[EMAIL PROTECTED] wrote: > > I am creating habitat selection models for caribou and other species with > data collected from GPS collars. In my current situation the radio-collars > recorded the locations of 30 caribou every 6 hours. I am then comparing > resources used at caribou locations to random locations using logistic > regression (standard habitat analysis). > > The data is therefore highly autocorrelated and this causes Type I error > two ways – small standard errors around beta-coefficients and > over-paramaterization during model selection. Robust standard errors are > easily calculated by block-bootstrapping the data using “animal” as a > cluster with the Design library, however I haven’t found a satisfactory > solution for model selection. > > A couple options are: > 1. Using QAIC where the deviance is divided by a variance inflation factor > (Burnham & Anderson). However, this VIF can vary greatly depending on the > data set and the set of covariates used in the global model. > 2. Manual forward stepwise regression using both changes in deviance and > robust p-values for the beta-coefficients. > > I have been looking for a solution to this problem for a couple years and > would appreciate any advice. > > Jesse
If you must do non-subject-matter-driven model selection, look at the fastbw function in Design, which will use the cluster bootstrap variance matrix. Frank > > ______________________________________________ > [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 -- Frank E Harrell Jr Professor and Chair School of Medicine Department of Biostatistics Vanderbilt University ______________________________________________ [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
