Dear List, I am looking for a function that will find the best subset of negative binomial models. I have a large data set with 15 variables that I am interested in. I want an easy way to run all possible models and find a subset of the "best" models that I can then look at in more detail. I have found two functions that seem to provide what I am looking for, but am not sure which one (if either) are appropriate.
glmulti() in package glmulti does an exhaustive search of all models and gives a number of candidate models to choose from based on your choice of Information Criterion. This seems to be exactly what I am after, but I found nothing about it on this list which makes me think there is some reason no one is using it. gl1ce() in package lasso2 uses the least absolute shrinkage and selection operator (lasso) to do something. I found it at another thread: http://tolstoy.newcastle.edu.au/R/help/05/03/0121.html I did not understand the paper it was based on, and want to know if it even does what I am interested in before investing a lot of time in trying to understand it. Yes, I have read about the problems with stepwise algorithms and am looking for a valid alternative to narrowing down models when you have a lot of data and a large number of variables your interested in. Any thoughts on either of these methods? Or should I be doing something else? Thanks for your help, Tim Tim Clark Department of Zoology University of Hawaii ______________________________________________ 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.
