Hi there, I am trying to fit a generalised linear model to some loan application and default data. The purpose of this is to eventually work out the probability an applicant will default.
However, R seems to crash or die when I run "glm" on anything greater than a 5-way saturated model for my data. My first question: is the best way to fit a generalised linear model in R to fit the saturated model and extract the significant terms only, or to start at the null model and to work up to the optimum one? I am importing a csv file with 3500 rows and 27 columns (3500x27 matrix). My second question: is there anyway to increase the memory I have so R can cope with more analysis? I can send my code if it would help to answer the question. Kind regards, AJC Sent from my BlackBerry smartphone from Virgin Media ______________________________________________ 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.
