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! -- View this message in context: http://www.nabble.com/model-simplification-using-Crawley-as-a-guide-tp17769044p17769044.html Sent from the R help mailing list archive at Nabble.com. ______________________________________________ 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.