[R-sig-ME] Question on lmer use and commands

[Ken Beath]

On 10/06/2008, at 7:35 AM, Peter S Coates wrote:

> To R users,
>
> I am new to R and would appreciate any suggestions to my questions.  
> I am
> modeling factors that influence point counts. My question is  
> regarding the
> use and command lines of lmer. First, I have repeated measures per  
> site
> (10-20 per site),  multiple years, and multiple spatial scales of each
> factor. The counts by day are different within each site but the
> explanatory data are the same and therefore do not vary (e.g., %  
> cover). I
> have few sites with no overlap, even at the largest scales. I  
> followed an
> example of mixed model use with longitudinal data from Faraway 2005  
> and
> modeled site and year as random effects. Example of my formula was:
> log(response) ~ hab1 + hab2 + (1 | year) + (1 | site). The models
> converged, no warnings, etc. Was this an appropriate model for these  
> data?
> Also, do you suggest including a time component (e.g., timing of  
> surveys
> partitioned into grouped days) , even though I am not necessarily
> interested in timing effects? If so, is it correct to model it as
> (time|site) instead of (1|time) and does time need to be additionally
> included as a fixed effect? Thank you very much for suggestions.
>

You haven't fully described your model. Usually for this data it would  
be either a Poisson or overdispersed Poisson, set using the family  
parameter, then the log transform of the response isn't required.

I would also be worried about the relatively small value of the hab2  
parameter estimate. This usually means that something is setup  
wrongly. Including the commands used and a few lines of data helps.

Ken


> Models and output are:
>
> Without including time (date of survey):
>
> r mixed-effects model fit by REML
> Formula: log(response) ~ hab1 + hab2 + (1 | year) + (1 | site)
>   Data: data
> AIC   BIC logLik MLdeviance REMLdeviance
> 177 192.1  -83.5      148.1          167
> Random effects:
> Groups   Name        Variance  Std.Dev.
> site     (Intercept) 0.1378352 0.371262
> year     (Intercept) 0.0023579 0.048558
> Residual             0.1180495 0.343583
>
>
> Fixed effects:
>              Estimate Std. Error t value
> (Intercept)  8.666e+00  1.031e+00   8.402
> hab1        -4.213e+00  1.194e+00  -3.527
> hab2        -3.100e-05  4.463e-05  -0.695
>
> Correlation of Fixed Effects:
>     (Intr) hab1
> hab1 -0.992
> hab2 -0.229  0.149
>
> With time (no time fixed effect):
>
> r mixed-effects model fit by REML
> Formula: log(response) ~ hab1 + hab2 + (1 | year) + (time | site)
>   Data: data
>   AIC   BIC logLik MLdeviance REMLdeviance
> 168.1 189.2 -77.07      134.9        154.1
> Random effects:
> Groups   Name        Variance   Std.Dev. Corr
> site     (Intercept) 0.16295061 0.403671
>          time        0.02118839 0.145562 -0.523
> year     (Intercept) 0.00055594 0.023578
> Residual             0.09299733 0.304955
>
>
> Fixed effects:
>              Estimate Std. Error t value
> (Intercept)  8.079e+00  1.006e+00   8.028
> hab1        -3.643e+00  1.162e+00  -3.136
> hab2        -2.391e-05  4.331e-05  -0.552
>
> Correlation of Fixed Effects:
>     (Intr) hab1
> hab1 -0.993
> hab2 -0.243  0.163
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