On 18-Feb-10 18:58:57, Dimitri Liakhovitski wrote:
> 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
Yes, they are predicted probabilities of response Z=1.
You specified this by setting 'type="response"'.
See ?predict.glm (the method for 'predict' which is used for GLMs).
Ted.
--------------------------------------------------------------------
E-Mail: (Ted Harding) <ted.hard...@manchester.ac.uk>
Fax-to-email: +44 (0)870 094 0861
Date: 18-Feb-10 Time: 19:38:54
------------------------------ XFMail ------------------------------
______________________________________________
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.
