library(car) # for Anova()
data(UCBAdmissions)
UCB.df <- as.data.frame(UCBAdmissions)
berk.mod1 <- glm(Admit == "Admitted" ~ Dept, data = UCB.df, weights = UCB.df$Freq, family = "binomial")
Anova(berk.mod1, test = "Wald")
## Analysis of Deviance Table (Type II tests)
##
## Response: Admit == "Admitted"
## Df Chisq Pr(>Chisq)
## Dept 5 623 <2e-16 ***
## Residuals 18
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Call:
## glm(formula = Admit == "Admitted" ~ Dept, family = "binomial",
## data = UCB.df, weights = UCB.df$Freq)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -25.433 -13.203 -0.028 15.919 21.222
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.5935 0.0684 8.68 <2e-16 ***
## DeptB -0.0506 0.1097 -0.46 0.64
## DeptC -1.2091 0.0973 -12.43 <2e-16 ***
## DeptD -1.2583 0.1015 -12.40 <2e-16 ***
## DeptE -1.6830 0.1173 -14.34 <2e-16 ***
## DeptF -3.2691 0.1671 -19.57 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 6044.3 on 23 degrees of freedom
## Residual deviance: 5189.0 on 18 degrees of freedom
## AIC: 5201
##
## Number of Fisher Scoring iterations: 6
berk.mod2 <- glm(Admit == "Admitted" ~ Dept + Gender, data = UCB.df, weights = UCB.df$Freq,
family = "binomial")
Anova(berk.mod2, test = "Wald")
## Analysis of Deviance Table (Type II tests)
##
## Response: Admit == "Admitted"
## Df Chisq Pr(>Chisq)
## Dept 5 534.71 <2e-16 ***
## Gender 1 1.53 0.22
## Residuals 17
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Call:
## glm(formula = Admit == "Admitted" ~ Dept + Gender, family = "binomial",
## data = UCB.df, weights = UCB.df$Freq)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -25.342 -13.058 -0.163 16.017 21.320
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.5821 0.0690 8.44 <2e-16 ***
## DeptB -0.0434 0.1098 -0.40 0.69
## DeptC -1.2626 0.1066 -11.84 <2e-16 ***
## DeptD -1.2946 0.1058 -12.23 <2e-16 ***
## DeptE -1.7393 0.1261 -13.79 <2e-16 ***
## DeptF -3.3065 0.1700 -19.45 <2e-16 ***
## GenderFemale 0.0999 0.0808 1.24 0.22
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 6044.3 on 23 degrees of freedom
## Residual deviance: 5187.5 on 17 degrees of freedom
## AIC: 5201
##
## Number of Fisher Scoring iterations: 6
library(effects) ## load the effects package
berk.eff2 <- allEffects(berk.mod2)
plot(berk.eff2)
