TMT_2 Models
Dissertation Analyses
load(file="/Users/meganwilliams/Desktop/Dissertation/StroopMixed.rdata")
load(file="/Users/meganwilliams/Desktop/Dissertation/Allvars.rdata")
library(effects)
library(interactions)
library(rcompanion)
library(car)Trail Making Test Part A - Psychological Aggression
Model 1
options(scipen = 999)
TMTAlog1 <- glm(PsychAggress ~ TrailsA + WRATtotal, data=Allvars,family = "binomial")
summary(TMTAlog1)##
## Call:
## glm(formula = PsychAggress ~ TrailsA + WRATtotal, family = "binomial",
## data = Allvars)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.1303 0.4823 0.5085 0.5449 1.0981
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 1.335665 0.655826 2.037 0.0417 *
## TrailsA -0.006319 0.002737 -2.309 0.0209 *
## WRATtotal 0.017840 0.014644 1.218 0.2231
## ---
## 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: 498.57 on 638 degrees of freedom
## AIC: 504.57
##
## Number of Fisher Scoring iterations: 4confint(TMTAlog1)## 2.5 % 97.5 %
## (Intercept) 0.08059980 2.6610057816
## TrailsA -0.01174768 -0.0006371154
## WRATtotal -0.01143901 0.0461535852exp(cbind(OR = coef(TMTAlog1), confint(TMTAlog1)))## OR 2.5 % 97.5 %
## (Intercept) 3.8025243 1.0839370 14.3106753
## TrailsA 0.9937012 0.9883211 0.9993631
## WRATtotal 1.0179999 0.9886262 1.0472352plot(predictorEffect("TrailsA",TMTAlog1))
#Wald chi-square Test
Anova(TMTAlog1, type="II", test="Wald")## Analysis of Deviance Table (Type II tests)
##
## Response: PsychAggress
## Df Chisq Pr(>Chisq)
## TrailsA 1 5.3315 0.02094 *
## WRATtotal 1 1.4840 0.22315
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1########Compare to null model
#Difference in Deviance
with(TMTAlog1,null.deviance - deviance)## [1] 6.831695#Degrees of freedom for the difference between two models
with(TMTAlog1,df.null - df.residual)## [1] 2#p-value
with(TMTAlog1,pchisq(null.deviance-deviance,df.null-df.residual,lower.tail=FALSE))## [1] 0.03284855#Pseudo R-Squared
nagelkerke(TMTAlog1)## $Models
##
## Model: "glm, PsychAggress ~ TrailsA + WRATtotal, binomial, Allvars"
## Null: "glm, PsychAggress ~ 1, binomial, Allvars"
##
## $Pseudo.R.squared.for.model.vs.null
## Pseudo.R.squared
## McFadden 0.0135174
## Cox and Snell (ML) 0.0106013
## Nagelkerke (Cragg and Uhler) 0.0194357
##
## $Likelihood.ratio.test
## Df.diff LogLik.diff Chisq p.value
## -2 -3.4158 6.8317 0.032849
##
## $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
options(scipen = 999)
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: 5confint(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 0.025273803exp(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 1.025596Anova(TMTAlog3, type="II", test="Wald")## Analysis of Deviance Table (Type II tests)
##
## Response: PsychAggress
## Df Chisq Pr(>Chisq)
## TrailsA 1 3.4814 0.06206 .
## Sex 1 2.6939 0.10073
## PovStat 1 1.0180 0.31299
## Age 1 2.5106 0.11309
## WRATtotal 1 2.0820 0.14904
## TrailsA:Sex 1 0.0137 0.90666
## TrailsA:PovStat 1 0.5539 0.45673
## Sex:PovStat 1 0.8076 0.36884
## TrailsA:Sex:PovStat 1 0.5798 0.44637
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1########Compare to null model
#Difference in Deviance
with(TMTAlog3,null.deviance - deviance)## [1] 16.21973#Degrees of freedom for the difference between two models
with(TMTAlog3,df.null - df.residual)## [1] 9#p-value
with(TMTAlog3,pchisq(null.deviance-deviance,df.null-df.residual,lower.tail=FALSE))## [1] 0.06243246#Pseudo R-Squared
nagelkerke(TMTAlog3)## $Models
##
## Model: "glm, PsychAggress ~ (TrailsA + Sex + PovStat)^3 + Age + WRATtotal, binomial, Allvars"
## Null: "glm, PsychAggress ~ 1, binomial, Allvars"
##
## $Pseudo.R.squared.for.model.vs.null
## Pseudo.R.squared
## McFadden 0.0320928
## Cox and Snell (ML) 0.0249863
## Nagelkerke (Cragg and Uhler) 0.0458083
##
## $Likelihood.ratio.test
## Df.diff LogLik.diff Chisq p.value
## -9 -8.1099 16.22 0.062432
##
## $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(TMTAlog1,TMTAlog3,test = "LR")## Analysis of Deviance Table
##
## Model 1: PsychAggress ~ TrailsA + WRATtotal
## Model 2: PsychAggress ~ (TrailsA + Sex + PovStat)^3 + Age + WRATtotal
## Resid. Df Resid. Dev Df Deviance Pr(>Chi)
## 1 638 498.57
## 2 631 489.18 7 9.388 0.226Trail Making Test Part A - Physical Assault
Model 1
options(scipen = 999)
TMTAlog1 <- glm(PhysAssault ~ TrailsA + WRATtotal, data=Allvars,family = "binomial")
summary(TMTAlog1)##
## Call:
## glm(formula = PhysAssault ~ TrailsA + WRATtotal, family = "binomial",
## data = Allvars)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.5836 -0.5414 -0.5166 -0.4765 2.2735
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.982492 0.861673 -2.301 0.0214 *
## TrailsA -0.009439 0.008155 -1.157 0.2471
## WRATtotal 0.007887 0.017005 0.464 0.6428
## ---
## 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: 479.65 on 638 degrees of freedom
## AIC: 485.65
##
## Number of Fisher Scoring iterations: 6exp(cbind(OR = coef(TMTAlog1), confint(TMTAlog1)))## OR 2.5 % 97.5 %
## (Intercept) 0.1377256 0.02554227 0.7641915
## TrailsA 0.9906055 0.97159815 1.0021088
## WRATtotal 1.0079181 0.97537269 1.0428449Anova(TMTAlog1, type="II", test="Wald")## Analysis of Deviance Table (Type II tests)
##
## Response: PhysAssault
## Df Chisq Pr(>Chisq)
## TrailsA 1 1.3396 0.2471
## WRATtotal 1 0.2151 0.6428########Compare to null model
#Difference in Deviance
with(TMTAlog1,null.deviance - deviance)## [1] 2.881826#Degrees of freedom for the difference between two models
with(TMTAlog1,df.null - df.residual)## [1] 2#p-value
with(TMTAlog1,pchisq(null.deviance-deviance,df.null-df.residual,lower.tail=FALSE))## [1] 0.2367116#Pseudo R-Squared
nagelkerke(TMTAlog1)## $Models
##
## Model: "glm, PhysAssault ~ TrailsA + WRATtotal, binomial, Allvars"
## Null: "glm, PhysAssault ~ 1, binomial, Allvars"
##
## $Pseudo.R.squared.for.model.vs.null
## Pseudo.R.squared
## McFadden 0.00597229
## Cox and Snell (ML) 0.00448574
## Nagelkerke (Cragg and Uhler) 0.00848053
##
## $Likelihood.ratio.test
## Df.diff LogLik.diff Chisq p.value
## -2 -1.4409 2.8818 0.23671
##
## $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
options(scipen = 999)
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) -1.36348112 1.05751855 -1.289 0.1973
## TrailsA 0.00007282 0.00727697 0.010 0.9920
## SexMen 0.07629996 0.57331921 0.133 0.8941
## PovStatBelow 1.66166783 0.89445973 1.858 0.0632 .
## Age -0.02951309 0.01411907 -2.090 0.0366 *
## WRATtotal 0.01521473 0.01744790 0.872 0.3832
## TrailsA:SexMen -0.00802885 0.01525606 -0.526 0.5987
## TrailsA:PovStatBelow -0.04104160 0.02947504 -1.392 0.1638
## SexMen:PovStatBelow -1.19151471 1.15924755 -1.028 0.3040
## TrailsA:SexMen:PovStatBelow 0.04371608 0.03509486 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: 6confint(TMTAlog3)## Waiting for profiling to be done...## 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 0.119436934exp(cbind(OR = coef(TMTAlog3), confint(TMTAlog3)))## Waiting for profiling to be done...## 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 1.1268622Anova(TMTAlog3, type="II", test="Wald")## Analysis of Deviance Table (Type II tests)
##
## Response: PhysAssault
## Df Chisq Pr(>Chisq)
## TrailsA 1 0.4071 0.52342
## Sex 1 0.2931 0.58823
## PovStat 1 4.2643 0.03892 *
## Age 1 4.3694 0.03659 *
## WRATtotal 1 0.7604 0.38320
## TrailsA:Sex 1 0.0004 0.98470
## TrailsA:PovStat 1 0.4051 0.52446
## Sex:PovStat 1 0.0421 0.83736
## TrailsA:Sex:PovStat 1 1.5517 0.21289
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1########Compare to null model
#Difference in Deviance
with(TMTAlog3,null.deviance - deviance)## [1] 16.52871#Degrees of freedom for the difference between two models
with(TMTAlog3,df.null - df.residual)## [1] 9#p-value
with(TMTAlog3,pchisq(null.deviance-deviance,df.null-df.residual,lower.tail=FALSE))## [1] 0.05662862#Pseudo R-Squared
nagelkerke(TMTAlog3)## $Models
##
## Model: "glm, PhysAssault ~ (TrailsA + Sex + PovStat)^3 + Age + WRATtotal, binomial, Allvars"
## Null: "glm, PhysAssault ~ 1, binomial, Allvars"
##
## $Pseudo.R.squared.for.model.vs.null
## Pseudo.R.squared
## McFadden 0.0342541
## Cox and Snell (ML) 0.0254562
## Nagelkerke (Cragg and Uhler) 0.0481263
##
## $Likelihood.ratio.test
## Df.diff LogLik.diff Chisq p.value
## -9 -8.2644 16.529 0.056629
##
## $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(TMTAlog1,TMTAlog3,test = "LR")## Analysis of Deviance Table
##
## Model 1: PhysAssault ~ TrailsA + WRATtotal
## Model 2: PhysAssault ~ (TrailsA + Sex + PovStat)^3 + Age + WRATtotal
## Resid. Df Resid. Dev Df Deviance Pr(>Chi)
## 1 638 479.65
## 2 631 466.00 7 13.647 0.05783 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Trail Making Test Part B - Psychological Aggression
Model 1
options(scipen = 999)
TMTBlog1 <- glm(PsychAggress ~ TrailsB + WRATtotal, data=Allvars,family = "binomial")
summary(TMTBlog1)##
## Call:
## glm(formula = PsychAggress ~ TrailsB + WRATtotal, family = "binomial",
## data = Allvars)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.1102 0.4895 0.5148 0.5478 0.7594
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 1.339399 0.816391 1.641 0.101
## TrailsB -0.001334 0.001663 -0.802 0.422
## WRATtotal 0.015670 0.016401 0.955 0.339
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 505.4 on 640 degrees of freedom
## Residual deviance: 502.6 on 638 degrees of freedom
## AIC: 508.6
##
## Number of Fisher Scoring iterations: 4confint(TMTBlog1)## 2.5 % 97.5 %
## (Intercept) -0.240037089 2.968605536
## TrailsB -0.004508184 0.002034085
## WRATtotal -0.016915484 0.047550012exp(cbind(OR = coef(TMTBlog1), confint(TMTBlog1)))## OR 2.5 % 97.5 %
## (Intercept) 3.8167494 0.7865987 19.464758
## TrailsB 0.9986672 0.9955020 1.002036
## WRATtotal 1.0157937 0.9832268 1.048699Anova(TMTBlog1, type="II", test="Wald")## Analysis of Deviance Table (Type II tests)
##
## Response: PsychAggress
## Df Chisq Pr(>Chisq)
## TrailsB 1 0.6435 0.4224
## WRATtotal 1 0.9129 0.3394########Compare to null model
#Difference in Deviance
with(TMTBlog1,null.deviance - deviance)## [1] 2.79723#Degrees of freedom for the difference between two models
with(TMTBlog1,df.null - df.residual)## [1] 2#p-value
with(TMTBlog1,pchisq(null.deviance-deviance,df.null-df.residual,lower.tail=FALSE))## [1] 0.2469387#Pseudo R-Squared
nagelkerke(TMTBlog1)## $Models
##
## Model: "glm, PsychAggress ~ TrailsB + WRATtotal, binomial, Allvars"
## Null: "glm, PsychAggress ~ 1, binomial, Allvars"
##
## $Pseudo.R.squared.for.model.vs.null
## Pseudo.R.squared
## McFadden 0.00553467
## Cox and Snell (ML) 0.00435435
## Nagelkerke (Cragg and Uhler) 0.00798297
##
## $Likelihood.ratio.test
## Df.diff LogLik.diff Chisq p.value
## -2 -1.3986 2.7972 0.24694
##
## $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
options(scipen = 999)
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
## -2.3166 0.4161 0.4923 0.5692 0.9530
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 2.11869216 1.06981158 1.980 0.0477 *
## TrailsB 0.00006311 0.00289151 0.022 0.9826
## SexMen -0.28635201 0.49006156 -0.584 0.5590
## PovStatBelow 0.27642697 0.71307763 0.388 0.6983
## Age -0.02183121 0.01355727 -1.610 0.1073
## WRATtotal 0.02252136 0.01718596 1.310 0.1900
## TrailsB:SexMen -0.00202431 0.00347289 -0.583 0.5600
## TrailsB:PovStatBelow -0.00157107 0.00516673 -0.304 0.7611
## SexMen:PovStatBelow -0.88586893 1.02357774 -0.865 0.3868
## TrailsB:SexMen:PovStatBelow 0.01099893 0.00806233 1.364 0.1725
## ---
## 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: 490.99 on 631 degrees of freedom
## AIC: 510.99
##
## Number of Fisher Scoring iterations: 5confint(TMTBlog3)## 2.5 % 97.5 %
## (Intercept) 0.040995977 4.243386118
## TrailsB -0.005314178 0.006180063
## SexMen -1.257432994 0.671448445
## PovStatBelow -1.102935668 1.720782202
## Age -0.048621607 0.004639968
## WRATtotal -0.011510819 0.056032243
## TrailsB:SexMen -0.009085005 0.004661670
## TrailsB:PovStatBelow -0.011454410 0.009293020
## SexMen:PovStatBelow -2.939374247 1.102186380
## TrailsB:SexMen:PovStatBelow -0.004275290 0.028250524exp(cbind(OR = coef(TMTBlog3), confint(TMTBlog3)))## OR 2.5 % 97.5 %
## (Intercept) 8.3202488 1.04184791 69.643273
## TrailsB 1.0000631 0.99469992 1.006199
## SexMen 0.7509982 0.28438310 1.957070
## PovStatBelow 1.3184107 0.33189532 5.588898
## Age 0.9784054 0.95254150 1.004651
## WRATtotal 1.0227769 0.98855518 1.057632
## TrailsB:SexMen 0.9979777 0.99095614 1.004673
## TrailsB:PovStatBelow 0.9984302 0.98861094 1.009336
## SexMen:PovStatBelow 0.4123557 0.05289882 3.010741
## TrailsB:SexMen:PovStatBelow 1.0110596 0.99573384 1.028653Anova(TMTBlog3, type="II", test="Wald")## Analysis of Deviance Table (Type II tests)
##
## Response: PsychAggress
## Df Chisq Pr(>Chisq)
## TrailsB 1 0.1338 0.71456
## Sex 1 3.3623 0.06671 .
## PovStat 1 0.7220 0.39548
## Age 1 2.5931 0.10733
## WRATtotal 1 1.7173 0.19004
## TrailsB:Sex 1 0.0000 0.99872
## TrailsB:PovStat 1 0.5469 0.45960
## Sex:PovStat 1 0.3112 0.57692
## TrailsB:Sex:PovStat 1 1.8611 0.17249
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1########Compare to null model
#Difference in Deviance
with(TMTBlog3,null.deviance - deviance)## [1] 14.41466#Degrees of freedom for the difference between two models
with(TMTBlog3,df.null - df.residual)## [1] 9#p-value
with(TMTBlog3,pchisq(null.deviance-deviance,df.null-df.residual,lower.tail=FALSE))## [1] 0.1083207#Pseudo R-Squared
nagelkerke(TMTBlog3)## $Models
##
## Model: "glm, PsychAggress ~ (TrailsB + Sex + PovStat)^3 + Age + WRATtotal, binomial, Allvars"
## Null: "glm, PsychAggress ~ 1, binomial, Allvars"
##
## $Pseudo.R.squared.for.model.vs.null
## Pseudo.R.squared
## McFadden 0.0285212
## Cox and Snell (ML) 0.0222368
## Nagelkerke (Cragg and Uhler) 0.0407675
##
## $Likelihood.ratio.test
## Df.diff LogLik.diff Chisq p.value
## -9 -7.2073 14.415 0.10832
##
## $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(TMTBlog1,TMTBlog3,test = "LR")## Analysis of Deviance Table
##
## Model 1: PsychAggress ~ TrailsB + WRATtotal
## Model 2: PsychAggress ~ (TrailsB + Sex + PovStat)^3 + Age + WRATtotal
## Resid. Df Resid. Dev Df Deviance Pr(>Chi)
## 1 638 502.60
## 2 631 490.99 7 11.617 0.1139Trail Making Test Part B - Physical Assault
Model 1
options(scipen = 999)
TMTBlog1 <- glm(PhysAssault ~ TrailsB + WRATtotal, data=Allvars,family = "binomial")
summary(TMTBlog1)##
## Call:
## glm(formula = PhysAssault ~ TrailsB + WRATtotal, family = "binomial",
## data = Allvars)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.5637 -0.5337 -0.5159 -0.4854 2.2079
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -2.2512544 0.9100576 -2.474 0.0134 *
## TrailsB -0.0009631 0.0019131 -0.503 0.6147
## WRATtotal 0.0092431 0.0181183 0.510 0.6099
## ---
## 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: 481.59 on 638 degrees of freedom
## AIC: 487.59
##
## Number of Fisher Scoring iterations: 4confint(TMTBlog1)## 2.5 % 97.5 %
## (Intercept) -4.080575386 -0.50337326
## TrailsB -0.004912096 0.00262842
## WRATtotal -0.025640019 0.04556428exp(cbind(OR = coef(TMTBlog1), confint(TMTBlog1)))## OR 2.5 % 97.5 %
## (Intercept) 0.1052671 0.01689774 0.6044881
## TrailsB 0.9990374 0.99509995 1.0026319
## WRATtotal 1.0092860 0.97468590 1.0466183Anova(TMTBlog1, type="II", test="Wald")## Analysis of Deviance Table (Type II tests)
##
## Response: PhysAssault
## Df Chisq Pr(>Chisq)
## TrailsB 1 0.2534 0.6147
## WRATtotal 1 0.2603 0.6099########Compare to null model
#Difference in Deviance
with(TMTBlog1,null.deviance - deviance)## [1] 0.941138#Degrees of freedom for the difference between two models
with(TMTBlog1,df.null - df.residual)## [1] 2#p-value
with(TMTBlog1,pchisq(null.deviance-deviance,df.null-df.residual,lower.tail=FALSE))## [1] 0.6246467#Pseudo R-Squared
nagelkerke(TMTBlog1)## $Models
##
## Model: "glm, PhysAssault ~ TrailsB + WRATtotal, binomial, Allvars"
## Null: "glm, PhysAssault ~ 1, binomial, Allvars"
##
## $Pseudo.R.squared.for.model.vs.null
## Pseudo.R.squared
## McFadden 0.00195041
## Cox and Snell (ML) 0.00146716
## Nagelkerke (Cragg and Uhler) 0.00277374
##
## $Likelihood.ratio.test
## Df.diff LogLik.diff Chisq p.value
## -2 -0.47057 0.94114 0.62465
##
## $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
options(scipen = 999)
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
## -0.8076 -0.5530 -0.4680 -0.3874 2.3898
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.3070879 1.1474161 -1.139 0.255
## TrailsB -0.0003952 0.0033321 -0.119 0.906
## SexMen -0.4164733 0.5404079 -0.771 0.441
## PovStatBelow 0.3301475 0.5989564 0.551 0.581
## Age -0.0339108 0.0142097 -2.386 0.017 *
## WRATtotal 0.0193090 0.0188812 1.023 0.306
## TrailsB:SexMen 0.0022476 0.0043351 0.518 0.604
## TrailsB:PovStatBelow 0.0012567 0.0049259 0.255 0.799
## SexMen:PovStatBelow 0.5698502 0.9182035 0.621 0.535
## TrailsB:SexMen:PovStatBelow -0.0046844 0.0073815 -0.635 0.526
## ---
## 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: 468.86 on 631 degrees of freedom
## AIC: 488.86
##
## Number of Fisher Scoring iterations: 5confint(TMTBlog3)## 2.5 % 97.5 %
## (Intercept) -3.586519885 0.922804562
## TrailsB -0.007675075 0.005630066
## SexMen -1.488985092 0.639884285
## PovStatBelow -0.853851868 1.506410081
## Age -0.062201434 -0.006373086
## WRATtotal -0.016975165 0.057248566
## TrailsB:SexMen -0.006319224 0.011010130
## TrailsB:PovStatBelow -0.008693041 0.011011262
## SexMen:PovStatBelow -1.229855510 2.384454883
## TrailsB:SexMen:PovStatBelow -0.019760664 0.009613506exp(cbind(OR = coef(TMTBlog3), confint(TMTBlog3)))## OR 2.5 % 97.5 %
## (Intercept) 0.2706069 0.02769454 2.5163377
## TrailsB 0.9996049 0.99235430 1.0056459
## SexMen 0.6593681 0.22560150 1.8962614
## PovStatBelow 1.3911733 0.42577175 4.5105093
## Age 0.9666577 0.93969358 0.9936472
## WRATtotal 1.0194966 0.98316810 1.0589190
## TrailsB:SexMen 1.0022501 0.99370070 1.0110710
## TrailsB:PovStatBelow 1.0012575 0.99134463 1.0110721
## SexMen:PovStatBelow 1.7680022 0.29233481 10.8531448
## TrailsB:SexMen:PovStatBelow 0.9953265 0.98043330 1.0096599Anova(TMTBlog3, type="II", test="Wald")## Analysis of Deviance Table (Type II tests)
##
## Response: PhysAssault
## Df Chisq Pr(>Chisq)
## TrailsB 1 0.0593 0.80762
## Sex 1 0.4079 0.52305
## PovStat 1 3.7284 0.05349 .
## Age 1 5.6952 0.01701 *
## WRATtotal 1 1.0458 0.30647
## TrailsB:Sex 1 0.0323 0.85735
## TrailsB:PovStat 1 0.0489 0.82501
## Sex:PovStat 1 0.0262 0.87131
## TrailsB:Sex:PovStat 1 0.4027 0.52568
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1########Compare to null model
#Difference in Deviance
with(TMTBlog3,null.deviance - deviance)## [1] 13.67086#Degrees of freedom for the difference between two models
with(TMTBlog3,df.null - df.residual)## [1] 9#p-value
with(TMTBlog3,pchisq(null.deviance-deviance,df.null-df.residual,lower.tail=FALSE))## [1] 0.1345244#Pseudo R-Squared
nagelkerke(TMTBlog3)## $Models
##
## Model: "glm, PhysAssault ~ (TrailsB + Sex + PovStat)^3 + Age + WRATtotal, binomial, Allvars"
## Null: "glm, PhysAssault ~ 1, binomial, Allvars"
##
## $Pseudo.R.squared.for.model.vs.null
## Pseudo.R.squared
## McFadden 0.0283315
## Cox and Snell (ML) 0.0211016
## Nagelkerke (Cragg and Uhler) 0.0398937
##
## $Likelihood.ratio.test
## Df.diff LogLik.diff Chisq p.value
## -9 -6.8354 13.671 0.13452
##
## $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(TMTBlog1,TMTBlog3,test = "LR")## Analysis of Deviance Table
##
## Model 1: PhysAssault ~ TrailsB + WRATtotal
## Model 2: PhysAssault ~ (TrailsB + Sex + PovStat)^3 + Age + WRATtotal
## Resid. Df Resid. Dev Df Deviance Pr(>Chi)
## 1 638 481.59
## 2 631 468.86 7 12.73 0.07897 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1