[R-sig-ME] Advice for reporting results of glmm from lme4

[Kay Cecil Cichini]

Hi Colin,

I faced quite the same problems recently -
I did some exhaustive search and finally came to the solution used in  
the attached publication.

For outputs showing significance of main effects and interaction use  
AIC as given in Zuur et al. (2009) - Mixed Effects Models and  
Extensions in Ecology with R.

For comparison of different level combinations you could use glht() in  
package multcomp.

One last thing: I wonder if the parameterization of your random  
effects is set up properly - from my understanding 1|stream would  
suffice, but it is likely that i didn't get the whole survey design.

Best wishes,
Kay Cichini




  Zitat von Colin Wahl <biowahl at 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
>
> 	[[alternative HTML version deleted]]
>
>

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