On Wed, Apr 7, 2010 at 9:25 PM, Eric Scott <[email protected]> wrote:
> Thank you for your reply. The WoodEnergy example helped a lot. I
> understand now that it is inappropriate to make all pairwise comparisons
> with an interaction present and better to make comparisons between levels of
> one factor within a constant level of the second factor. As I understand it,
> the solution in the WoodEnergy example is to produce separate ANOVAs for
> each type of wood and then perform the multiple comparisons between stove
> types within each wood type. This makes a lot of sense. For my data, I'm
> using glm.nb and if I run the model separately for each level of "site," it
> estimates a different theta for each which I immagine is a problem. Is this
> ok, or do I need to find a way to use the coefficients from the full model
> with the interaction to compare levels of clipping within fixed levels of
> site?
>
> -Eric Scott
>
>
The "right" solution is to fit one model and then work with its
coefficients. For this example
the R glht function did not, at the time I wrote the example, have the
option of averaging over
the wood types. It now has "experimental" options for
interaction_average covariate_average
These usually, but not always, do the right thing.
For this example, I prefer the analysis in file HH/demo/MMC.WoodEnergy.s.R
in which one aov model is calculated and the adjustments are made for the
levels of Wood.
That file works in S-Plus, but not in R. As I noted before, I still need to
revise
the WoodEnergy example to use the experimental option in glht to duplicate
the results I
get from S-Plus.
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