[R-sig-ME] Mixed effect model, Hurdle function

[Pin chanratana]

Hi Ben,

Thanks for your response, there is an zero in the TN that I accidentally
made it. Now I fixed it, and now it work fine.

Best,

Ratana

On Fri, Nov 18, 2016 at 9:38 PM, Ben Bolker <bbolker at gmail.com> wrote:

>
>   This isn't really a "mixed effect model" in the standard terminology
> (there's no random effect). Nevertheless ...
>
> On 16-11-17 11:29 PM, Pin chanratana wrote:
> > Hi everyone here,
> >
> > My name's Ratana, and I'm a Msc. student studying conservation ecology.
> >
> > I'm new to the using of mixed effects model. I try to fit the mixed
> effect
> > models include an offset term (which is trapnight)  of my camera-trap
> data
> > by using hurdle function. The following are the model that I fit.
> >
> > m1 <- hurdle(GI ~
> > depth+Ele+dRoad+offset(log(TN))|depth+Ele+offset(log(TN)), data=ndata2,
> > dist="poisson", zero.dist="poisson")
> >
> > m2 <- hurdle(GI ~ depth+Ele+dRoad+offset(TN2)|depth+Ele+offset(TN2),
> > data=ndata2, dist="poisson", zero.dist="poisson")
>
>   I'm unfamiliar with models that use the censored Poisson for their
> hurdle model (binomial is more standard in my experience), but whatever.
>
>
> >
> >
> > GI: Giant ibis
> > depth: depth of waterholes
> > Ele: Elevation
> > dRoad: distance to Road
> > TN: Trap-night
> > TN2: standardize or scale of Trap-night
> >
> > But, I could not fit model m1 and there are error message: Error in
> > glm.fit(X, Y, family = poisson(), weights = weights, offset = offsetx) :
> > NA/NaN/Inf in 'y'
> >
> > Model m2 is work fine and the result look reasonable.
>
>   Your first model looks more correct/standard; log(exposure) is the
> standard offset in a Poisson count model.  (Not as clear what to use as
> an offset for the hurdle; I would actually say that a log-exposure
> offset with a complementary log-log link would actually make the most
> sense for a binomial, but I haven't thought about how that would go
> together with a censored Poisson ...)
>
>   Is it possible that you have some observations in your data with TN=0?
> That would cause the first model to fail. (It wouldn't really make
> sense, but I've seen observational data like this where the person
> taking the data rounded down to zero trap-nights.)
>
>   Could you please send follow-up questions, if any, to
> r-sig-ecology at r-project.org ?
>
> >
> > I would like to ask if anyone know, is the way I do with model 2 is
> correct
> > by standardize the Trap-night that's use for offset in the fitting model?
> > If it's ok to do so, is there any reference (that I can cite) regarding
> to
> > this kind of process ?
> >
> > Regards,
> >
> > Ratana
> >
> >       [[alternative HTML version deleted]]
> >
> > _______________________________________________
> > R-sig-mixed-models at r-project.org mailing list
> > https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
> >
>
> _______________________________________________
> R-sig-mixed-models at r-project.org mailing list
> https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
>

	[[alternative HTML version deleted]]