Abigail Clifton <abigailclifton <at> me.com> writes: > I am wanting to find a good predictive model, yes. It's part of a > project so if I have time after finding the model I may want to find > some patterns but it's not a priority. I just want the model for now > (I need the coefficients above all).
> It's all categorical data, I categorised any continuous data before > I started trying to fit the glm. That's not necessarily a good idea (categorising often loses power relative to fitting something like an additive model), but OK. > I was unsure of how to get the csv file to you,however, I have > uploaded it and it should be available for download from here: > http://www.filedropper.com/prepareddata Here's how far I got: Prepared_Data <- na.omit(read.csv("Prepared_Data.csv", header=TRUE)) pd <- Prepared_Data[,-3] ## data minus response variable ## how many levels per variable? lev <- sapply(pd,function(x) length(unique(x))) ## total parameters for n variables par(las=1,bty="l") plot(cumprod(lev),log="y") library(Matrix) m <- sparse.model.matrix(~.^2,data=pd) ## slower than model.matrix ncol(m) ##8352 columns (!!) library(glmnet) g1 <- glmnet(m,Prepared_Data$C3, family="binomial") This doesn't appear to work properly, yet (I get funny values), but it's the direction I would go ... ______________________________________________ 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.
