search for: tnoise_sqrt

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 c...

...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 shoul...