#### Commands for a logistic regression.

uva.nomiss <- na.omit(uva)  ### Remove missing values.
dim(uva.nomiss) ### check dimensions.
in.sample <- sample(19022,9500) ### Choose the in-sample rows.
out.sample <- c(1:19022)[-in.sample]

attach(uva.nomiss) ### make the data set available.

### The logistic regression command.
glm(Newbie ~ Age, family="binomial",subset = in.sample,data=uva.nomiss)

### Saving the model output to a variable.
glm.out <- glm(Newbie ~ Age, family="binomial",subset = in.sample,data=uva.nomiss)

### predicting new values.
predict.glm(glm.out,uva.nomiss,type=c("response"))

### saving the predicted values
my.preds <- predict.glm(glm.out,uva.nomiss,type=c("response"))

### Recoding them as categories.
my.classes <- as.factor(ifelse(my.preds > .333, 1, 0))

### Calling the original newbie variable a factor
Newbie <- as.factor(Newbie)

### Cross classify: (in sample)
table (Newbie[in.sample],my.classes[in.sample])

### Cross classify: (out of sample)
table (Newbie[out.sample],my.classes[out.sample])