ DigitSpanBck + WRATtotal, data=Allvars,family = "binomial")
summary(DSBlog1)
##
## Call:
## glm(formula = PhysAssault ~ DigitSpanBck + WRATtotal, family = "binomial",
## data = Allvars)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.6479 -0.5320 -0.5020 -0.4776 2.2345
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -2.480502 0.722952 -3.431 0.000601 ***
## DigitSpanBck 0.064338 0.059616 1.079 0.280490
## WRATtotal 0.003327 0.018664 0.178 0.858527
## ---
## 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: 480.70 on 638 degrees of freedom
## AIC: 486.7
##
## Number of Fisher Scoring iterations: 4
confint(DSBlog1)
## 2.5 % 97.5 %
## (Intercept) -3.96354620 -1.12189948
## DigitSpanBck -0.05415689 0.18008573
## WRATtotal -0.03250252 0.04082243
exp(cbind(OR = coef(DSBlog1), confint(DSBlog1)))
## OR 2.5 % 97.5 %
## (Intercept) 0.08370116 0.01899563 0.3256606
## DigitSpanBck 1.06645295 0.94728347 1.1973200
## WRATtotal 1.00333240 0.96802001 1.0416671
Anova(DSBlog1, type="II", test="Wald")
## Analysis of Deviance Table (Type II tests)
##
## Response: PhysAssault
## Df Chisq Pr(>Chisq)
## DigitSpanBck 1 1.1647 0.2805
## WRATtotal 1 0.0318 0.8585
########Compare to null model
#Difference in Deviance
with(DSBlog1,null.deviance - deviance)
## [1] 1.829701
#Degrees of freedom for the difference between two models
with(DSBlog1,df.null - df.residual)
## [1] 2
#p-value
with(DSBlog1,pchisq(null.deviance-deviance,df.null-df.residual,lower.tail=FALSE))
## [1] 0.4005766
#Pseudo R-Squared
nagelkerke(DSBlog1)
## $Models
##
## Model: "glm, PhysAssault ~ DigitSpanBck + WRATtotal, binomial, Allvars"
## Null: "glm, PhysAssault ~ 1, binomial, Allvars"
##
## $Pseudo.R.squared.for.model.vs.null
## Pseudo.R.squared
## McFadden 0.00379187
## Cox and Snell (ML) 0.00285038
## Nagelkerke (Cragg and Uhler) 0.00538879
##
## $Likelihood.ratio.test
## Df.diff LogLik.diff Chisq p.value
## -2 -0.91485 1.8297 0.40058
##
## $Number.of.observations
##
## Model: 641
## Null: 641
##
## $Messages
## [1] "Note: For models fit with REML, these statistics are based on refitting with ML"
##
## $Warnings
## [1] "None"
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
Anova(DSBlog3, type="II", test="Wald")
## Analysis of Deviance Table (Type II tests)
##
## Response: PhysAssault
## Df Chisq Pr(>Chisq)
## DigitSpanBck 1 0.9600 0.32719
## Sex 1 0.4929 0.48263
## PovStat 1 3.9229 0.04763 *
## Age 1 5.3479 0.02075 *
## WRATtotal 1 0.2270 0.63373
## DigitSpanBck:Sex 1 0.0305 0.86132
## DigitSpanBck:PovStat 1 0.0363 0.84884
## Sex:PovStat 1 0.0412 0.83915
## DigitSpanBck:Sex:PovStat 1 0.0631 0.80162
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
########Compare to null model
#Difference in Deviance
with(DSBlog3,null.deviance - deviance)
## [1] 14.20855
#Degrees of freedom for the difference between two models
with(DSBlog3,df.null - df.residual)
## [1] 9
#p-value
with(DSBlog3,pchisq(null.deviance-deviance,df.null-df.residual,lower.tail=FALSE))
## [1] 0.1150977
#Pseudo R-Squared
nagelkerke(DSBlog3)
## $Models
##
## Model: "glm, PhysAssault ~ (DigitSpanBck + Sex + PovStat)^3 + Age + WRATtotal, binomial, Allvars"
## Null: "glm, PhysAssault ~ 1, binomial, Allvars"
##
## $Pseudo.R.squared.for.model.vs.null
## Pseudo.R.squared
## McFadden 0.0294458
## Cox and Snell (ML) 0.0219224
## Nagelkerke (Cragg and Uhler) 0.0414454
##
## $Likelihood.ratio.test
## Df.diff LogLik.diff Chisq p.value
## -9 -7.1043 14.209 0.1151
##
## $Number.of.observations
##
## Model: 641
## Null: 641
##
## $Messages
## [1] "Note: For models fit with REML, these statistics are based on refitting with ML"
##
## $Warnings
## [1] "None"
Compare Models 1 & 3
anova(DSBlog1,DSBlog3,test = "LR")
## Analysis of Deviance Table
##
## Model 1: PhysAssault ~ DigitSpanBck + WRATtotal
## Model 2: PhysAssault ~ (DigitSpanBck + Sex + PovStat)^3 + Age + WRATtotal
## Resid. Df Resid. Dev Df Deviance Pr(>Chi)
## 1 638 480.70
## 2 631 468.32 7 12.379 0.08877 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1