Look at ?predict.glm mydata.glm <- glm(formula = Death ~ Temperature, family = binomial(link = "logit"), data = mydata)
and see that predict(mydata.glm, type="response") gives the predictions on the probability scale. On Mon, May 28, 2012 at 10:16 AM, eddie smith <eddie...@gmail.com> wrote: > Hello everyone, > > I tried to understand the relationship between temperature and the > death of an organism by using logistic regression. > glm(formula = Death ~ Temperature, family = binomial(link = "logit"), > data = mydata) > > Coefficients: > Estimate Std. Error z value Pr(>|z|) > (Intercept) -87.9161 7.7987 -11.27 <2e-16 *** > Temperature 2.9532 0.2616 11.29 <2e-16 *** > > >From the above summary, I could understand that log odds of death = > -87.9161 + 2.9532*Temperature. Odds=exp(log[odds]). Probability = > odds/(1+odds) > > Assuming my data is randomly normal distributed with (u=0, standard > deviation=0.35), and I want to run it for n=10,000, how do I get to > probability from log odds? > > Regards, > Eddie > > ______________________________________________ > 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<http://www.r-project.org/posting-guide.html> > and provide commented, minimal, self-contained, reproducible code. > [[alternative HTML version deleted]] ______________________________________________ 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.
