Monte Milanuk wrote: >> I have a reference book that discusses regression model selection >> using several methods - what they call 'Forward Model Selection' i.e. >> add one variable at a time and examining R, R^2, Mallow's C-p value, >> etc., Backward Model Selection' i.e. starting out with all the >> variables included and then remove them one at a time, and examining >> for fit, and finally a 'Best Subsets' procedure, to find which >> combination (forward, backward, or other) gives the best fit. Which method is appropriate depends on whether your primary interest is prediction, parameter estimation, or something else. A thoughtful discussion of methods and objectives is provided by R. R. Hocking, "The Analysis and Selection of Variables in Linear Regression," Biometrics, March 1976, vol 32, no. 1, pp. 1-49.
>> Unfortunately everything is directed at use with >> Minitab, so while I get the general concept behind what they are >> discussing, I'm at somewhat of a loss as to how to do the same sort >> of thing in R. I searched the R-project site and archives for >> 'regression model selection' and got *too much* info... thousands of >> hits. Apparently its either a *very* popular subject or my >> search-foo needs some work ;) >> >> If someone could perhaps point me in the right direction, I'd greatly >> appreciate it. The R package leaps is useful, particularly its regsubsets function. -- John P. Burkett Department of Environmental and Natural Resource Economics and Department of Economics University of Rhode Island Kingston, RI 02881-0808 USA phone (401) 874-9195 ______________________________________________ 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.
