[R] model simplification using Crawley as a guide

ChCh jmo101 at student.canterbury.ac.nz
Wed Jun 11 04:50:33 CEST 2008


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!

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