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)?
--
Frank E Harrell Jr Professor and Chair School of Medicine
Department of Biostatistics Vanderbilt University
______________________________________________
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.
