Hi,

Use offset variables if count occurrences of an event and you want to 
model the
observation time.

glm(count ~ predictors + offset(log(observation_time)), family=poisson)

If you want to compare durations, look at library(survival), ?coxph

If tnoise_sqrt is the square root of tourist noise, your example seems
incorrect, because it is a predictor, not the dependent variable

tnoise_sqrt ~ lengthfeeding_log

Best wishes,

Matthias


Am 30.12.2011 16:29, schrieb Lucy Dablin:
> Great lists, I always find them useful, thank you to
> everyone who contributes to them.
>
>
> My question is regarding non-integer values from some data I
> collected on parrots when using the poisson GLM. I observed the parrots on a
> daily basis to see if they were affected by tourist presence. My key predictors
> are tourist noise (averaged over a day period so decimal value, square root to
> adjust for skew),  tourist number (the
> number of tourists at a site, square root), and the number of boats passing the
> site in a day (log). These are compared with predictors: total number of birds
> (count data, square root), average time devoted to foraging at site (log), species
> richness (sqrt), and the number of flushes per day. Apart from the last one
> they are all non-integer values. When I run a glm for example:
>
>
> parrots<- glm(tnoise_sqrt ~ lengthfeeding_log, family =
> poisson)
>
> summary(parrots)
>
>
> There are warnings which are "27: In dpois(y, mu, log =
> TRUE) : non-integer x = 1.889822" I was advised to use the offset() function
> however this does not seem to correct the problem and I find the code confusing.
> What GLM approach should I be using for multiple non-integer predictors and
> non-integer responses? Does my GLM approach seem appropriate?
> Thank you for taking the time to consider this.
>
>
>
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>
>
>
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