how-to identify redundant predictors

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