R help - Dec 2011 - Fwd: Re: Poisson GLM using non-integer response/predictors?

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.
>
>
>
>    		 	   		
> 	[[alternative HTML version deleted]]
>
>
>
> ______________________________________________
> R-help@r-project.org  mailing list
> https://stat.ethz.ch/mailman/listinfo/r-help
> PLEASE do read the posting guidehttp://www.R-project.org/posting-guide.html
> and provide commented, minimal, self-contained, reproducible code.


	[[alternative HTML version deleted]]

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-ecology at r-project.org , which
is an R mailing list specifically devoted to ecological questions.