How to predict multiclass probabilities from glm logistic regression?

Question

I have 10 datasets with binary and multiclass factors, I used logistic regression with R "glm" which predicts the class probability class,prediction(formula,data,type="response") . How can I get the predicted class instead, like other models give? For example:

df=data.frame(y=c(1,0,0,1),x1=c(1,2,3,4),x2=c(12,13,43,3))
df$y=as.factor(df$y)
testdf=data.frame(y=c(1,1,0,0),x1=c(11,16,65,8),x2=c(3,2,5,0))
testdf$y=as.factor(testdf$y)
model_glm=glm(y~.,data=df,family="binomial")
pred_glm=predict(model_glm,newdata=testdf,type="response")

this will give the probability prediction:

> pred_glm
           1            2            3            4 
2.220446e-16 2.220446e-16 2.220446e-16 2.220446e-16

• However I need the class prediction whether it is 0 or 1. Or the probability predictions in two columns: one for class 1 and the other for class 0?
• And how it can used when I have multi class?

Answer 1

I had some issues doing this with my data a while back. In order to predict you must first create your logistic regression

lgr= glm(y ~ x1 + x2 +x3...x10, data=df1)

then you can predict or play around with your model from there:

new predictions: NOTE df1 and df2 must be the same length (so add NAs if need be)

predicts= predict(lgr, newdata= "df2")

Then to see it and use it for presentations, ect. I would write it to a csv

write.csv(predicts, "K:/filelocation/filename.csv")

EDIT If trying to do classifications based on predictions, you need to do specificity and sensitivity calculations. See: https://stats.stackexchange.com/questions/25389/obtaining-predicted-values-y-1-or-0-from-a-logistic-regression-model-fit/25398#25398?newreg=2f9713d7d60f427d9e123208c39f69f8