On Wed, Jun 11, 2008 at 6:42 AM, Frank E Harrell Jr <[EMAIL PROTECTED]> wrote: > ChCh wrote: >> >> Hello, >> >> I have consciously avoided using step() for model simplification in favour >> of manually updating the model by removing non-significant terms one at a >> time. I'm using The R Book by M.J. Crawley as a guide. It comes as no >> surprise that my analysis does proceed as smoothly as does Crawley's and >> being a beginner, I'm struggling with what to do next. >> I have a model: >> >> lm(y~A * B * C) >> >> where A is a categorical variable with three levels and B and C are >> continuous covariates. >> >> Following Crawley, I execute the model, then use summary.aov() to identify >> non-significant terms. I begin deleting non-significant interaction terms >> one at a time (using update). After each update() statement, I use >> anova(modelOld,modelNew) to contrast the previous model with the updated >> one. After removing all the interaction terms, I'm left with: >> >> lm(y~ A + B + C) >> >> again, using summary.aov() I identify A to be non-significant, so I remove >> it, leaving: >> >> lm(y~B + C) both of which are continuous variables >> >> Does it still make sense to use summary.aov() or should I use summary.lm() >> instead? Has the analysis switched from an ANCOVA to a regression? Both >> give different results so I'm uncertain which summary to accept. >> >> Any help would be appreciated! >> >> > > What is the theoretical basis for removing insignificant terms? How will > you compensate for this in the final analysis (e.g., how do you unbias your > estimate of sigma squared)?
And in a similar vein, where are your exploratory graphics? How do you know that there is a linear relationship between your response and your predictors? Are the distributional assumptions you are making appropriate? Hadley -- http://had.co.nz/ ______________________________________________ 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.
