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
## -2.0373652 0.5193847 0.5246390 0.5314622 1.1140222
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 1.996695213 0.131880498 15.14019 < 2e-16
## TrailsA -0.003076260 0.001335536 -2.30339 0.021257
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 505.40142 on 640 degrees of freedom
## Residual deviance: 500.77544 on 639 degrees of freedom
## AIC: 504.77544
##
## Number of Fisher Scoring iterations: 4
confint(TMTAlog1)
## 2.5 % 97.5 %
## (Intercept) 1.743910321967 2.2617752778868
## TrailsA -0.005757579576 -0.0003006257254
exp(cbind(OR = coef(TMTAlog1), confint(TMTAlog1)))
## OR 2.5 % 97.5 %
## (Intercept) 7.3646771511 5.7196654846 9.6001169231
## TrailsA 0.9969284669 0.9942589635 0.9996994195
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
## -2.3447862 0.4176990 0.4970925 0.5688854 1.0547868
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 2.121290670 0.937377654 2.26301 0.023635
## TrailsA -0.002674780 0.001362399 -1.96329 0.049613
## Age -0.020128697 0.013105822 -1.53586 0.124573
## SexMen -0.421401937 0.237184804 -1.77668 0.075621
## PovStatBelow 0.282190784 0.272291838 1.03635 0.300037
## WRATtotal 0.022125675 0.014632810 1.51206 0.130519
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 505.40142 on 640 degrees of freedom
## Residual deviance: 491.14329 on 635 degrees of freedom
## AIC: 503.14329
##
## Number of Fisher Scoring iterations: 5
confint(TMTAlog2)
## 2.5 % 97.5 %
## (Intercept) 0.308141477824 3.9903558959663
## TrailsA -0.005366489292 0.0001985874588
## Age -0.046021888477 0.0054691447531
## SexMen -0.891816154758 0.0406676919104
## PovStatBelow -0.238167337912 0.8337512536937
## WRATtotal -0.007104779862 0.0504394740797
exp(cbind(OR = coef(TMTAlog2), confint(TMTAlog2)))
## OR 2.5 % 97.5 %
## (Intercept) 8.3418971819 1.3608935116 54.074130704
## TrailsA 0.9973287937 0.9946478846 1.000198607
## Age 0.9800725324 0.9550210580 1.005484128
## SexMen 0.6561263269 0.4099106152 1.041505947
## PovStatBelow 1.3260316821 0.7880708059 2.301937717
## WRATtotal 1.0223722635 0.9929203994 1.051733204
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
## -2.3162026 0.4125179 0.4876138 0.5679440 0.9071982
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 2.2237514530 0.9456124818 2.35165 0.018690
## TrailsA -0.0021023141 0.0021776789 -0.96539 0.334348
## SexMen -0.5399009704 0.3159591140 -1.70877 0.087494
## PovStatBelow -0.2511691275 0.9094668724 -0.27617 0.782416
## Age -0.0226030780 0.0133531387 -1.69272 0.090509
## WRATtotal 0.0233698662 0.0147431083 1.58514 0.112935
## TrailsA:SexMen 0.0002271136 0.0030406953 0.07469 0.940460
## TrailsA:PovStatBelow 0.0099771801 0.0260926868 0.38237 0.702184
## SexMen:PovStatBelow 1.0344839352 1.0218745757 1.01234 0.311376
## TrailsA:SexMen:PovStatBelow -0.0163108083 0.0268205899 -0.60815 0.543091
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 505.40142 on 640 degrees of freedom
## Residual deviance: 488.50205 on 631 degrees of freedom
## AIC: 508.50205
##
## Number of Fisher Scoring iterations: 5
confint(TMTAlog3)
## 2.5 % 97.5 %
## (Intercept) 0.395752060828 4.110089059633
## TrailsA -0.006312819470 0.003161140710
## SexMen -1.169725338434 0.073937162644
## PovStatBelow -2.178462364394 1.413792744920
## Age -0.048994349766 0.003467751984
## WRATtotal -0.006044195016 0.051931961784
## TrailsA:SexMen -0.006202763749 0.006523213303
## TrailsA:PovStatBelow -0.032068567851 0.071742155241
## SexMen:PovStatBelow -0.852081276485 3.192161797973
## TrailsA:SexMen:PovStatBelow -0.079483750964 0.027234359286
exp(cbind(OR = coef(TMTAlog3), confint(TMTAlog3)))
## OR 2.5 % 97.5 %
## (Intercept) 9.2419366055 1.4855009580 60.952145704
## TrailsA 0.9978998942 0.9937070645 1.003166142
## SexMen 0.5828059645 0.3104521988 1.076739144
## PovStatBelow 0.7778907977 0.1132154810 4.111519815
## Age 0.9776504578 0.9521865098 1.003473772
## WRATtotal 1.0236450813 0.9939740344 1.053304075
## TrailsA:SexMen 1.0002271393 0.9938164337 1.006544536
## TrailsA:PovStatBelow 1.0100271181 0.9684401759 1.074378286
## SexMen:PovStatBelow 2.8136538332 0.4265262884 24.340990912
## TrailsA:SexMen:PovStatBelow 0.9838214927 0.9235930273 1.027608604
Model 4 (Two-Way Interaction w/Sex instead)
TMTAlog4 <- glm(PsychAggress ~ TrailsA + Age + Sex + PovStat + WRATtotal + TrailsA*Sex, data = Allvars, family = "binomial")
summary(TMTAlog4)
##
## Call:
## glm(formula = PsychAggress ~ TrailsA + Age + Sex + PovStat +
## WRATtotal + TrailsA * Sex, family = "binomial", data = Allvars)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.3484505 0.4182709 0.4971779 0.5668706 1.1726912
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 2.127892078 0.938048959 2.26842 0.023303
## TrailsA -0.001958748 0.002174199 -0.90091 0.367639
## Age -0.020922844 0.013226751 -1.58186 0.113682
## SexMen -0.363313892 0.271595073 -1.33770 0.180993
## PovStatBelow 0.285564577 0.272492837 1.04797 0.294652
## WRATtotal 0.022078814 0.014639264 1.50819 0.131506
## TrailsA:SexMen -0.001229688 0.002825404 -0.43523 0.663399
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 505.40142 on 640 degrees of freedom
## Residual deviance: 490.95025 on 634 degrees of freedom
## AIC: 504.95025
##
## Number of Fisher Scoring iterations: 4
vif(TMTAlog4)
## TrailsA Age Sex PovStat WRATtotal TrailsA:Sex
## 2.503776008 1.053107983 1.316065404 1.050026240 1.025072176 2.833546976
Model 4 (Two-Way Interaction w/PovStat instead)
TMTAlog4 <- glm(PsychAggress ~ TrailsA + Age + Sex + PovStat + WRATtotal + TrailsA*PovStat, data = Allvars, family = "binomial")
summary(TMTAlog4)
##
## Call:
## glm(formula = PsychAggress ~ TrailsA + Age + Sex + PovStat +
## WRATtotal + TrailsA * PovStat, family = "binomial", data = Allvars)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.3591855 0.4121920 0.4959751 0.5686124 0.9096723
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 2.141296082 0.938394740 2.28187 0.022497
## TrailsA -0.001995172 0.001519228 -1.31328 0.189089
## Age -0.021535139 0.013192540 -1.63237 0.102601
## SexMen -0.400504718 0.237912385 -1.68341 0.092295
## PovStatBelow 0.490855723 0.338614071 1.44960 0.147169
## WRATtotal 0.022219137 0.014639709 1.51773 0.129082
## TrailsA:PovStatBelow -0.004941184 0.004905011 -1.00737 0.313755
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 505.40142 on 640 degrees of freedom
## Residual deviance: 489.74861 on 634 degrees of freedom
## AIC: 503.74861
##
## Number of Fisher Scoring iterations: 5
vif(TMTAlog4)
## TrailsA Age Sex PovStat WRATtotal TrailsA:PovStat
## 1.121180185 1.037126222 1.007260768 1.592627717 1.026903352 1.650915199
Model 4 (Two-Way Interaction w/Age instead)
TMTAlog4 <- glm(PsychAggress ~ TrailsA + Age + Sex + PovStat + WRATtotal + TrailsA*Age, data = Allvars, family = "binomial")
summary(TMTAlog4)
##
## Call:
## glm(formula = PsychAggress ~ TrailsA + Age + Sex + PovStat +
## WRATtotal + TrailsA * Age, family = "binomial", data = Allvars)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.3447948 0.4176953 0.4970797 0.5688922 1.0548183
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 2.121526e+00 9.801700e-01 2.16445 0.030430
## TrailsA -2.679776e-03 6.228199e-03 -0.43027 0.667003
## Age -2.013402e-02 1.461542e-02 -1.37759 0.168331
## SexMen -4.213784e-01 2.389017e-01 -1.76381 0.077763
## PovStatBelow 2.822027e-01 2.726807e-01 1.03492 0.300706
## WRATtotal 2.212582e-02 1.463379e-02 1.51197 0.130542
## TrailsA:Age 1.004140e-07 1.221483e-04 0.00082 0.999344
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 505.40142 on 640 degrees of freedom
## Residual deviance: 491.14329 on 634 degrees of freedom
## AIC: 505.14329
##
## Number of Fisher Scoring iterations: 5
vif(TMTAlog4)
## TrailsA Age Sex PovStat WRATtotal TrailsA:Age
## 21.079575532 1.286587355 1.017576242 1.052545295 1.025245722 21.676103491
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
## -0.5608951 -0.5372888 -0.5190227 -0.4883552 2.2263059
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.640211931 0.304566153 -5.38540 7.2282e-08
## TrailsA -0.009264020 0.008948915 -1.03521 0.30057
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 482.53265 on 640 degrees of freedom
## Residual deviance: 479.73415 on 639 degrees of freedom
## AIC: 483.73415
##
## Number of Fisher Scoring iterations: 7
exp(cbind(OR = coef(TMTAlog1), confint(TMTAlog1)))
## OR 2.5 % 97.5 %
## (Intercept) 0.1939389362 0.1244338612 0.3673428443
## TrailsA 0.9907787590 0.9713986802 1.0007161339
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
## -0.8272530 -0.5522101 -0.4745035 -0.3959299 2.4047282
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.145135462 1.048088627 -1.09259 0.274572
## TrailsA -0.004516216 0.005776681 -0.78180 0.434332
## Age -0.031615701 0.013958875 -2.26492 0.023518
## SexMen -0.134524446 0.244944505 -0.54920 0.582866
## PovStatBelow 0.494295327 0.253357740 1.95098 0.051060
## WRATtotal 0.015313867 0.017314465 0.88446 0.376451
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 482.53265 on 640 degrees of freedom
## Residual deviance: 468.09957 on 635 degrees of freedom
## AIC: 480.09957
##
## Number of Fisher Scoring iterations: 6
confint(TMTAlog2)
## 2.5 % 97.5 %
## (Intercept) -3.227477573792 0.899615309574
## TrailsA -0.022715454116 0.001843390865
## Age -0.059337811062 -0.004450463585
## SexMen -0.619092649700 0.344071504168
## PovStatBelow -0.007335472836 0.988746756401
## WRATtotal -0.017925325967 0.050237206634
exp(cbind(OR = coef(TMTAlog2), confint(TMTAlog2)))
## OR 2.5 % 97.5 %
## (Intercept) 0.3181808135 0.03965740561 2.4586571074
## TrailsA 0.9954939672 0.97754059936 1.0018450910
## Age 0.9688788496 0.94238836617 0.9955594251
## SexMen 0.8741315097 0.53843276315 1.4106795014
## PovStatBelow 1.6393426319 0.99269136608 2.6878638125
## WRATtotal 1.0154317246 0.98223437702 1.0515204944
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
## -0.9894222 -0.5443506 -0.4699886 -0.3921826 2.4262481
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.331294496 1.044866094 -1.27413 0.202618
## TrailsA -0.001252700 0.004948351 -0.25315 0.800148
## SexMen -0.021008881 0.534448767 -0.03931 0.968644
## PovStatBelow 1.618462413 0.880724791 1.83765 0.066114
## Age -0.029361037 0.014053284 -2.08927 0.036684
## WRATtotal 0.015345518 0.017366002 0.88365 0.376884
## TrailsA:SexMen -0.004939713 0.013812181 -0.35763 0.720617
## TrailsA:PovStatBelow -0.039750254 0.029074749 -1.36717 0.171571
## SexMen:PovStatBelow -1.133556613 1.100886161 -1.02968 0.303162
## TrailsA:SexMen:PovStatBelow 0.041847970 0.033500105 1.24919 0.211596
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 482.53265 on 640 degrees of freedom
## Residual deviance: 465.71973 on 631 degrees of freedom
## AIC: 485.71973
##
## Number of Fisher Scoring iterations: 7
confint(TMTAlog3)
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## 2.5 % 97.5 %
## (Intercept) -3.4137478670839 0.698829624157
## TrailsA -0.0220489361591 0.004786185956
## SexMen -0.9481968749937 1.312400912817
## PovStatBelow -0.0003686128361 3.448137617000
## Age -0.0573005753812 -0.002062199720
## WRATtotal -0.0179867671432 0.050352342555
## TrailsA:SexMen -0.0459696862796 0.019386310305
## TrailsA:PovStatBelow -0.1039308689203 0.010005756841
## SexMen:PovStatBelow -3.4509513002666 1.159887824131
## TrailsA:SexMen:PovStatBelow -0.0251526118408 0.116801661448
exp(cbind(OR = coef(TMTAlog3), confint(TMTAlog3)))
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## OR 2.5 % 97.5 %
## (Intercept) 0.2641351180 0.03291759812 2.0113972386
## TrailsA 0.9987480845 0.97819236490 1.0047976580
## SexMen 0.9792102677 0.38743899494 3.7150826026
## PovStatBelow 5.0453267214 0.99963145509 31.4417811088
## Age 0.9710658102 0.94431019031 0.9979399252
## WRATtotal 1.0154638646 0.98217402924 1.0516415692
## TrailsA:SexMen 0.9950724671 0.95507091350 1.0195754451
## TrailsA:PovStatBelow 0.9610294220 0.90128760225 1.0100559818
## SexMen:PovStatBelow 0.3218863928 0.03171545111 3.1895754626
## TrailsA:SexMen:PovStatBelow 1.0427359398 0.97516107954 1.1238964956
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
## -2.0179429 0.5297853 0.5317403 0.5352407 0.5783476
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 1.9095514753 0.1585262803 12.04565 < 2e-16
## TrailsB -0.0003433842 0.0008115642 -0.42311 0.67221
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 505.40142 on 640 degrees of freedom
## Residual deviance: 505.22752 on 639 degrees of freedom
## AIC: 509.22752
##
## Number of Fisher Scoring iterations: 4
confint(TMTBlog1)
## 2.5 % 97.5 %
## (Intercept) 1.603528690005 2.226283495151
## TrailsB -0.001843453174 0.001370329363
exp(cbind(OR = coef(TMTBlog1), confint(TMTBlog1)))
## OR 2.5 % 97.5 %
## (Intercept) 6.7500605506 4.9705410134 9.265367229
## TrailsB 0.9996566747 0.9981582449 1.001371269
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
## -2.3619201 0.4150226 0.5003701 0.5759826 0.8584249
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 1.8020518007 0.9778048130 1.84296 0.065335
## TrailsB 0.0006771207 0.0009539781 0.70979 0.477837
## Age -0.0237146195 0.0133421780 -1.77742 0.075500
## SexMen -0.4354462923 0.2367388629 -1.83935 0.065863
## PovStatBelow 0.3039214092 0.2721369360 1.11680 0.264082
## WRATtotal 0.0287736507 0.0161637462 1.78014 0.075054
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 505.40142 on 640 degrees of freedom
## Residual deviance: 493.99091 on 635 degrees of freedom
## AIC: 505.99091
##
## Number of Fisher Scoring iterations: 5
confint(TMTBlog2)
## 2.5 % 97.5 %
## (Intercept) -0.095930604996 3.744570374303
## TrailsB -0.001082010488 0.002691321510
## Age -0.050095753808 0.002318790196
## SexMen -0.905052949857 0.025652999036
## PovStatBelow -0.215831013739 0.855443932277
## WRATtotal -0.003227790918 0.060312930158
exp(cbind(OR = coef(TMTBlog2), confint(TMTBlog2)))
## OR 2.5 % 97.5 %
## (Intercept) 6.0620728779 0.9085270611 42.290834086
## TrailsB 1.0006773500 0.9989185747 1.002694946
## Age 0.9765643625 0.9511383450 1.002321481
## SexMen 0.6469758623 0.4045204652 1.025984869
## PovStatBelow 1.3551625487 0.8058714716 2.352418463
## WRATtotal 1.0291916113 0.9967774128 1.062168879
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
## -2.3117180 0.4143321 0.4919177 0.5758064 0.9089105
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 1.8470930729 1.0170851298 1.81607 0.069360
## TrailsB 0.0009530415 0.0016848834 0.56564 0.571637
## SexMen -0.3776448395 0.3832676444 -0.98533 0.324462
## PovStatBelow 0.1350920168 0.5644040131 0.23935 0.810832
## Age -0.0247057097 0.0134045297 -1.84309 0.065316
## WRATtotal 0.0293986503 0.0166197474 1.76890 0.076911
## TrailsB:SexMen -0.0011459927 0.0019778146 -0.57942 0.562303
## TrailsB:PovStatBelow -0.0002674949 0.0033940544 -0.07881 0.937182
## SexMen:PovStatBelow -0.4577983756 0.8724901955 -0.52470 0.599790
## TrailsB:SexMen:PovStatBelow 0.0068832353 0.0064133721 1.07326 0.283153
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 505.40142 on 640 degrees of freedom
## Residual deviance: 490.68327 on 631 degrees of freedom
## AIC: 510.68327
##
## Number of Fisher Scoring iterations: 6
confint(TMTBlog3)
## 2.5 % 97.5 %
## (Intercept) -0.130044656883 3.866276278586
## TrailsB -0.001972185116 0.004876943445
## SexMen -1.136178955393 0.373151204892
## PovStatBelow -0.995653315165 1.257161817210
## Age -0.051214434188 0.001446409501
## WRATtotal -0.003491844780 0.061838894212
## TrailsB:SexMen -0.005444410412 0.002553614571
## TrailsB:PovStatBelow -0.006344026114 0.008143077348
## SexMen:PovStatBelow -2.277018711242 1.194936380911
## TrailsB:SexMen:PovStatBelow -0.004563295552 0.022491682612
exp(cbind(OR = coef(TMTBlog3), confint(TMTBlog3)))
## OR 2.5 % 97.5 %
## (Intercept) 6.3413588358 0.8780562188 47.764193966
## TrailsB 1.0009534958 0.9980297584 1.004888855
## SexMen 0.6854739107 0.3210434023 1.452303919
## PovStatBelow 1.1446421059 0.3694819775 3.515429882
## Age 0.9755969786 0.9500749201 1.001447456
## WRATtotal 1.0298350567 0.9965142446 1.063790948
## TrailsB:SexMen 0.9988546637 0.9945703835 1.002556878
## TrailsB:PovStatBelow 0.9997325409 0.9936760547 1.008176322
## SexMen:PovStatBelow 0.6326750261 0.1025896005 3.303347608
## TrailsB:SexMen:PovStatBelow 1.0069069792 0.9954471005 1.022746528
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
## -0.5179902 -0.5163131 -0.5161637 -0.5160838 2.0407011
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.949543e+00 1.640463e-01 -11.88410 < 2e-16
## TrailsB 1.437601e-05 8.734048e-04 0.01646 0.98687
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 482.53265 on 640 degrees of freedom
## Residual deviance: 482.53238 on 639 degrees of freedom
## AIC: 486.53238
##
## Number of Fisher Scoring iterations: 4
confint(TMTBlog1)
## 2.5 % 97.5 %
## (Intercept) -2.27705346305 -1.632336504412
## TrailsB -0.00185338485 0.001610947636
exp(cbind(OR = coef(TMTBlog1), confint(TMTBlog1)))
## OR 2.5 % 97.5 %
## (Intercept) 0.1423391726 0.1025860354 0.1954723182
## TrailsB 1.0000143761 0.9981483316 1.0016122459
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
## -0.8296252 -0.5502548 -0.4702835 -0.3852792 2.4149812
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.5644450813 1.0563771828 -1.48095 0.138619
## TrailsB 0.0009793906 0.0009424128 1.03924 0.298694
## Age -0.0361937961 0.0140596627 -2.57430 0.010044
## SexMen -0.1672178649 0.2444699199 -0.68400 0.493974
## PovStatBelow 0.4877010257 0.2524718526 1.93170 0.053396
## WRATtotal 0.0236622246 0.0180503672 1.31090 0.189892
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 482.53265 on 640 degrees of freedom
## Residual deviance: 468.41667 on 635 degrees of freedom
## AIC: 480.41667
##
## Number of Fisher Scoring iterations: 5
confint(TMTBlog2)
## 2.5 % 97.5 %
## (Intercept) -3.672079914651 0.479179127932
## TrailsB -0.001004125849 0.002734022888
## Age -0.064203068212 -0.008963590412
## SexMen -0.651102760992 0.310192286678
## PovStatBelow -0.012396682288 0.980167579083
## WRATtotal -0.010869929017 0.060084244206
exp(cbind(OR = coef(TMTBlog2), confint(TMTBlog2)))
## OR 2.5 % 97.5 %
## (Intercept) 0.2092040722 0.02542353613 1.6147483562
## TrailsB 1.0009798704 0.99899637812 1.0027377637
## Age 0.9644533681 0.93781453987 0.9910764628
## SexMen 0.8460152742 0.52147040235 1.3636873078
## PovStatBelow 1.6285678773 0.98767984004 2.6649027865
## WRATtotal 1.0239443963 0.98918893518 1.0619260039
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
## -0.8516227 -0.5472787 -0.4694062 -0.3835559 2.4223542
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.5047592688 1.0909002395 -1.37937 0.167780
## TrailsB 0.0006868601 0.0016674226 0.41193 0.680391
## SexMen -0.2700782573 0.4201320509 -0.64284 0.520327
## PovStatBelow 0.3317966058 0.4541254889 0.73063 0.465007
## Age -0.0359140055 0.0140777207 -2.55112 0.010738
## WRATtotal 0.0231490066 0.0183290360 1.26297 0.206600
## TrailsB:SexMen 0.0005718268 0.0021795065 0.26237 0.793040
## TrailsB:PovStatBelow 0.0009890884 0.0024708400 0.40030 0.688932
## SexMen:PovStatBelow 0.3715339090 0.6866325064 0.54110 0.588442
## TrailsB:SexMen:PovStatBelow -0.0022662872 0.0036875071 -0.61459 0.538829
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 482.53265 on 640 degrees of freedom
## Residual deviance: 467.98506 on 631 degrees of freedom
## AIC: 487.98506
##
## Number of Fisher Scoring iterations: 5
confint(TMTBlog3)
## 2.5 % 97.5 %
## (Intercept) -3.678000065177 0.610271056575
## TrailsB -0.003187690537 0.003605598626
## SexMen -1.104549024141 0.551216412191
## PovStatBelow -0.569590397778 1.219846577596
## Age -0.063961083343 -0.008649269520
## WRATtotal -0.011949379169 0.060101997998
## TrailsB:SexMen -0.003782969485 0.005106246076
## TrailsB:PovStatBelow -0.004013429847 0.006037226265
## SexMen:PovStatBelow -0.976968870741 1.726534192337
## TrailsB:SexMen:PovStatBelow -0.010127718785 0.004822559580
exp(cbind(OR = coef(TMTBlog3), confint(TMTBlog3)))
## OR 2.5 % 97.5 %
## (Intercept) 0.2220707468 0.02527346962 1.8409303274
## TrailsB 1.0006870961 0.99681738475 1.0036121066
## SexMen 0.7633197567 0.33136028404 1.7353626508
## PovStatBelow 1.3934693960 0.56575712662 3.3866681030
## Age 0.9647232508 0.93804150426 0.9913880278
## WRATtotal 1.0234190243 0.98812173114 1.0619448573
## TrailsB:SexMen 1.0005719904 0.99622417693 1.0051193052
## TrailsB:PovStatBelow 1.0009895777 0.99599461320 1.0060554870
## SexMen:PovStatBelow 1.4499570118 0.37645044118 5.6211383251
## TrailsB:SexMen:PovStatBelow 0.9977362789 0.98992339386 1.0048342068
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
## -2.2372322 0.4749479 0.5204598 0.5697073 0.6798854
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 1.15244832 0.39762516 2.89833 0.0037516
## DigitSpanFwd 0.09728752 0.05324606 1.82713 0.0676801
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 505.40142 on 640 degrees of freedom
## Residual deviance: 501.92974 on 639 degrees of freedom
## AIC: 505.92974
##
## Number of Fisher Scoring iterations: 4
confint(DSFlog1)
## 2.5 % 97.5 %
## (Intercept) 0.374377408189 1.9358468923
## DigitSpanFwd -0.004952947684 0.2041858048
exp(cbind(OR = coef(DSFlog1), confint(DSFlog1)))
## OR 2.5 % 97.5 %
## (Intercept) 3.165934647 1.4540858308 6.929910459
## DigitSpanFwd 1.102177230 0.9950592979 1.226526027
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
## -2.3744128 0.4081614 0.4996690 0.5857642 0.8451478
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 1.78186681 0.95031940 1.87502 0.060790
## DigitSpanFwd 0.07980024 0.06020625 1.32545 0.185023
## Age -0.02014341 0.01315337 -1.53143 0.125664
## SexMen -0.44464385 0.23712349 -1.87516 0.060771
## PovStatBelow 0.31763163 0.27195854 1.16794 0.242830
## WRATtotal 0.01382260 0.01643467 0.84106 0.400312
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 505.40142 on 640 degrees of freedom
## Residual deviance: 492.71471 on 635 degrees of freedom
## AIC: 504.71471
##
## Number of Fisher Scoring iterations: 5
confint(DSFlog2)
## 2.5 % 97.5 %
## (Intercept) -0.05975027523 3.672887542870
## DigitSpanFwd -0.03597106027 0.200534414414
## Age -0.04613506996 0.005539275927
## SexMen -0.91505007325 0.017156077690
## PovStatBelow -0.20190211803 0.868656505971
## WRATtotal -0.01900185525 0.045600638797
exp(cbind(OR = coef(DSFlog2), confint(DSFlog2)))
## OR 2.5 % 97.5 %
## (Intercept) 5.9409366622 0.9419997449 39.365411218
## DigitSpanFwd 1.0830706896 0.9646682103 1.222055668
## Age 0.9800581122 0.9549129734 1.005554646
## SexMen 0.6410525438 0.4004965714 1.017304088
## PovStatBelow 1.3738700750 0.8171749107 2.383706206
## WRATtotal 1.0139185723 0.9811775419 1.046656334
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
## -2.2630008 0.4174068 0.4863972 0.5671236 0.8935774
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 2.05209182 1.11256614 1.84447 0.065115
## DigitSpanFwd 0.04840210 0.09789249 0.49444 0.620995
## SexMen -0.95692387 0.94535760 -1.01223 0.311426
## PovStatBelow 0.19631268 1.31459869 0.14933 0.881291
## Age -0.02016174 0.01320093 -1.52730 0.126687
## WRATtotal 0.01412286 0.01656662 0.85249 0.393943
## DigitSpanFwd:SexMen 0.05684389 0.12434437 0.45715 0.647564
## DigitSpanFwd:PovStatBelow -0.01233986 0.17920093 -0.06886 0.945101
## SexMen:PovStatBelow 0.20818361 1.84272356 0.11298 0.910050
## DigitSpanFwd:SexMen:PovStatBelow 0.02780642 0.25559331 0.10879 0.913368
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 505.40142 on 640 degrees of freedom
## Residual deviance: 491.87867 on 631 degrees of freedom
## AIC: 511.87867
##
## Number of Fisher Scoring iterations: 5
confint(DSFlog3)
## 2.5 % 97.5 %
## (Intercept) -0.09264103587 4.279188872301
## DigitSpanFwd -0.13709893733 0.248989556080
## SexMen -2.82517597018 0.891305947020
## PovStatBelow -2.37156875814 2.815163307488
## Age -0.04624725628 0.005614340697
## WRATtotal -0.01894721632 0.046169635037
## DigitSpanFwd:SexMen -0.18968590728 0.299953466512
## DigitSpanFwd:PovStatBelow -0.35703284673 0.351928833638
## SexMen:PovStatBelow -3.43757513188 3.815705043739
## DigitSpanFwd:SexMen:PovStatBelow -0.47184254317 0.537277577280
exp(cbind(OR = coef(DSFlog3), confint(DSFlog3)))
## OR 2.5 % 97.5 %
## (Intercept) 7.7841671746 0.91152064479 72.181867544
## DigitSpanFwd 1.0495926099 0.87188395999 1.282728636
## SexMen 0.3840725283 0.05929822119 2.438311879
## PovStatBelow 1.2169073431 0.09333419260 16.695902124
## Age 0.9800401451 0.95480585126 1.005630131
## WRATtotal 1.0142230561 0.98123115387 1.047252047
## DigitSpanFwd:SexMen 1.0584905589 0.82721891659 1.349795995
## DigitSpanFwd:PovStatBelow 0.9877359661 0.69974951287 1.421807335
## SexMen:PovStatBelow 1.2314392517 0.03214253231 45.408760262
## DigitSpanFwd:SexMen:PovStatBelow 1.0281966296 0.62385173490 1.711341520
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
## -0.5494205 -0.5236506 -0.5136389 -0.5037942 2.0889323
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -2.10335662 0.40909469 -5.14149 2.7257e-07
## DigitSpanFwd 0.02063155 0.05157483 0.40003 0.68913
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 482.53265 on 640 degrees of freedom
## Residual deviance: 482.37355 on 639 degrees of freedom
## AIC: 486.37355
##
## Number of Fisher Scoring iterations: 4
confint(DSFlog1)
## 2.5 % 97.5 %
## (Intercept) -2.91625120026 -1.3096826595
## DigitSpanFwd -0.08208485615 0.1205407414
exp(cbind(OR = coef(DSFlog1), confint(DSFlog1)))
## OR 2.5 % 97.5 %
## (Intercept) 0.1220460782 0.05413625335 0.2699056948
## DigitSpanFwd 1.0208458513 0.92119378642 1.1281067007
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
## -0.8265004 -0.5482389 -0.4738375 -0.3939720 2.3907656
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.298524843 1.038991730 -1.24979 0.211375
## DigitSpanFwd -0.003763441 0.059056178 -0.06373 0.949188
## Age -0.033411221 0.013805699 -2.42010 0.015516
## SexMen -0.154817914 0.244319833 -0.63367 0.526297
## PovStatBelow 0.486848094 0.253371728 1.92148 0.054672
## WRATtotal 0.018102113 0.018831838 0.96125 0.336426
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 482.53265 on 640 degrees of freedom
## Residual deviance: 469.42166 on 635 degrees of freedom
## AIC: 481.42166
##
## Number of Fisher Scoring iterations: 5
confint(DSFlog2)
## 2.5 % 97.5 %
## (Intercept) -3.37067738329 0.710740633509
## DigitSpanFwd -0.12148521302 0.110537824307
## Age -0.06090493211 -0.006661779747
## SexMen -0.63831368881 0.322397628627
## PovStatBelow -0.01492452321 0.981166641298
## WRATtotal -0.01785195501 0.056119758928
exp(cbind(OR = coef(DSFlog2), confint(DSFlog2)))
## OR 2.5 % 97.5 %
## (Intercept) 0.2729341170 0.03436635027 2.0354982587
## DigitSpanFwd 0.9962436315 0.88560414867 1.1168785934
## Age 0.9671407691 0.94091268611 0.9933603607
## SexMen 0.8565711326 0.52818235330 1.3804335667
## PovStatBelow 1.6271794117 0.98518629550 2.6675665206
## WRATtotal 1.0182669492 0.98230644714 1.0577243481
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
## -0.8189416 -0.5530261 -0.4756359 -0.3928898 2.4094491
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.19141039 1.18123439 -1.00861 0.313159
## DigitSpanFwd -0.01788098 0.09489921 -0.18842 0.850547
## SexMen -0.47145298 1.09257205 -0.43151 0.666099
## PovStatBelow 0.30840246 1.14432983 0.26950 0.787541
## Age -0.03343420 0.01380823 -2.42132 0.015464
## WRATtotal 0.01844323 0.01895623 0.97294 0.330584
## DigitSpanFwd:SexMen 0.03635870 0.13300570 0.27336 0.784575
## DigitSpanFwd:PovStatBelow 0.01955502 0.14871591 0.13149 0.895386
## SexMen:PovStatBelow 0.55778424 1.74829471 0.31904 0.749693
## DigitSpanFwd:SexMen:PovStatBelow -0.06335783 0.22404819 -0.28279 0.777340
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 482.53265 on 640 degrees of freedom
## Residual deviance: 469.29546 on 631 degrees of freedom
## AIC: 489.29546
##
## Number of Fisher Scoring iterations: 5
confint(DSFlog3)
## 2.5 % 97.5 %
## (Intercept) -3.55771444654 1.082542539390
## DigitSpanFwd -0.21071775415 0.163745917874
## SexMen -2.63659094586 1.663594558824
## PovStatBelow -1.95436295090 2.551997385183
## Age -0.06093179996 -0.006678361512
## WRATtotal -0.01776097756 0.056701218367
## DigitSpanFwd:SexMen -0.22512921830 0.298520849793
## DigitSpanFwd:PovStatBelow -0.27523533431 0.311318252979
## SexMen:PovStatBelow -2.87426109737 3.999016173664
## DigitSpanFwd:SexMen:PovStatBelow -0.50694634267 0.374844061673
exp(cbind(OR = coef(DSFlog3), confint(DSFlog3)))
## OR 2.5 % 97.5 %
## (Intercept) 0.3037924956 0.02850389750 2.9521760408
## DigitSpanFwd 0.9822779381 0.81000265451 1.1779149899
## SexMen 0.6240948123 0.07160495913 5.2782497648
## PovStatBelow 1.3612487285 0.14165468895 12.8327100663
## Age 0.9671185479 0.94088740615 0.9933438892
## WRATtotal 1.0186143611 0.98239581894 1.0583395507
## DigitSpanFwd:SexMen 1.0370277652 0.79841304252 1.3478636396
## DigitSpanFwd:PovStatBelow 1.0197474749 0.75939339088 1.3652236385
## SexMen:PovStatBelow 1.7467977281 0.05645784094 54.5444613497
## DigitSpanFwd:SexMen:PovStatBelow 0.9386075471 0.60233208915 1.4547645434
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
## -2.1240523 0.5008340 0.5328424 0.5665973 0.6394971
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 1.48331289 0.32546747 4.55748 5.177e-06
## DigitSpanBck 0.06617895 0.05387261 1.22843 0.21928
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 505.40142 on 640 degrees of freedom
## Residual deviance: 503.84797 on 639 degrees of freedom
## AIC: 507.84797
##
## Number of Fisher Scoring iterations: 4
confint(DSBlog1)
## 2.5 % 97.5 %
## (Intercept) 0.84949262396 2.1271669670
## DigitSpanBck -0.03708871353 0.1744528619
exp(cbind(OR = coef(DSBlog1), confint(DSBlog1)))
## OR 2.5 % 97.5 %
## (Intercept) 4.407523139 2.338460072 8.391060953
## DigitSpanBck 1.068417896 0.963590648 1.190594619
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
## -2.3545317 0.4187843 0.4988313 0.5806451 0.8067070
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 2.00096614 0.93370968 2.14303 0.032111
## DigitSpanBck 0.03632517 0.06182840 0.58752 0.556857
## Age -0.02133343 0.01313077 -1.62469 0.104229
## SexMen -0.43391381 0.23663042 -1.83372 0.066696
## PovStatBelow 0.30340071 0.27136385 1.11806 0.263542
## WRATtotal 0.01871284 0.01689828 1.10738 0.268129
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 505.40142 on 640 degrees of freedom
## Residual deviance: 494.17108 on 635 degrees of freedom
## AIC: 506.17108
##
## Number of Fisher Scoring iterations: 4
confint(DSBlog2)
## 2.5 % 97.5 %
## (Intercept) 0.19390854671 3.86170894232
## DigitSpanBck -0.08303012544 0.15984463056
## Age -0.04727808738 0.00431379176
## SexMen -0.90331488233 0.02703804271
## PovStatBelow -0.21507587601 0.85330506683
## WRATtotal -0.01488367612 0.05154513504
exp(cbind(OR = coef(DSBlog2), confint(DSBlog2)))
## OR 2.5 % 97.5 %
## (Intercept) 7.3961984350 1.2139852549 47.546536267
## DigitSpanBck 1.0369929958 0.9203234217 1.173328557
## Age 0.9788925150 0.9538221148 1.004323110
## SexMen 0.6479681032 0.4052241605 1.027406887
## PovStatBelow 1.3544570938 0.8064802454 2.347392334
## WRATtotal 1.0188890233 0.9852265383 1.052896708
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
## -2.3575917 0.3941302 0.4862270 0.5675464 0.9101301
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 1.86140021 1.04549342 1.78040 0.075010
## DigitSpanBck 0.09322733 0.11540742 0.80781 0.419200
## SexMen -0.62912202 0.78860021 -0.79777 0.425004
## PovStatBelow 1.65113122 1.19085432 1.38651 0.165591
## Age -0.02285358 0.01322837 -1.72762 0.084056
## WRATtotal 0.01774683 0.01713537 1.03568 0.300350
## DigitSpanBck:SexMen 0.01376219 0.13381759 0.10284 0.918088
## DigitSpanBck:PovStatBelow -0.27450735 0.19328529 -1.42022 0.155544
## SexMen:PovStatBelow 0.18041472 1.54836584 0.11652 0.907241
## DigitSpanBck:SexMen:PovStatBelow 0.03101892 0.24939724 0.12438 0.901018
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 505.40142 on 640 degrees of freedom
## Residual deviance: 489.28665 on 631 degrees of freedom
## AIC: 509.28665
##
## Number of Fisher Scoring iterations: 5
confint(DSBlog3)
## 2.5 % 97.5 %
## (Intercept) -0.15637002152 3.952719490181
## DigitSpanBck -0.12406374042 0.329083867172
## SexMen -2.18681354208 0.911473493328
## PovStatBelow -0.63382657143 4.071847414250
## Age -0.04900141614 0.002971879691
## WRATtotal -0.01629414780 0.051059483301
## DigitSpanBck:SexMen -0.25325179436 0.273077030932
## DigitSpanBck:PovStatBelow -0.65478254122 0.109955684785
## SexMen:PovStatBelow -2.88490444221 3.215516594545
## DigitSpanBck:SexMen:PovStatBelow -0.45930083238 0.523585571061
exp(cbind(OR = coef(DSBlog3), confint(DSBlog3)))
## OR 2.5 % 97.5 %
## (Intercept) 6.4327376266 0.85524267362 52.076796776
## DigitSpanBck 1.0977112508 0.88332353568 1.389694400
## SexMen 0.5330596112 0.11227393541 2.487985864
## PovStatBelow 5.2128734000 0.53055769475 58.665241546
## Age 0.9774055832 0.95217978127 1.002976300
## WRATtotal 1.0179052387 0.98383788374 1.052385491
## DigitSpanBck:SexMen 1.0138573286 0.77627239620 1.314001460
## DigitSpanBck:PovStatBelow 0.7599464161 0.51955503211 1.116228603
## SexMen:PovStatBelow 1.1977139809 0.05586012715 24.916160139
## DigitSpanBck:SexMen:PovStatBelow 1.0315050224 0.63172517292 1.688069505
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
## -0.6460561 -0.5331854 -0.4994967 -0.4833622 2.2165849
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -2.36700173 0.33814611 -6.99994 2.5608e-12
## DigitSpanBck 0.06971369 0.05143053 1.35549 0.17526
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 482.53265 on 640 degrees of freedom
## Residual deviance: 480.73485 on 639 degrees of freedom
## AIC: 484.73485
##
## Number of Fisher Scoring iterations: 4
confint(DSBlog1)
## 2.5 % 97.5 %
## (Intercept) -3.04161373984 -1.7138403142
## DigitSpanBck -0.03277604975 0.1693351775
exp(cbind(OR = coef(DSBlog1), confint(DSBlog1)))
## OR 2.5 % 97.5 %
## (Intercept) 0.09376142765 0.04775775868 0.1801725431
## DigitSpanBck 1.07220115258 0.96775526435 1.1845170958
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
## -0.8504546 -0.5507446 -0.4663366 -0.3876885 2.4000426
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.345693616 1.024586010 -1.31340 0.189047
## DigitSpanBck 0.059014101 0.060632420 0.97331 0.330400
## Age -0.031850990 0.013777263 -2.31185 0.020786
## SexMen -0.170211925 0.244791244 -0.69534 0.486845
## PovStatBelow 0.502315329 0.253971356 1.97784 0.047946
## WRATtotal 0.008856137 0.019220067 0.46078 0.644960
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 482.53265 on 640 degrees of freedom
## Residual deviance: 468.48895 on 635 degrees of freedom
## AIC: 480.48895
##
## Number of Fisher Scoring iterations: 5
confint(DSBlog2)
## 2.5 % 97.5 %
## (Intercept) -3.3916650371716 0.633726336713
## DigitSpanBck -0.0613206922680 0.176938898028
## Age -0.0592997147084 -0.005165805139
## SexMen -0.6548052358143 0.307756719751
## PovStatBelow -0.0004322829643 0.998046082708
## WRATtotal -0.0279935568715 0.047521446254
exp(cbind(OR = coef(DSBlog2), confint(DSBlog2)))
## OR 2.5 % 97.5 %
## (Intercept) 0.2603590561 0.0336525974 1.8846202406
## DigitSpanBck 1.0607901986 0.9405215734 1.1935581621
## Age 0.9686509096 0.9424242684 0.9948475147
## SexMen 0.8434860420 0.5195432412 1.3603699975
## PovStatBelow 1.6525430260 0.9995678105 2.7129757161
## WRATtotal 1.0088954682 0.9723946320 1.0486686909
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
## -0.8330370 -0.5503165 -0.4685234 -0.3883880 2.4253245
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.255613150 1.120610968 -1.12047 0.262513
## DigitSpanBck 0.044520394 0.107314778 0.41486 0.678246
## SexMen -0.455390497 0.908307828 -0.50136 0.616117
## PovStatBelow 0.408942227 1.027509466 0.39799 0.690635
## Age -0.031862010 0.013777818 -2.31256 0.020747
## WRATtotal 0.009189051 0.019285385 0.47648 0.633734
## DigitSpanBck:SexMen 0.038519326 0.133091347 0.28942 0.772260
## DigitSpanBck:PovStatBelow 0.008995643 0.165177626 0.05446 0.956568
## SexMen:PovStatBelow 0.440726732 1.437950626 0.30650 0.759227
## DigitSpanBck:SexMen:PovStatBelow -0.056577865 0.225188727 -0.25125 0.801624
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 482.53265 on 640 degrees of freedom
## Residual deviance: 468.32410 on 631 degrees of freedom
## AIC: 488.3241
##
## Number of Fisher Scoring iterations: 5
confint(DSBlog3)
## 2.5 % 97.5 %
## (Intercept) -3.50061722477 0.901710583281
## DigitSpanBck -0.17306221621 0.249958352967
## SexMen -2.25539641606 1.319990640181
## PovStatBelow -1.63003034907 2.417495429761
## Age -0.05931303802 -0.005176631857
## WRATtotal -0.02780733629 0.047973667806
## DigitSpanBck:SexMen -0.22130481581 0.302664129855
## DigitSpanBck:PovStatBelow -0.31741437663 0.334113806200
## SexMen:PovStatBelow -2.38215259083 3.271157547582
## DigitSpanBck:SexMen:PovStatBelow -0.50186031831 0.384406407812
exp(cbind(OR = coef(DSBlog3), confint(DSBlog3)))
## OR 2.5 % 97.5 %
## (Intercept) 0.2849011074 0.03017875060 2.4638140677
## DigitSpanBck 1.0455262988 0.84108528408 1.2839719420
## SexMen 0.6342002665 0.10483197842 3.7433863397
## PovStatBelow 1.5052247559 0.19592362794 11.2177285128
## Age 0.9686402354 0.94241171228 0.9948367438
## WRATtotal 1.0092314004 0.97257572880 1.0491430287
## DigitSpanBck:SexMen 1.0392708131 0.80147234161 1.3534598014
## DigitSpanBck:PovStatBelow 1.0090362256 0.72802901441 1.3967020879
## SexMen:PovStatBelow 1.5538360305 0.09235156826 26.3418136048
## DigitSpanBck:SexMen:PovStatBelow 0.9449928997 0.60540336851 1.4687422287
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
## -2.1216821 0.4953105 0.5325412 0.5586829 0.6214751
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 1.36583844 0.42838045 3.18838 0.0014307
## FluencyWord 0.02578806 0.02163196 1.19213 0.2332111
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 505.40142 on 640 degrees of freedom
## Residual deviance: 503.95315 on 639 degrees of freedom
## AIC: 507.95315
##
## Number of Fisher Scoring iterations: 4
confint(ANlog1)
## 2.5 % 97.5 %
## (Intercept) 0.53088097605 2.21242735437
## FluencyWord -0.01598520463 0.06893413678
exp(cbind(OR = coef(ANlog1), confint(ANlog1)))
## OR 2.5 % 97.5 %
## (Intercept) 3.919007526 1.7004296868 9.137870344
## FluencyWord 1.026123447 0.9841418807 1.071365643
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
## -2.3875983 0.4129537 0.4957884 0.5797209 0.8605072
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 1.76445161 0.96978420 1.81943 0.068846
## FluencyWord 0.02273074 0.02415879 0.94089 0.346762
## Age -0.02054506 0.01317532 -1.55936 0.118911
## SexMen -0.46955395 0.24091614 -1.94903 0.051291
## PovStatBelow 0.30850666 0.27181090 1.13500 0.256373
## WRATtotal 0.01839489 0.01565678 1.17488 0.240042
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 505.40142 on 640 degrees of freedom
## Residual deviance: 493.62446 on 635 degrees of freedom
## AIC: 505.62446
##
## Number of Fisher Scoring iterations: 5
confint(ANlog2)
## 2.5 % 97.5 %
## (Intercept) -0.11687211989 3.6921775663759
## FluencyWord -0.02410872691 0.0707277786548
## Age -0.04657233118 0.0051886613254
## SexMen -0.94753111496 -0.0005008864751
## PovStatBelow -0.21071032706 0.8592819822309
## WRATtotal -0.01283072064 0.0487244783733
exp(cbind(OR = coef(ANlog2), confint(ANlog2)))
## OR 2.5 % 97.5 %
## (Intercept) 5.8383697980 0.8896989607 40.1321422807
## FluencyWord 1.0229910538 0.9761795670 1.0732890139
## Age 0.9796645505 0.9544955183 1.0052021457
## SexMen 0.6252811112 0.3876970222 0.9994992389
## PovStatBelow 1.3613905824 0.8100086705 2.3614645114
## WRATtotal 1.0185651141 0.9872512421 1.0499310322
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
## -2.3284371 0.4143278 0.4834821 0.5638555 1.0061477
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 2.008377358 1.153265820 1.74147 0.081601
## FluencyWord 0.011819924 0.038971311 0.30330 0.761663
## SexMen -1.424780780 1.027353089 -1.38685 0.165489
## PovStatBelow -0.019326011 1.426950587 -0.01354 0.989194
## Age -0.020952952 0.013261269 -1.58001 0.114104
## WRATtotal 0.019146126 0.015830367 1.20946 0.226488
## FluencyWord:SexMen 0.043940403 0.052092261 0.84351 0.398943
## FluencyWord:PovStatBelow 0.007168668 0.077333771 0.09270 0.926144
## SexMen:PovStatBelow 2.731875803 2.084764361 1.31040 0.190060
## FluencyWord:SexMen:PovStatBelow -0.119742146 0.105977707 -1.12988 0.258527
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 505.40142 on 640 degrees of freedom
## Residual deviance: 490.61310 on 631 degrees of freedom
## AIC: 510.6131
##
## Number of Fisher Scoring iterations: 5
confint(ANlog3)
## 2.5 % 97.5 %
## (Intercept) -0.23228008085 4.298218844366
## FluencyWord -0.06215661083 0.091173976273
## SexMen -3.44849509561 0.586633606389
## PovStatBelow -2.81316385460 2.808538918628
## Age -0.04715221023 0.004947069751
## WRATtotal -0.01238725087 0.049844768936
## FluencyWord:SexMen -0.05919917560 0.145632770882
## FluencyWord:PovStatBelow -0.14070102107 0.164603075661
## SexMen:PovStatBelow -1.31542020248 6.892736638041
## FluencyWord:SexMen:PovStatBelow -0.33129449730 0.085873067677
exp(cbind(OR = coef(ANlog3), confint(ANlog3)))
## OR 2.5 % 97.5 %
## (Intercept) 7.4512168733 0.79272406538 73.568639734
## FluencyWord 1.0118900549 0.93973570248 1.095459573
## SexMen 0.2405611932 0.03179344649 1.797925691
## PovStatBelow 0.9808595387 0.06001481356 16.585667497
## Age 0.9792650362 0.95394218678 1.004959327
## WRATtotal 1.0193305881 0.98768915531 1.051107919
## FluencyWord:SexMen 1.0449200791 0.94251902367 1.156771310
## FluencyWord:PovStatBelow 1.0071944244 0.86874901052 1.178925082
## SexMen:PovStatBelow 15.3616754843 0.26836153336 985.093576360
## FluencyWord:SexMen:PovStatBelow 0.8871491626 0.71799369070 1.089668005
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
## -0.5533511 -0.5264666 -0.5148843 -0.4979271 2.1386293
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.71589063 0.44187101 -3.88324 0.00010307
## FluencyWord -0.01189371 0.02197746 -0.54118 0.58838520
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 482.53265 on 640 degrees of freedom
## Residual deviance: 482.23740 on 639 degrees of freedom
## AIC: 486.2374
##
## Number of Fisher Scoring iterations: 4
confint(ANlog1)
## 2.5 % 97.5 %
## (Intercept) -2.59122387187 -0.85664979393
## FluencyWord -0.05568066658 0.03060648696
exp(cbind(OR = coef(ANlog1), confint(ANlog1)))
## OR 2.5 % 97.5 %
## (Intercept) 0.1798035109 0.07492828133 0.4245821399
## FluencyWord 0.9881767416 0.94584112635 1.0310796807
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
## -0.8546205 -0.5507375 -0.4755007 -0.3869497 2.4487942
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.04609337 1.06197791 -0.98504 0.324603
## FluencyWord -0.02459797 0.02437307 -1.00923 0.312866
## Age -0.03520998 0.01391092 -2.53110 0.011370
## SexMen -0.11481253 0.24751137 -0.46387 0.642743
## PovStatBelow 0.47094678 0.25357575 1.85722 0.063279
## WRATtotal 0.02420255 0.01833262 1.32019 0.186771
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 482.53265 on 640 degrees of freedom
## Residual deviance: 468.39410 on 635 degrees of freedom
## AIC: 480.3941
##
## Number of Fisher Scoring iterations: 5
confint(ANlog2)
## 2.5 % 97.5 %
## (Intercept) -3.16048184809 1.011381104822
## FluencyWord -0.07308123930 0.022627659472
## Age -0.06293818393 -0.008280836338
## SexMen -0.60420415115 0.368987111755
## PovStatBelow -0.03137322672 0.965505520224
## WRATtotal -0.01073216937 0.061288898671
exp(cbind(OR = coef(ANlog2), confint(ANlog2)))
## OR 2.5 % 97.5 %
## (Intercept) 0.3513074990 0.04240530324 2.7493955976
## FluencyWord 0.9757020972 0.92952531297 1.0228856069
## Age 0.9654026749 0.93900151728 0.9917533553
## SexMen 0.8915332619 0.54650919233 1.4462689637
## PovStatBelow 1.6015097478 0.96911380640 2.6261148752
## WRATtotal 1.0244978074 0.98932521489 1.0632060286
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
## -0.8524626 -0.5493344 -0.4680542 -0.3800857 2.5157463
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.600446122 1.212630710 -1.31981 0.186897
## FluencyWord 0.003623519 0.036623808 0.09894 0.921187
## SexMen 0.975862761 1.161915414 0.83987 0.400979
## PovStatBelow 1.495935503 1.229963696 1.21624 0.223892
## Age -0.034896957 0.013936941 -2.50392 0.012283
## WRATtotal 0.024425870 0.018370850 1.32960 0.183650
## FluencyWord:SexMen -0.056565304 0.056060986 -1.00900 0.312977
## FluencyWord:PovStatBelow -0.057080798 0.064744182 -0.88164 0.377974
## SexMen:PovStatBelow -1.816965339 1.938738767 -0.93719 0.348661
## FluencyWord:SexMen:PovStatBelow 0.098700237 0.097334923 1.01403 0.310570
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 482.53265 on 640 degrees of freedom
## Residual deviance: 466.96663 on 631 degrees of freedom
## AIC: 486.96663
##
## Number of Fisher Scoring iterations: 5
confint(ANlog3)
## 2.5 % 97.5 %
## (Intercept) -4.01506723107 0.747960668721
## FluencyWord -0.06998105611 0.074381636056
## SexMen -1.31267722001 3.255391216162
## PovStatBelow -0.90815520953 3.931751771500
## Age -0.06267744800 -0.007917640949
## WRATtotal -0.01061634267 0.061555508928
## FluencyWord:SexMen -0.16772931438 0.052793528479
## FluencyWord:PovStatBelow -0.18772678495 0.067587070147
## SexMen:PovStatBelow -5.66030107622 1.961694248319
## FluencyWord:SexMen:PovStatBelow -0.09128824614 0.291518236523
exp(cbind(OR = coef(ANlog3), confint(ANlog3)))
## OR 2.5 % 97.5 %
## (Intercept) 0.2018064676 0.018041741544 2.112687151
## FluencyWord 1.0036300917 0.932411483242 1.077217832
## SexMen 2.6534555209 0.269098654836 25.929756690
## PovStatBelow 4.4635102282 0.403267482254 50.996233217
## Age 0.9657049204 0.939246380636 0.992113621
## WRATtotal 1.0247266255 0.989439811800 1.063489528
## FluencyWord:SexMen 0.9450047695 0.845582690783 1.054211958
## FluencyWord:PovStatBelow 0.9445177510 0.828841128174 1.069923414
## SexMen:PovStatBelow 0.1625181909 0.003481468553 7.111365283
## FluencyWord:SexMen:PovStatBelow 1.1037353907 0.912754574995 1.338458042
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
## -2.3247904 0.4332109 0.5120195 0.5922912 0.8944092
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.55268246 0.38541196 1.43400 0.151571
## StroopMixed 0.03924981 0.01221460 3.21335 0.001312
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 452.03956 on 547 degrees of freedom
## Residual deviance: 441.35555 on 546 degrees of freedom
## AIC: 445.35555
##
## Number of Fisher Scoring iterations: 4
confint(SCWTlog1)
## 2.5 % 97.5 %
## (Intercept) -0.19647466130 1.31824277010
## StroopMixed 0.01558200695 0.06356160915
exp(cbind(OR = coef(SCWTlog1), confint(SCWTlog1)))
## OR 2.5 % 97.5 %
## (Intercept) 1.737908637 0.8216221499 3.736849101
## StroopMixed 1.040030257 1.0157040394 1.065625136
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
## -2.3955975 0.4029892 0.5068707 0.5948066 0.9438007
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.99698150 1.12673566 0.88484 0.376243
## StroopMixed 0.02973078 0.01418808 2.09548 0.036129
## Age -0.01788863 0.01502404 -1.19067 0.233784
## SexMen -0.32165183 0.24817679 -1.29606 0.194955
## PovStatBelow 0.28469513 0.28845607 0.98696 0.323661
## WRATtotal 0.01799801 0.01818214 0.98987 0.322236
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 452.03956 on 547 degrees of freedom
## Residual deviance: 435.88526 on 542 degrees of freedom
## AIC: 447.88526
##
## Number of Fisher Scoring iterations: 5
confint(SCWTlog2)
## 2.5 % 97.5 %
## (Intercept) -1.202319724087 3.22403984948
## StroopMixed 0.002045792217 0.05778364820
## Age -0.047571215322 0.01146232261
## SexMen -0.811751656288 0.16397947805
## PovStatBelow -0.267438077077 0.86796108777
## WRATtotal -0.018211854756 0.05328156080
exp(cbind(OR = coef(SCWTlog2), confint(SCWTlog2)))
## OR 2.5 % 97.5 %
## (Intercept) 2.7100890651 0.3004963342 25.129434529
## StroopMixed 1.0301771476 1.0020478863 1.059485749
## Age 0.9822704262 0.9535425639 1.011528267
## SexMen 0.7249505541 0.4440795099 1.178190136
## PovStatBelow 1.3293566890 0.7653377211 2.382049110
## WRATtotal 1.0181609529 0.9819529789 1.054726573
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
## -2.3827496 0.3928600 0.5067206 0.6023454 0.9832938
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.51878063 1.25468058 0.41348 0.679258
## StroopMixed 0.04567790 0.02265326 2.01639 0.043759
## SexMen 0.36679095 0.92213851 0.39776 0.690806
## PovStatBelow 0.47414614 1.32993836 0.35652 0.721453
## Age -0.01746358 0.01506189 -1.15945 0.246271
## WRATtotal 0.01894128 0.01835879 1.03173 0.302200
## StroopMixed:SexMen -0.02634958 0.02887660 -0.91249 0.361511
## StroopMixed:PovStatBelow -0.01308719 0.04322584 -0.30276 0.762070
## SexMen:PovStatBelow -0.12799566 1.76637272 -0.07246 0.942234
## StroopMixed:SexMen:PovStatBelow 0.01669747 0.05833408 0.28624 0.774695
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 452.03956 on 547 degrees of freedom
## Residual deviance: 434.45099 on 538 degrees of freedom
## AIC: 454.45099
##
## Number of Fisher Scoring iterations: 5
confint(SCWTlog3)
## 2.5 % 97.5 %
## (Intercept) -1.933308023460 2.99882242367
## StroopMixed 0.002187171733 0.09153844610
## SexMen -1.446357086521 2.18326043599
## PovStatBelow -2.110003806914 3.15042319088
## Age -0.047217544575 0.01196513381
## WRATtotal -0.017581383972 0.05460430761
## StroopMixed:SexMen -0.083643506742 0.02999360088
## StroopMixed:PovStatBelow -0.096984596868 0.07394835759
## SexMen:PovStatBelow -3.603769498763 3.36101186498
## StroopMixed:SexMen:PovStatBelow -0.098817960598 0.13091968462
exp(cbind(OR = coef(SCWTlog3), confint(SCWTlog3)))
## OR 2.5 % 97.5 %
## (Intercept) 1.6799778869 0.14466883814 20.061898591
## StroopMixed 1.0467371989 1.00218956534 1.095858907
## SexMen 1.4430962027 0.23542636572 8.875196145
## PovStatBelow 1.6066417667 0.12123750489 23.345942281
## Age 0.9826880221 0.95387986363 1.012037002
## WRATtotal 1.0191218006 0.98257226678 1.056122632
## StroopMixed:SexMen 0.9739945380 0.91975908557 1.030447940
## StroopMixed:PovStatBelow 0.9869980721 0.90756998545 1.076751198
## SexMen:PovStatBelow 0.8798572017 0.02722091959 28.818336396
## StroopMixed:SexMen:PovStatBelow 1.0168376491 0.90590760389 1.139876228
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
## -0.5817233 -0.5062837 -0.4888048 -0.4694833 2.1973758
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -2.39524495 0.45602129 -5.25248 1.5006e-07
## StroopMixed 0.01067228 0.01296188 0.82336 0.4103
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 391.01831 on 547 degrees of freedom
## Residual deviance: 390.34029 on 546 degrees of freedom
## AIC: 394.34029
##
## Number of Fisher Scoring iterations: 4
confint(SCWTlog1)
## 2.5 % 97.5 %
## (Intercept) -3.31626203628 -1.525836593
## StroopMixed -0.01475071005 0.036146176
exp(cbind(OR = coef(SCWTlog1), confint(SCWTlog1)))
## OR 2.5 % 97.5 %
## (Intercept) 0.09115034935 0.03628822261 0.2174390728
## StroopMixed 1.01072942912 0.98535754872 1.0368073918
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
## -0.8575227 -0.5252132 -0.4386811 -0.3595507 2.4818343
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.923474630 1.308108704 -1.47042 0.141447
## StroopMixed -0.002239192 0.015750902 -0.14216 0.886951
## Age -0.030718242 0.016569900 -1.85386 0.063759
## SexMen -0.340829462 0.279464016 -1.21958 0.222623
## PovStatBelow 0.670720379 0.289539404 2.31651 0.020531
## WRATtotal 0.028232998 0.022555026 1.25174 0.210665
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 391.01831 on 547 degrees of freedom
## Residual deviance: 376.77391 on 542 degrees of freedom
## AIC: 388.77391
##
## Number of Fisher Scoring iterations: 5
confint(SCWTlog2)
## 2.5 % 97.5 %
## (Intercept) -4.52828769402 0.613429347591
## StroopMixed -0.03313449993 0.028730581884
## Age -0.06373627558 0.001399405771
## SexMen -0.89963957447 0.200617506776
## PovStatBelow 0.09865658575 1.237696041307
## WRATtotal -0.01471822033 0.073934790692
exp(cbind(OR = coef(SCWTlog2), confint(SCWTlog2)))
## OR 2.5 % 97.5 %
## (Intercept) 0.1460984411 0.0107991517 1.846753712
## StroopMixed 0.9977633134 0.9674084345 1.029147286
## Age 0.9697487689 0.9382524070 1.001400385
## SexMen 0.7111801812 0.4067162242 1.222157216
## PovStatBelow 1.9556456191 1.1036872126 3.447661039
## WRATtotal 1.0286353264 0.9853895632 1.076736590
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
## -0.8173694 -0.5563736 -0.4236083 -0.3231578 2.5110847
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -2.78652460 1.43745275 -1.93852 0.052560
## StroopMixed 0.02098870 0.02256839 0.93000 0.352369
## SexMen 1.84015015 1.24297147 1.48044 0.138755
## PovStatBelow 1.24603986 1.35126388 0.92213 0.356461
## Age -0.02923893 0.01666756 -1.75424 0.079389
## WRATtotal 0.02940014 0.02237084 1.31422 0.188773
## StroopMixed:SexMen -0.07247627 0.03616851 -2.00385 0.045086
## StroopMixed:PovStatBelow -0.02214878 0.03822761 -0.57939 0.562325
## SexMen:PovStatBelow -2.03682950 1.95253406 -1.04317 0.296868
## StroopMixed:SexMen:PovStatBelow 0.07915210 0.05825065 1.35882 0.174204
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 391.01831 on 547 degrees of freedom
## Residual deviance: 371.46540 on 538 degrees of freedom
## AIC: 391.4654
##
## Number of Fisher Scoring iterations: 5
confint(SCWTlog3)
## 2.5 % 97.5 %
## (Intercept) -5.66663487145 -0.016991477639
## StroopMixed -0.02299516918 0.065885642183
## SexMen -0.63428161853 4.271705151018
## PovStatBelow -1.46094827835 3.874539035875
## Age -0.06242360679 0.003099514199
## WRATtotal -0.01319397304 0.074756969610
## StroopMixed:SexMen -0.14463656846 -0.002266139709
## StroopMixed:PovStatBelow -0.09735843033 0.053418320310
## SexMen:PovStatBelow -5.89405066129 1.798465501597
## StroopMixed:SexMen:PovStatBelow -0.03451700485 0.194549934874
exp(cbind(OR = coef(SCWTlog3), confint(SCWTlog3)))
## OR 2.5 % 97.5 %
## (Intercept) 0.06163504843 0.003459487330 0.9831520634
## StroopMixed 1.02121051054 0.977267204759 1.0681045641
## SexMen 6.29748377662 0.530316320927 71.6436948441
## PovStatBelow 3.47654803065 0.232016154483 48.1604929141
## Age 0.97118439243 0.939484830335 1.0031043227
## WRATtotal 1.02983658962 0.986892685875 1.0776222241
## StroopMixed:SexMen 0.93008781971 0.865336726632 0.9977364260
## StroopMixed:PovStatBelow 0.97809470109 0.907230768824 1.0548708267
## SexMen:PovStatBelow 0.13044162062 0.002755791281 6.0403714089
## StroopMixed:SexMen:PovStatBelow 1.08236893586 0.966071911637 1.2147641405
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
## -2.2397259 0.4638957 0.5138944 0.5604258 0.7019255
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 1.27530094 0.28686158 4.4457 8.7606e-06
## CVLtca 0.03102604 0.01435262 2.1617 0.030641
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 505.40142 on 640 degrees of freedom
## Residual deviance: 500.72672 on 639 degrees of freedom
## AIC: 504.72672
##
## Number of Fisher Scoring iterations: 4
confint(CVLTlog1)
## 2.5 % 97.5 %
## (Intercept) 0.726387844575 1.85354798148
## CVLtca 0.002905057604 0.05927112174
exp(cbind(OR = coef(CVLTlog1), confint(CVLTlog1)))
## OR 2.5 % 97.5 %
## (Intercept) 3.579778559 2.067598615 6.382424121
## CVLtca 1.031512363 1.002909281 1.061062879
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
## -2.4012837 0.4155605 0.4937615 0.5809894 0.8756141
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 1.69988103 0.96538268 1.76084 0.078266
## CVLtca 0.01946184 0.01610695 1.20829 0.226936
## Age -0.01841877 0.01339162 -1.37540 0.169009
## SexMen -0.37454937 0.24039349 -1.55807 0.119217
## PovStatBelow 0.33198602 0.27314680 1.21541 0.224209
## WRATtotal 0.01786747 0.01537027 1.16247 0.245045
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 505.40142 on 640 degrees of freedom
## Residual deviance: 493.07048 on 635 degrees of freedom
## AIC: 505.07048
##
## Number of Fisher Scoring iterations: 5
confint(CVLTlog2)
## 2.5 % 97.5 %
## (Intercept) -0.17016237008 3.622026456787
## CVLtca -0.01231725862 0.050931166004
## Age -0.04485799931 0.007752946445
## SexMen -0.85096655099 0.094038698909
## PovStatBelow -0.18985492063 0.885330955413
## WRATtotal -0.01277706871 0.047655994449
exp(cbind(OR = coef(CVLTlog2), confint(CVLTlog2)))
## OR 2.5 % 97.5 %
## (Intercept) 5.4732961850 0.8435278418 37.413307505
## CVLtca 1.0196524523 0.9877582883 1.052250460
## Age 0.9817498197 0.9561332438 1.007783078
## SexMen 0.6875990618 0.4270020132 1.098602260
## PovStatBelow 1.3937333672 0.8270791174 2.423786424
## WRATtotal 1.0180280502 0.9873042115 1.048809797
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
## -2.3337143 0.4129433 0.4814082 0.5690541 0.9980326
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 1.507921293 1.041508943 1.44782 0.14767
## CVLtca 0.032916962 0.027323093 1.20473 0.22831
## SexMen -0.490834712 0.717735451 -0.68387 0.49406
## PovStatBelow 1.064530628 1.054751143 1.00927 0.31284
## Age -0.018731017 0.013524879 -1.38493 0.16607
## WRATtotal 0.017975760 0.015500834 1.15966 0.24619
## CVLtca:SexMen 0.002083142 0.035675962 0.05839 0.95344
## CVLtca:PovStatBelow -0.047365862 0.048002417 -0.98674 0.32377
## SexMen:PovStatBelow 0.365349265 1.389996792 0.26284 0.79267
## CVLtca:SexMen:PovStatBelow -0.009220450 0.068860863 -0.13390 0.89348
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 505.40142 on 640 degrees of freedom
## Residual deviance: 490.17679 on 631 degrees of freedom
## AIC: 510.17679
##
## Number of Fisher Scoring iterations: 5
confint(CVLTlog3)
## 2.5 % 97.5 %
## (Intercept) -0.49988121076 3.595142572027
## CVLtca -0.02103624718 0.086641646843
## SexMen -1.93022741877 0.897900681030
## PovStatBelow -0.91709632477 3.263924014249
## Age -0.04543933840 0.007698432371
## WRATtotal -0.01291089144 0.048037701710
## CVLtca:SexMen -0.06781451916 0.072447799611
## CVLtca:PovStatBelow -0.14316688865 0.046005025572
## SexMen:PovStatBelow -2.39711923176 3.102579238154
## CVLtca:SexMen:PovStatBelow -0.14502772106 0.125832049476
exp(cbind(OR = coef(CVLTlog3), confint(CVLTlog3)))
## OR 2.5 % 97.5 %
## (Intercept) 4.5173308195 0.60660271331 36.420892218
## CVLtca 1.0334647188 0.97918347129 1.090505823
## SexMen 0.6121152411 0.14511519281 2.454445036
## PovStatBelow 2.8994777297 0.39967789260 26.151956714
## Age 0.9814433180 0.95557756768 1.007728141
## WRATtotal 1.0181382965 0.98717209659 1.049210212
## CVLtca:SexMen 1.0020853132 0.93443377707 1.075136682
## CVLtca:PovStatBelow 0.9537383968 0.86660942955 1.047079673
## SexMen:PovStatBelow 1.4410172169 0.09097966748 22.255278983
## CVLtca:SexMen:PovStatBelow 0.9908219276 0.86499831411 1.134091681
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
## -0.6067031 -0.5308979 -0.5087078 -0.4873320 2.1694554
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -2.25338404 0.33030215 -6.82219 8.9663e-12
## CVLtca 0.01521485 0.01505192 1.01082 0.3121
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 482.53265 on 640 degrees of freedom
## Residual deviance: 481.50242 on 639 degrees of freedom
## AIC: 485.50242
##
## Number of Fisher Scoring iterations: 4
confint(CVLTlog1)
## 2.5 % 97.5 %
## (Intercept) -2.92543924566 -1.62855333958
## CVLtca -0.01407327415 0.04501824839
exp(cbind(OR = coef(CVLTlog1), confint(CVLTlog1)))
## OR 2.5 % 97.5 %
## (Intercept) 0.105043152 0.05364112511 0.1962132228
## CVLtca 1.015331186 0.98602529145 1.0460469484
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
## -0.8202860 -0.5516455 -0.4716284 -0.3931618 2.3908967
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.369387802 1.063371647 -1.28778 0.197823
## CVLtca 0.003692358 0.016614634 0.22224 0.824131
## Age -0.032616369 0.014098367 -2.31349 0.020696
## SexMen -0.143840415 0.249702346 -0.57605 0.564583
## PovStatBelow 0.494611203 0.254675391 1.94212 0.052122
## WRATtotal 0.016374809 0.017888096 0.91540 0.359980
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 482.53265 on 640 degrees of freedom
## Residual deviance: 469.37622 on 635 degrees of freedom
## AIC: 481.37622
##
## Number of Fisher Scoring iterations: 5
confint(CVLTlog2)
## 2.5 % 97.5 %
## (Intercept) -3.491971077425 0.685248919335
## CVLtca -0.028593810017 0.036641979335
## Age -0.060680847024 -0.005287058702
## SexMen -0.637616291710 0.344169066695
## PovStatBelow -0.009561474772 0.991649369562
## WRATtotal -0.017785200011 0.052499541818
exp(cbind(OR = coef(CVLTlog2), confint(CVLTlog2)))
## OR 2.5 % 97.5 %
## (Intercept) 0.2542625709 0.0304408118 1.9842656962
## CVLtca 1.0036991836 0.9718111243 1.0373215718
## Age 0.9679098083 0.9411235542 0.9947268932
## SexMen 0.8660259417 0.5285508346 1.4108171375
## PovStatBelow 1.6398605427 0.9904840908 2.6956769752
## WRATtotal 1.0165096114 0.9823720232 1.0539020792
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
## -0.8807150 -0.5520694 -0.4698589 -0.3868094 2.3691596
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.56944394 1.17684565 -1.33360 0.182334
## CVLtca 0.01507351 0.02827328 0.53314 0.593939
## SexMen 0.31859214 0.90423281 0.35233 0.724588
## PovStatBelow 1.19627951 0.93802275 1.27532 0.202196
## Age -0.03284666 0.01415330 -2.32078 0.020299
## WRATtotal 0.01566060 0.01801605 0.86926 0.384706
## CVLtca:SexMen -0.02337847 0.04019146 -0.58168 0.560784
## CVLtca:PovStatBelow -0.03455877 0.04090742 -0.84480 0.398220
## SexMen:PovStatBelow -1.59191293 1.38679936 -1.14790 0.251008
## CVLtca:SexMen:PovStatBelow 0.08675023 0.06569447 1.32051 0.186665
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 482.53265 on 640 degrees of freedom
## Residual deviance: 467.48473 on 631 degrees of freedom
## AIC: 487.48473
##
## Number of Fisher Scoring iterations: 5
confint(CVLTlog3)
## 2.5 % 97.5 %
## (Intercept) -3.94186703434 0.682851593602
## CVLtca -0.03911155521 0.072130045296
## SexMen -1.45623020983 2.114702092404
## PovStatBelow -0.64898891305 3.058400756279
## Age -0.06103090000 -0.005420144785
## WRATtotal -0.01877806919 0.052007523899
## CVLtca:SexMen -0.10277124013 0.055227806485
## CVLtca:PovStatBelow -0.11546732555 0.045572506357
## SexMen:PovStatBelow -4.37034665690 1.095833308039
## CVLtca:SexMen:PovStatBelow -0.04073430332 0.217556779457
exp(cbind(OR = coef(CVLTlog3), confint(CVLTlog3)))
## OR 2.5 % 97.5 %
## (Intercept) 0.2081608997 0.01941193814 1.9795144624
## CVLtca 1.0151876931 0.96164342683 1.0747951070
## SexMen 1.3751903263 0.23311340902 8.2871166034
## PovStatBelow 3.3077874136 0.52257387737 21.2934764591
## Age 0.9676869330 0.94079416878 0.9945945177
## WRATtotal 1.0157838727 0.98139714034 1.0533836680
## CVLtca:SexMen 0.9768926876 0.90233336753 1.0567813289
## CVLtca:PovStatBelow 0.9660315676 0.89094968305 1.0466268890
## SexMen:PovStatBelow 0.2035358901 0.01264685569 2.9916746315
## CVLtca:SexMen:PovStatBelow 1.0906242410 0.96008418724 1.2430360064
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
## -2.4382217 0.4087501 0.5131784 0.5736875 0.7124874
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 1.24154095 0.20845829 5.95582 2.5876e-09
## CVLfrl 0.11988490 0.03650983 3.28363 0.0010248
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 505.40142 on 640 degrees of freedom
## Residual deviance: 494.00990 on 639 degrees of freedom
## AIC: 498.0099
##
## Number of Fisher Scoring iterations: 5
confint(CVLTlog1)
## 2.5 % 97.5 %
## (Intercept) 0.84167992958 1.6603765594
## CVLfrl 0.04955221742 0.1929285523
exp(cbind(OR = coef(CVLTlog1), confint(CVLTlog1)))
## OR 2.5 % 97.5 %
## (Intercept) 3.460942491 2.320261581 5.261291660
## CVLfrl 1.127367079 1.050800461 1.212796139
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
## -2.4603786 0.3884587 0.4866815 0.5876138 0.8771856
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 1.48555397 0.95470336 1.55604 0.119699
## CVLfrl 0.09764001 0.03976352 2.45552 0.014068
## Age -0.01359497 0.01351067 -1.00624 0.314300
## SexMen -0.32574514 0.24040730 -1.35497 0.175426
## PovStatBelow 0.34544762 0.27275979 1.26649 0.205338
## WRATtotal 0.01366706 0.01512438 0.90364 0.366184
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 505.40142 on 640 degrees of freedom
## Residual deviance: 488.38073 on 635 degrees of freedom
## AIC: 500.38073
##
## Number of Fisher Scoring iterations: 5
confint(CVLTlog2)
## 2.5 % 97.5 %
## (Intercept) -0.36542427085 3.38494072664
## CVLfrl 0.02029871535 0.17648431810
## Age -0.04025045601 0.01282704691
## SexMen -0.80195753589 0.14310288698
## PovStatBelow -0.17561150180 0.89805025014
## WRATtotal -0.01652091101 0.04294845630
exp(cbind(OR = coef(CVLTlog2), confint(CVLTlog2)))
## OR 2.5 % 97.5 %
## (Intercept) 4.4174118474 0.6939021859 29.516243137
## CVLfrl 1.1025658052 1.0205061353 1.193015718
## Age 0.9864970210 0.9605488338 1.012909666
## SexMen 0.7219891676 0.4484502469 1.153848512
## PovStatBelow 1.4126220974 0.8389438482 2.454812172
## WRATtotal 1.0137608836 0.9836148108 1.043884088
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
## -2.4092127 0.3890613 0.4796106 0.5819100 0.9065641
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 1.594363723 0.995643748 1.60134 0.10930
## CVLfrl 0.081954124 0.064864529 1.26347 0.20642
## SexMen -0.539601167 0.519371314 -1.03895 0.29883
## PovStatBelow 0.034576389 0.728639041 0.04745 0.96215
## Age -0.013555334 0.013552655 -1.00020 0.31721
## WRATtotal 0.014484977 0.015216710 0.95191 0.34114
## CVLfrl:SexMen 0.020872420 0.087236895 0.23926 0.81090
## CVLfrl:PovStatBelow 0.019247162 0.118391029 0.16257 0.87085
## SexMen:PovStatBelow 0.403465918 0.951778351 0.42391 0.67163
## CVLfrl:SexMen:PovStatBelow 0.007242629 0.175255397 0.04133 0.96704
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 505.40142 on 640 degrees of freedom
## Residual deviance: 487.69940 on 631 degrees of freedom
## AIC: 507.6994
##
## Number of Fisher Scoring iterations: 5
confint(CVLTlog3)
## 2.5 % 97.5 %
## (Intercept) -0.33012860190 3.58191680807
## CVLfrl -0.04333937278 0.21245834135
## SexMen -1.58108983304 0.46411695548
## PovStatBelow -1.35788725547 1.53077967330
## Age -0.04029900194 0.01294495147
## WRATtotal -0.01586668780 0.04396448680
## CVLfrl:SexMen -0.15057415018 0.19264864758
## CVLfrl:PovStatBelow -0.21063952488 0.25722476736
## SexMen:PovStatBelow -1.48466749805 2.27285706858
## CVLfrl:SexMen:PovStatBelow -0.33464295343 0.35634529753
exp(cbind(OR = coef(CVLTlog3), confint(CVLTlog3)))
## OR 2.5 % 97.5 %
## (Intercept) 4.9251942884 0.7188312844 35.942369484
## CVLfrl 1.0854060141 0.9575863562 1.236714593
## SexMen 0.5829807177 0.2057507420 1.590608990
## PovStatBelow 1.0351811014 0.2572036088 4.621778895
## Age 0.9865361255 0.9605022042 1.013029100
## WRATtotal 1.0145903926 0.9842585250 1.044945245
## CVLfrl:SexMen 1.0210917729 0.8602139426 1.212456719
## CVLfrl:PovStatBelow 1.0194335823 0.8100660229 1.293335794
## SexMen:PovStatBelow 1.4970042106 0.2265776657 9.707095069
## CVLfrl:SexMen:PovStatBelow 1.0072689206 0.7155935410 1.428100583
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
## -0.6037338 -0.5267193 -0.5088354 -0.4830055 2.1318126
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -2.16353104 0.24189383 -8.94414 <2e-16
## CVLfrl 0.03690981 0.03499808 1.05462 0.2916
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 482.53265 on 640 degrees of freedom
## Residual deviance: 481.42424 on 639 degrees of freedom
## AIC: 485.42424
##
## Number of Fisher Scoring iterations: 4
confint(CVLTlog1)
## 2.5 % 97.5 %
## (Intercept) -2.65528125160 -1.7053017484
## CVLfrl -0.03197076535 0.1055069029
exp(cbind(OR = coef(CVLTlog1), confint(CVLTlog1)))
## OR 2.5 % 97.5 %
## (Intercept) 0.1149186221 0.07027906978 0.1817175449
## CVLfrl 1.0375994331 0.96853489645 1.1112737756
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
## -0.8221971 -0.5504921 -0.4723514 -0.3947454 2.3860246
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.326022308 1.048692746 -1.26445 0.206068
## CVLfrl 0.003212396 0.038749967 0.08290 0.933931
## Age -0.033009090 0.014255224 -2.31558 0.020581
## SexMen -0.151689112 0.248635745 -0.61009 0.541805
## PovStatBelow 0.489546712 0.253615839 1.93027 0.053574
## WRATtotal 0.017180968 0.017742214 0.96837 0.332861
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 482.53265 on 640 degrees of freedom
## Residual deviance: 469.41885 on 635 degrees of freedom
## AIC: 481.41885
##
## Number of Fisher Scoring iterations: 5
confint(CVLTlog2)
## 2.5 % 97.5 %
## (Intercept) -3.41939734084 0.700297484672
## CVLfrl -0.07270854393 0.079504532952
## Age -0.06136405887 -0.005354469107
## SexMen -0.64344727848 0.334149873290
## PovStatBelow -0.01264032142 0.984415505947
## WRATtotal -0.01669413496 0.053017532859
exp(cbind(OR = coef(CVLTlog2), confint(CVLTlog2)))
## OR 2.5 % 97.5 %
## (Intercept) 0.2655313655 0.03273215533 2.0143518571
## CVLfrl 1.0032175615 0.92987180732 1.0827504676
## Age 0.9675297649 0.94048078707 0.9946598405
## SexMen 0.8592553713 0.52547782972 1.3967524638
## PovStatBelow 1.6315764780 0.98743923190 2.6762471766
## WRATtotal 1.0173294095 0.98344443991 1.0544481324
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
## -0.9607832 -0.5455750 -0.4611073 -0.3756312 2.3879734
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.54115885 1.11091043 -1.38729 0.165352
## CVLfrl 0.05846810 0.06310805 0.92648 0.354199
## SexMen 0.16453283 0.66351677 0.24797 0.804157
## PovStatBelow 1.74986865 0.69445905 2.51976 0.011744
## Age -0.03334298 0.01429076 -2.33318 0.019638
## WRATtotal 0.01402218 0.01794100 0.78157 0.434466
## CVLfrl:SexMen -0.04767578 0.09092512 -0.52434 0.600041
## CVLfrl:PovStatBelow -0.21185038 0.10025197 -2.11318 0.034585
## SexMen:PovStatBelow -1.68383287 1.02210873 -1.64741 0.099474
## CVLfrl:SexMen:PovStatBelow 0.30139555 0.15435288 1.95264 0.050862
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 482.53265 on 640 degrees of freedom
## Residual deviance: 463.47961 on 631 degrees of freedom
## AIC: 483.47961
##
## Number of Fisher Scoring iterations: 5
confint(CVLTlog3)
## 2.5 % 97.5 %
## (Intercept) -3.768138753587 0.596098026995
## CVLfrl -0.063914094158 0.184956138930
## SexMen -1.134460896519 1.487606632928
## PovStatBelow 0.394379811850 3.136825837922
## Age -0.061778689335 -0.005627172106
## WRATtotal -0.020308029830 0.050188402641
## CVLfrl:SexMen -0.228410112243 0.129351706763
## CVLfrl:PovStatBelow -0.412916522743 -0.018096869923
## SexMen:PovStatBelow -3.734693588956 0.293286422948
## CVLfrl:SexMen:PovStatBelow 0.001638780937 0.608694441497
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) 0.2655313655 0.02309500881 1.8150227952
## CVLfrl 1.0032175615 0.93808558310 1.2031656668
## SexMen 0.9675297649 0.32159544784 4.4264886189
## PovStatBelow 0.8592553713 1.48346387885 23.0306477069
## Age 1.6315764780 0.94009091592 0.9943886308
## WRATtotal 1.0173294095 0.97989678937 1.0514691773
## CVLfrl:SexMen 0.2655313655 0.79579782647 1.1380903278
## CVLfrl:PovStatBelow 1.0032175615 0.66171751889 0.9820658951
## SexMen:PovStatBelow 0.9675297649 0.02388048719 1.3408267790
## CVLfrl:SexMen:PovStatBelow 0.8592553713 1.00164012447 1.8380301757
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
## -2.3304536 0.4406188 0.5231567 0.5692655 0.6716823
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 1.37417808 0.21529691 6.38271 1.7398e-10
## CVLfrs 0.09091905 0.03594629 2.52930 0.011429
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 505.40142 on 640 degrees of freedom
## Residual deviance: 498.81117 on 639 degrees of freedom
## AIC: 502.81117
##
## Number of Fisher Scoring iterations: 4
confint(CVLTlog1)
## 2.5 % 97.5 %
## (Intercept) 0.96232300602 1.8079209989
## CVLfrs 0.02130091602 0.1624841878
exp(cbind(OR = coef(CVLTlog1), confint(CVLTlog1)))
## OR 2.5 % 97.5 %
## (Intercept) 3.951827287 2.617770512 6.097757002
## CVLfrs 1.095180344 1.021529400 1.176429716
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
## -2.4021833 0.4033347 0.4940124 0.5796325 0.8528028
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 1.68964611 0.95005413 1.77847 0.075326
## CVLfrs 0.06416270 0.03918655 1.63737 0.101554
## Age -0.01655299 0.01349068 -1.22699 0.219825
## SexMen -0.37563476 0.23871864 -1.57355 0.115592
## PovStatBelow 0.32174455 0.27221934 1.18193 0.237233
## WRATtotal 0.01667966 0.01516767 1.09968 0.271469
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 505.40142 on 640 degrees of freedom
## Residual deviance: 491.82259 on 635 degrees of freedom
## AIC: 503.82259
##
## Number of Fisher Scoring iterations: 5
confint(CVLTlog2)
## 2.5 % 97.5 %
## (Intercept) -0.15074294567 3.581577714600
## CVLfrs -0.01239183729 0.141526009626
## Age -0.04317354960 0.009825014526
## SexMen -0.84877073791 0.089687004419
## PovStatBelow -0.19825756499 0.873317859342
## WRATtotal -0.01356016632 0.046079221533
exp(cbind(OR = coef(CVLTlog2), confint(CVLTlog2)))
## OR 2.5 % 97.5 %
## (Intercept) 5.4175631646 0.8600687546 35.930183728
## CVLfrs 1.0662658662 0.9876846254 1.152030468
## Age 0.9835832608 0.9577451593 1.009873438
## SexMen 0.6868531565 0.4279406600 1.093831866
## PovStatBelow 1.3795323255 0.8201585818 2.394843440
## WRATtotal 1.0168195435 0.9865313586 1.047157365
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
## -2.4190888 0.4137243 0.4818654 0.5749832 0.8827030
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 1.746422714 0.985750587 1.77167 0.07645
## CVLfrs 0.061860896 0.064502469 0.95905 0.33754
## SexMen -0.521850136 0.526470105 -0.99122 0.32158
## PovStatBelow 0.451158891 0.777361976 0.58037 0.56166
## Age -0.016645088 0.013563235 -1.22722 0.21974
## WRATtotal 0.017110231 0.015252038 1.12183 0.26193
## CVLfrs:SexMen 0.009236814 0.085199094 0.10841 0.91367
## CVLfrs:PovStatBelow -0.058363553 0.117320841 -0.49747 0.61886
## SexMen:PovStatBelow -0.178900362 1.012604119 -0.17667 0.85976
## CVLfrs:SexMen:PovStatBelow 0.114185917 0.174686238 0.65366 0.51333
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 505.40142 on 640 degrees of freedom
## Residual deviance: 490.65113 on 631 degrees of freedom
## AIC: 510.65113
##
## Number of Fisher Scoring iterations: 5
confint(CVLTlog3)
## 2.5 % 97.5 %
## (Intercept) -0.15644107458 3.717405146660
## CVLfrs -0.06328044999 0.191168570057
## SexMen -1.57745879872 0.496345880517
## PovStatBelow -1.01346250663 2.072883547379
## Age -0.04341787769 0.009867831129
## WRATtotal -0.01327224311 0.046698314106
## CVLfrs:SexMen -0.15853627751 0.176616871533
## CVLfrs:PovStatBelow -0.29008091709 0.173430402243
## SexMen:PovStatBelow -2.20399825572 1.799787039752
## CVLfrs:SexMen:PovStatBelow -0.22596721257 0.462209001394
exp(cbind(OR = coef(CVLTlog3), confint(CVLTlog3)))
## OR 2.5 % 97.5 %
## (Intercept) 5.7340535948 0.8551819082 41.157457860
## CVLfrs 1.0638143539 0.9386801839 1.210663516
## SexMen 0.5934216208 0.2064991880 1.642707640
## PovStatBelow 1.5701307423 0.3629600497 7.947707699
## Age 0.9834926762 0.9575111839 1.009916679
## WRATtotal 1.0172574491 0.9868154447 1.047805853
## CVLfrs:SexMen 1.0092796052 0.8533920043 1.193173867
## CVLfrs:PovStatBelow 0.9433069426 0.7482030227 1.189377906
## SexMen:PovStatBelow 0.8361892112 0.1103610235 6.048359267
## CVLfrs:SexMen:PovStatBelow 1.1209605117 0.7977442572 1.587577074
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
## -0.5612899 -0.5278860 -0.5118361 -0.4962133 2.0950336
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -2.07646923 0.24360711 -8.52384 < 2e-16
## CVLfrs 0.02200585 0.03570988 0.61624 0.53774
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 482.53265 on 640 degrees of freedom
## Residual deviance: 482.15339 on 639 degrees of freedom
## AIC: 486.15339
##
## Number of Fisher Scoring iterations: 4
confint(CVLTlog1)
## 2.5 % 97.5 %
## (Intercept) -2.57185248670 -1.61505511735
## CVLfrs -0.04822673911 0.09204515396
exp(cbind(OR = coef(CVLTlog1), confint(CVLTlog1)))
## OR 2.5 % 97.5 %
## (Intercept) 0.1253720921 0.07639389559 0.1988797084
## CVLfrs 1.0222497643 0.95291769887 1.0964143284
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
## -0.8203658 -0.5498648 -0.4749316 -0.3933093 2.3918406
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.24393413 1.04408203 -1.19141 0.233491
## CVLfrs -0.01331674 0.03922543 -0.33949 0.734239
## Age -0.03453968 0.01420637 -2.43128 0.015046
## SexMen -0.16753684 0.24646838 -0.67975 0.496663
## PovStatBelow 0.48208316 0.25340451 1.90243 0.057116
## WRATtotal 0.01932986 0.01779397 1.08632 0.277340
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 482.53265 on 640 degrees of freedom
## Residual deviance: 469.31056 on 635 degrees of freedom
## AIC: 481.31056
##
## Number of Fisher Scoring iterations: 5
confint(CVLTlog2)
## 2.5 % 97.5 %
## (Intercept) -3.32802792245 0.773449567990
## CVLfrs -0.09020011903 0.063887421165
## Age -0.06282922262 -0.007012407373
## SexMen -0.65525936751 0.313848029748
## PovStatBelow -0.01980691838 0.976408787020
## WRATtotal -0.01464108180 0.055273014563
exp(cbind(OR = coef(CVLTlog2), confint(CVLTlog2)))
## OR 2.5 % 97.5 %
## (Intercept) 0.2882479797 0.03586376149 2.1672293792
## CVLfrs 0.9867715306 0.91374830855 1.0659723861
## Age 0.9660500049 0.93910383769 0.9930121222
## SexMen 0.8457454556 0.51930735366 1.3686817218
## PovStatBelow 1.6194444502 0.98038794992 2.6549047730
## WRATtotal 1.0195178889 0.98546557767 1.0568291050
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
## -1.0298021 -0.5417614 -0.4575791 -0.3739349 2.5096221
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.75276000 1.12367677 -1.55984 0.1187969
## CVLfrs 0.07310074 0.06395628 1.14298 0.2530470
## SexMen 0.42284514 0.67236213 0.62889 0.5294178
## PovStatBelow 2.04484947 0.69989501 2.92165 0.0034818
## Age -0.03436550 0.01426933 -2.40835 0.0160249
## WRATtotal 0.01753688 0.01813839 0.96684 0.3336254
## CVLfrs:SexMen -0.09052511 0.09202949 -0.98365 0.3252860
## CVLfrs:PovStatBelow -0.26602586 0.10347503 -2.57092 0.0101429
## SexMen:PovStatBelow -1.90366889 1.03608084 -1.83737 0.0661546
## CVLfrs:SexMen:PovStatBelow 0.33769661 0.15903837 2.12337 0.0337232
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 482.53265 on 640 degrees of freedom
## Residual deviance: 461.85266 on 631 degrees of freedom
## AIC: 481.85266
##
## Number of Fisher Scoring iterations: 5
confint(CVLTlog3)
## 2.5 % 97.5 %
## (Intercept) -4.00877842469 0.404600037809
## CVLfrs -0.05036067455 0.201952058968
## SexMen -0.89138897002 1.767342468494
## PovStatBelow 0.68639106510 3.450202002130
## Age -0.06278524986 -0.006718410273
## WRATtotal -0.01713924073 0.054128889409
## CVLfrs:SexMen -0.27352408788 0.088581673025
## CVLfrs:PovStatBelow -0.47447559412 -0.066916161439
## SexMen:PovStatBelow -3.98657185600 0.097029190385
## CVLfrs:SexMen:PovStatBelow 0.02909836791 0.654605762089
exp(cbind(OR = coef(CVLTlog3), confint(CVLTlog3)))
## OR 2.5 % 97.5 %
## (Intercept) 0.1732949894 0.01815556008 1.4987029556
## CVLfrs 1.0758389105 0.95088640212 1.2237893370
## SexMen 1.5262979191 0.41008576016 5.8552720966
## PovStatBelow 7.7279951260 1.98653331156 31.5067560978
## Age 0.9662182865 0.93914513359 0.9933041078
## WRATtotal 1.0176915501 0.98300680052 1.0556206517
## CVLfrs:SexMen 0.9134513968 0.76069401263 1.0926234868
## CVLfrs:PovStatBelow 0.7664193102 0.62221126217 0.9352736100
## SexMen:PovStatBelow 0.1490208735 0.01856324263 1.1018925378
## CVLfrs:SexMen:PovStatBelow 1.4017151790 1.02952586180 1.9243837028