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

[Seth Bigelow]

Re: the plant count & summits discussion:
 
-I've had good results with glmmadmb & negative binomial distribution,
though the zero-inflation addition has not worked, probably because of a
small dataset.

- Is slope incorporated into the calculation of solar radiation? If so it
might be feasible to drop one of these terms to simplify the model

- There may be a quadratic relationship with elevation and plant count, it
would be wise to test for this (e.g., make models with and without quadratic
elevation term and use <- anova(linearmodel, quadraticmodel) to test.

- the David Atkins et al. tutorial on count regression, often mentioned on
this list, is highly recommended 

- one way of putting the questions would be, 'does the relationship between
plant count and elevation change from the first survey to the second?
Syntax:

glmmadmb(count~sample*elevation + solar.radiation + (1|summit), data=dat,
family="nbinom", zeroInflation=TRUE) 

...and test this vs. the no-interaction model.

--Seth 


-----Original Message-----
From: r-sig-mixed-models-bounces at r-project.org
[mailto:r-sig-mixed-models-bounces at r-project.org] On Behalf Of Tim Howard
Sent: Friday, December 13, 2013 1:03 PM
To: r-sig-mixed-models at r-project.org
Subject: Re: [R-sig-ME] model specification for continuous environmental
vars


>>>> Hans Ekbrand <hans.ekbrand at gmail.com> 12/13/2013 12:42 PM >>>
>On Fri, Dec 13, 2013 at 08:40:55AM -0500, Tim Howard wrote:
>> >From: Hans Ekbrand <hans.ekbrand at gmail.com> 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.
>> >
>> >If you only want to test if there is a difference in plant density 
>> >between yr1 and yr2, then I don't think you should include the 
>> >environmental variables, since difference in the outcome that 
>> >relates to changes in the environmental variables between yr1 and 
>> >yr2 would be attributed to the enviromental variables and "hide" the 
>> >actual differences in outcome between yr1 and yr2.
>> >
>> >mod <- glmer(count ~ samp + (1|summit), data = dat, 
>> >family="poisson")
>> >
>> >would be more appropriate, I think.
>> >
>> >Inclusions of the envirmental variables should only be done if you 
>> >want to explain differences between yr1 and yr2, not for estimating 
>> >their size.
>> These are good points. My reasoning was that I need to control for 
>> these variables somehow. What if I have higher densities in yr2 but 
>> it is completely due to sampling -- that my plots happened to be at 
>> elevations where there are higher densities? I want to remove the 
>> effect of differences in elevation when testing for the differences
between yr1 and yr2.
>
>OK, I thought about real changes between yr1 and yr2, not artifacts due 
>to sampling. elevation and slope does not vary over time, but solar 
>radiation could vary over time, I guess.
>
>If your research question is about changes over time, then why did you 
>change the sampled areas between the measure points?
 
I am following the idea of Probabilistic Survey designs championed by USEPA
(authors are primarily Kincaid and Olsen, randomization method is GRTS using
R package spsurvey). The main idea is that you get an estimate of the target
population through a random sample at T1 and then another estimate of the
population through a separate random sample at T2. 
 
http://www.epa.gov/nheerl/arm/designpages/monitdesign/survey_overview.htm 
 
Perhaps that was a bad idea! :)  But, it actually would have been VERY hard
to visit the exact same locations again and again so this approach does make
sense in this context. 
 
Best,
Tim
 
 

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