Hello, Is there an example out there that shows how to treat each of the predictor variable types when doing logistic regression in R? Something like this:
glm(y~x1+x2+x3+x4, data=mydata, family=binomial(link="logit"), na.action=na.pass) I'm drawing mostly from: http://www.ats.ucla.edu/stat/r/dae/logit.htm ...but there are only two types of variable in the example given. I'm wondering if the answer is that easy or if I have to consider more with different types of variables. It seems like as.factor() is doing a lot of the organization for me. I will need to understand how to perform logistic regression in R on all data types all in the same model (potentially). As it stands, I think I can solve all of my data type issues with: as.factor(x,ordered=T) ...for all discrete ordinal variables as.factor(x, ordered=F) ...for all discrete nominal variables ...and do nothing for everything else. I'm pretty sure its not that simple because of some other posts I've seen, but I haven't seen a post that discusses ALL data types in logistic regression. Here is what I think will work at this point: glm(y ~ **all_other_vars + as.factor(disc_ord_var,ordered=T) + as.factor(disc_nom_var,ordered=F), data=mydata, family=binomial(link="logit"), na.action=na.pass) I'm also looking for any best practices help as well. I'm new'ish to R...and oddly enough I haven't had the pleasure of doing much regression R yet. Regards, Ben [[alternative HTML version deleted]] ______________________________________________ [email protected] 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.
