[R-sig-ME] model specification for continuous environmental vars

[Tim Howard]

Neil,
Ok. Thank you. I'll check out glmmADMB as well. 
Tim


>Date: Fri, 13 Dec 2013 18:12:26 +0800
>From: Neil Collier <neilandertal at gmail.com>
>To: Hans Ekbrand <hans.ekbrand at gmail.com>
>Cc: r-sig-mixed-models at r-project.org 
>Subject: Re: [R-sig-ME] model specification for continuous
>	environmental vars
>Message-ID:
>	<CAOVghqoreuLSMsaHQpBL8+mUN87qew3A58N-Xvd=YGrnStddHw at mail.gmail.com>
>Content-Type: text/plain
>
>I agree with Hans. You should probably plot your data to check whether
>these random effects terms might improve model fit.
>
>Also, If the data are zero-inflated inter alia check out the glmmADMB
>package.
>
>Cheers,
>
>Neil
>
>
>On Fri, Dec 13, 2013 at 6:03 PM, Hans Ekbrand <hans.ekbrand at gmail.com>wrote:
>
>> On Thu, Dec 12, 2013 at 01:39:04PM -0500, Tim Howard wrote:
>> > List members -
>> > I am learning a lot, quickly, but still have a way to go. I would
>> > greatly appreciate some help with model specification in glmer.
>> > I can't find a good example that parallels what I've got.
>> >
>> > My dataset consists of spatially-balanced random samples of rare
>> > plants within alpine summits. There were two sampling bouts (yr1 and yr2)
>> > with yr2 collected 6 years after yr1. A new set of random plots were
>> > collected at each bout (e.g. new estimate of the population, not repeated
>> > measures). I would like to test the difference in plant density from yr1
>> to yr2, overall.
>> >
>> > These are count data with many zeros, fitting a negative binomial
>> distribution.
>> >
>> > This is what confuses me:  I ALSO have environmental information
>> > that influences density, such as elevation, solar radiation, slope (and
>> more)
>> > I would like to include these variables in the model, but I am not
>> > exactly sure how. Based on my reading, this is what I think I have:
>> >
>> > block random effects: summit
>> > continuous random effects: elev, solrad, slope
>> > fixed effect: time (=samp)
>> > individual random effects to deal with overdisperson: plotID
>> >
>> > I have over 350 plots for each sample bout, spread among 17 summits.
>> >
>> > Given this I think my model is:
>> >
>> > mod <- glmer(count ~ samp + (1|summit) + (1|elev) + (1|solrad) +
>> (1|slope) + (1|pltID),
>> >        data=dat, family="poisson")
>> >
>> > My primary questions:
>> > Is this the appropriate way to handle these environmental variables?
>>
>> >From my limited understanding of these issues, I'd say elev, solrad
>> and slope should be fixed effects - the are universal in the sense
>> that they are defined for every (imaginable) case.
>>
>> If they vary within each summit, you could - in addition to having
>> them as fixed terms - also include them as random slope terms:
>> (elev|summit) + (solrad|summit) + (slope|summit), if you want to
>> explain (some of) the variance between summits.
>>
>> Since you do not have repeated measures of pltID, you can not include
>> a random term for pltID - there is no variance within each value of
>> pltID.
>>
>> I would model like this
>>
>> mod <- glmer(count ~ samp + (1|summit) + elev + solrad + slope, data=dat,
>> family="poisson")
>>
>> And possibly try (but I don't think you have enough data for this)
>>
>> mod <- glmer(count ~ samp + (1|summit) + elev + solrad + slope +
>> (elev|summit) + (solrad|summit) + (slope|summit), data=dat,
>> family="poisson")
>>
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>