V&R describes binomial GLMs with mortality out of 20 budworms. Is it appropriate to use the same approach with mortality out of numbers as low as 3? I feel reticent to do so with data that is not very continuous. There are one continuous and one categorical independent variables. Would it be more appropriate to treat the response as an ordered factor with four levels? If so, what family would one use? TIA -- Patrick Connolly HortResearch Mt Albert Auckland New Zealand Ph: +64-9 815 4200 x 7188 ~.~.~.~.~.~.~.~.~.~.~.~.~.~.~.~.~.~.~.~.~.~.~.~.~.~.~.~.~.~.~.~.~.~.~.~ I have the world`s largest collection of seashells. I keep it on all the beaches of the world ... Perhaps you`ve seen it. ---Steven Wright ~.~.~.~.~.~.~.~.~.~.~.~.~.~.~.~.~.~.~.~.~.~.~.~.~.~.~.~.~.~.~.~.~.~.~.~
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 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.
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.
Hope this helps.
spencer graves
Patrick Connolly wrote:
>V&R describes binomial GLMs with mortality out of 20 budworms.
>
>Is it appropriate to use the same approach with mortality out of
>numbers as low as 3? I feel reticent to do so with data that is not
>very continuous. There are one continuous and one categorical
>independent variables.
>
>Would it be more appropriate to treat the response as an ordered
>factor with four levels? If so, what family would one use?
>
>TIA
>
>
>
V&R has a binomial glm with binary data, in fact, (e.g. the birth weight data) and this is quite usual. Going to large numbers of trials for each probability is really only important if you plan to use the absolute residual deviance as a test of fit. With binary data the distribution of the deviance is a can of worms, but the distribution of the difference in deviances for two fixed models is usually still well approximated by the appropriate chi-squared distribution. Note that for that particular example in V&R they discuss other possible methods of dealing with it. That's probably a good idea for lots of things. V. -----Original Message----- From: r-help-bounces at stat.math.ethz.ch [mailto:r-help-bounces at stat.math.ethz.ch] On Behalf Of Patrick Connolly Sent: Thursday, 15 January 2004 9:28 AM To: R-help Subject: [R] Binomial glms with very small numbers V&R describes binomial GLMs with mortality out of 20 budworms. Is it appropriate to use the same approach with mortality out of numbers as low as 3? I feel reticent to do so with data that is not very continuous. There are one continuous and one categorical independent variables. Would it be more appropriate to treat the response as an ordered factor with four levels? If so, what family would one use? TIA -- Patrick Connolly HortResearch Mt Albert Auckland New Zealand Ph: +64-9 815 4200 x 7188 ~.~.~.~.~.~.~.~.~.~.~.~.~.~.~.~.~.~.~.~.~.~.~.~.~.~.~.~.~.~.~.~.~.~.~.~ I have the world`s largest collection of seashells. I keep it on all the beaches of the world ... Perhaps you`ve seen it. ---Steven Wright ~.~.~.~.~.~.~.~.~.~.~.~.~.~.~.~.~.~.~.~.~.~.~.~.~.~.~.~.~.~.~.~.~.~.~.~ ______________________________________________ R-help at stat.math.ethz.ch mailing list https://www.stat.math.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html