Hi Ben,
Thanks for clarifying this, I used a misleading word, "model" the
observation time
sounds as if observation time were the dependent variable - which it is
not, of course,
instead, in the scenario described, the parrot counts are modeled.
Best wishes,
Matthias
Am 30.12.2011 20:50, schrieb Ben Bolker:
Matthias Gondan<matthias-gondan<at> gmx.de> writes:
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:
Your description sounds like you might already have transformed
your predictors: generally speaking, you don't want to do that
before running a GLM (the variance function incorporated in the
GLM takes care of heteroscedasticity, and the link function takes
care of nonlinearity in the response).
I suspect you want total number of birds, number of flushes per day,
and species richness to be modeled as Poisson (or negative binomial --
see ?glm.nb in the MASS package). Species richness *might* be
binomial, or more complicated, if you are drawing from a limited
species pool (e.g. if there are only 5 possible species and you
sometimes see 4 or 5 of them in a day). Is the total number
of birds really non-integer *before* you square-root transform it?
Time devoted to foraging at the site is most easily
modeled as log-normal (unless the response includes zeros:
i.e., log-transform as you have already done and use lm),
or possibly Gamma-distributed (although you may want to
use a log link instead of the default inverse link).
As Matthias said, offsets are used for the specific case of
non-uniform sampling effort (e.g. if you sampled different areas,
or for different lengths of time, every day).
You may be interested in r-sig-ecol...@r-project.org , which
is an R mailing list specifically devoted to ecological questions.
______________________________________________
R-help@r-project.org mailing list
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
______________________________________________
R-help@r-project.org mailing list
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
