how-to identify redundant predictors
On Sat, Apr 21, 2012 at 12:02 PM, C Hess <13184 at stud.leuphana.de> wrote:
dear list, my actual task in the process of fitting an lme()-model is to identify and remove redundant predictors before using them as fixed effects. to get an overview and pick a group of final predictors i use the correlation-coefficients cor() and a pca prcomp() trying and testing seems an essential way in the process of model fitting, but maybe there is another way/method to get a list of predictors in a more structured way like: this are the top 5 predictors with the fewest correlation, or something else thanks CH
You have not mentioned how many samples and predictor variables you have, but I think that you can also use PLS (partial least square regression, package "pls"), especially if you have many, possibly correlated, predictor variables, and relatively few samples. The accompanying web-site (http://mevik.net/work/software/pls.html) provides R-code that implements the VIP (variance importance in projection) algorithm that might be useful during variable selection. Cheers, Ivailo
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