Dear gurus, I've analyzed a (fake) data set ("data") using logistic regression (glm):
logreg1 <- glm(z ~ x1 + x2 + y, data=data, family=binomial("logit"), na.action=na.pass) Then, I created a data frame with 2 fixed levels (0 and 1) for each predictor: attach(data) x1<-c(0,1) x2<-c(0,1) y<-c(0,1) newdata1<-data.frame(expand.grid(x1,x2,y)) names(newdata1)<-c("x1","x2","y") Finally, I calculated model-predicted probabilities for each combination of those fixed levels: newdata1$predicted <-predict(logreg1,newdata=newdata1, type="response") I am pretty sure the results I get (see the table below) are actual probabilities. But just in case - could someone please confirm that these are probabilities rather than log odds or odds? Thanks a lot! x1 x2 y predicted 1 0 0 0 0.08700468 2 1 0 0 0.19262901 3 0 1 0 0.27108334 4 1 1 0 0.48216220 5 0 0 1 0.53686154 6 1 0 1 0.74373367 7 0 1 1 0.81896484 8 1 1 1 0.91887072 -- Dimitri Liakhovitski dimitri.liakhovit...@ninah.com ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.