[R] Binomial glms with very small numbers
Patrick Connolly
p.connolly at hortresearch.co.nz
Thu Jan 15 02:54:01 CET 2004
On Wed, 14-Jan-2004 at 05:15PM -0800, Spencer Graves wrote:
|> The advisability of using "glm" with mortality depends not on
|> the size of sample groups but on the assumption of independence:
|> Whether you have 3 individuals per group or 30 or 1, is it
I think we can assume independence. What concerned me more was the
fact that there will be rather a lot of 0s and 1s, corresponding to
-Inf and Inf on the transformed scale. Only half the possible values
(namely, 1 & 2) will be usable in the fitting. On second thoughts,
since the response can be given as a binary, perhaps I was
unnecessarily concerned.
|> plausible to assume that all individuals represented in your
|> data.frame have independent chances of survival give the
|> potentially explanatory variables? If the answer is "yes", then
|> "glm" is appropriate. If the answer is "no", then some other tool
|> may be preferable. However, "glm" is quick and easy in R, and I
|> might start with that, even if I felt the assumption of
|> independence was violated. If I found nothing there, I would not
|> likely find anything with techniques that handled more
|> appropriately the violations of independence.
Thanks for that suggestion.
|>
|> Similarly, I can't see how it would matter whether potentially
|> explanatory variables were continuous or categorical, as long as a
|> categorical variable were appropriately coded as a factor (or
|> "character", which is then treated as a factor) if it has more than 2
|> levels.
I didn't think it would make a difference but I included it in case
someone more knowledgeable had reasons why it did.
Thanks.
--
Patrick Connolly
HortResearch
Mt Albert
Auckland
New Zealand
Ph: +64-9 815 4200 x 7188
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