Model with interaction

mod.cowles <- glm(volunteer ~ sex + neuroticism*extraversion, 
    data=Cowles, family=binomial)
summary(mod.cowles)
## 
## Call:
## glm(formula = volunteer ~ sex + neuroticism * extraversion, family = binomial, 
##     data = Cowles)
## 
## Deviance Residuals: 
##    Min      1Q  Median      3Q     Max  
## -1.475  -1.060  -0.893   1.261   1.998  
## 
## Coefficients:
##                          Estimate Std. Error z value Pr(>|z|)    
## (Intercept)              -2.35821    0.50132   -4.70  2.6e-06 ***
## sexmale                  -0.24715    0.11163   -2.21   0.0268 *  
## neuroticism               0.11078    0.03765    2.94   0.0033 ** 
## extraversion              0.16682    0.03772    4.42  9.7e-06 ***
## neuroticism:extraversion -0.00855    0.00293   -2.92   0.0036 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 1933.5  on 1420  degrees of freedom
## Residual deviance: 1897.4  on 1416  degrees of freedom
## AIC: 1907
## 
## Number of Fisher Scoring iterations: 4

Effect plots

eff.cowles <- allEffects(mod.cowles, 
    xlevels=list(neuroticism=seq(0, 24, 6), 
               extraversion=seq(0, 24, 8)))

plot(eff.cowles, 'neuroticism:extraversion', 
     ylab="Prob(Volunteer)",
    ticks=list(at=c(.1, .25, .5, .75, .9)), 
    layout=c(4,1), aspect=1)

plot(eff.cowles, 'neuroticism:extraversion', 
     multiline=TRUE, 
    ylab="Prob(Volunteer)", 
    key.args=list(x = .8, y = .9))

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