R-devotees, I have a question about modeling in the case where the response variable is binary.
I have a case where I have a response variable that is the probability of success, and four descriptor variables, The response has a sigmoid response with one of the variables. I would like to test for the effect of the various descriptor variables on the percentage success of the binary trait. I have looked at glm with family = "binomial" but am not sure I totally understand its use (and therefore am not sure it is the appropriate test) and am looking for two things: (1) is glm with family = 'binomial' the right way to do this, and (2) are there any good references on how it works. I have posted a plot of a sample of the data I am looking at as well as the sample data used to generate the plots. Sample Plot: http://www.uoregon.edu/~kemerson/tmp/plot.pdf Sample Data: http://www.uoregon.edu/~kemerson/tmp/data.csv Response variable is percent.dev (se2.dev are the errors from binomial estimates given probability and number of samples). Descriptor variables are num.days, ppd, temp, and pop. Any help would be greatly appreciated. Cheers, Kevin Emerson ==================================== Kevin J. Emerson Bradshaw - Holzapfel Lab 1210 University of Oregon Eugene, OR, 97403 email: [EMAIL PROTECTED] web: http://evodevo.uoregon.edu/people/emerson.html ______________________________________________ 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.
