Hi Colin,

Try the book from Murray Logan: Biostatistical Design and Analysis Using R
A Practical Guide from 2010. You can download it from his website:

http://users.monash.edu.au/~murray/BDAR/index.html

You'll also find other useful links and packages on this site.

Regards,

Julia



2011/8/28 Colin Wahl <biowahl@gmail.com>

> Hello,
> I am currently writing my master's thesis and would like some advice on how
> to report my glmm results. I am testing how stream macroinvertebrate
> distributions vary between watersheds defined by different types of land
> use, and between stream reaches with and without riparian corridors. I am
> considering using the following glmer output to report my results (the
> actual glmer output is included at the end of this post).
>
>     Treatment
>
> Estimate
>
> St. error
>
> z value
>
> p value (>|z|)
>
> Cultivated(intercept)
>
> 1.35
>
> 0.49
>
> -8.694
>
> <0.001***
>
> Developed
>
> 0.18
>
> 0.76
>
> -2.705
>
> 0.007**
>
> Forested
>
> 28.2
>
> 0.6339
>
> 5.297
>
> <0.001***
>
> Grassland
>
> 28.9
>
> 0.7486
>
> 4.531
>
> <0.001***
>
> Riparia:cultivated
>
> 1.55
>
> 0.6323
>
> 0.225
>
> 0.822
>
> Riparia:developed
>
> 0.29
>
> 0.9682
>
> 0.383
>
> 0.701
>
> Riparia:forested
>
> 16.6
>
> 0.8087
>
> -1.071
>
> 0.284
>
> Riparia:grassland
>
> 1.9
>
> 0.9601
>
> -3.284
>
> 0.001**
>  I am concerned about two things: the confidence of these results, and how
> to report them
>
> These results (treatment estimates, errors and p values [suspect, I know])
> are very much in agreement with very distinct trends in the data.  In
> previous posts I have been directed toward various approaches using mcmc,
> bootstrapping, or profiling to get more accurate estimates of 95%
> confidence
> intervals and accurately determine significant differences. I have
> struggled
> with attempting these approaches but have not been rewarded with much
> success (no local faculty are familiar enough with these types of analyses
> to provide support or assistance). In meetings with my committee we've
> decided that these results are sufficient, given the scope of my project,
> how well they fit distinct trends, how strong significant differences
> (though likely biased) are, and how fresh these advanced approaches are.
>
> This type of output is alien (and understandably discomforting) to everyone
> on my committee and it seems likely it will be to most ecologists and or
> reviewers, who in my experience expect the omnipotent ANOVA table with main
> effects and interactions. While I am comfortable interpreting and
> explaining
> these results, reporting them is a different story.
>
> My questions are:
> How should glmer/lmer results be reported and submitted?
> How presentable would you consider these results and how dangerous is it to
> assume these p values reflect real differences in the data?
> What improvements would you expect for submission to reviewers, considering
> this is coming from an institution whose faculty is unfamiliar with these
> non-traditional approaches (with which general consensus is somewhat
> lacking)?
>
> I would very much like to do this right, but I need to be finished with
> this
> project in 3 months and do not have the time to commit (or, likely, also
> the
> requisite experience) to sufficiently teach myself mcmc, profiling, or even
> the matrix-based framework lme4 uses.
>
> As always, thank you to all the busy people out there who make time to
> help,
> Colin Wahl
> Masters Student
> Western Washington University
> Bellingham, WA
>
>
>
>
>
> glmer output (estimates not back-transformed):
>
> Generalized linear mixed model fit by the Laplace approximation
> Formula: E ~ wsh * rip + (1 | stream) + (1 | stream:rip) + (1 | obs)
>   Data: ept
>   AIC   BIC logLik deviance
>  284.4 309.5 -131.2    262.4
> Random effects:
>  Groups     Name        Variance Std.Dev.
>  obs        (Intercept) 0.30186  0.54942
>  stream:rip (Intercept) 0.40229  0.63427
>  stream     (Intercept) 0.12788  0.35760
> Number of obs: 72, groups: obs, 72; stream:rip, 24; stream, 12
>
> Fixed effects:
>            Estimate Std. Error z value Pr(>|z|)
> (Intercept)  -4.2906     0.4935  -8.694  < 2e-16 ***
> wshd         -2.0557     0.7601  -2.705 0.00684 **
> wshf          3.3575     0.6339   5.297 1.18e-07 ***
> wshg          3.3923     0.7486   4.531 5.86e-06 ***
> ripN          0.1425     0.6323   0.225  0.82165
> wshd:ripN     0.3708     0.9682   0.383  0.70170
> wshf:ripN    -0.8665     0.8087  -1.071  0.28400
> wshg:ripN    -3.1530     0.9601  -3.284  0.00102 **
> ---
> Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
>
> Correlation of Fixed Effects:
>          (Intr) wshd   wshf   wshg   ripN   wshd:N wshf:N
> wshd      -0.649
> wshf      -0.779  0.505
> wshg      -0.659  0.428  0.513
> ripN      -0.644  0.418  0.501  0.424
> wshd:ripN  0.421 -0.672 -0.327 -0.277 -0.653
> wshf:ripN  0.503 -0.327 -0.638 -0.332 -0.782  0.511
> wshg:ripN  0.424 -0.275 -0.330 -0.632 -0.659  0.430  0.515
>
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>
>
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>
>


-- 
Julia Sommerfeld - PhD Candidate
Institute for Marine and Antarctic Studies
University of Tasmania
Private Bag 129, Hobart
TAS 7001

Phone: +61 458 247 348
Email: julia.somma@gmx.de
Julia.Sommerfeld@utas.edu.au

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