GLM: Classification problem. Help!

What am I missing?

A trip to the help page. predict.glm has details on the "type"
argument specific to this situation:

"the type of prediction required. The default is on the scale of the
linear predictors; the alternative "response" is on the scale of the
response variable. Thus for a default binomial model the default
predictions are of log-odds (probabilities on logit scale) and type =
"response" gives the predicted probabilities. The "terms" option
returns a matrix giving the fitted values of each term in the model
formula on the linear predictor scale."

You will have to do the conversion to the class estimate.

Max

Hello,
I need help with this. Let's say that I have n features that I want  
to use
to predict which class an observation belongs to. Using training  
data I try
to do the following:

training$result <- as.factor(training$result)
model <- glm(result ~., family=binomial("logit"), data = training)

However, when I run the model on my test data I receive predictions  
that
have continuous values. I.e. if I have the classes 0 and 1 in  
"results" I
get predictions of 0.234235 and so on.
How do I force the output to be just 0 or 1? What am I missing?

The fact that predict gives you probabilities? If you want a decision,  
then you need to specify a decision rule, i.e. a threshold.

David Winsemius, MD
Heritage Laboratories
West Hartford, CT