# Output from berkeley-logit.R

```library(car)  # for Anova()
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)
##
##           Df Chisq Pr(>Chisq)
## Dept       5   623     <2e-16 ***
## Residuals 18
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
```
```summary(berk.mod1)
```
```##
## Call:
##     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)
##
##           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
```
```summary(berk.mod2)
```
```##
## 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 main effects
plot(berk.eff2)
```
```# plot 'interaction' -- produces a harmless warning
plot(effect("Dept:Gender", berk.mod2), multiline = TRUE)
```
```
```

Generated with `R version 2.15.1 (2012-06-22)` using the R package knitr (version `0.8.4`) on `Wed Sep 26 09:00:24 2012`.