Thanks for the advice,
In your publication, it is unclear to me how you tested main effects and
interactions using AIC. It appears to me at initial glance that you main
(gap-effect) and interaction (gap-stage interaction effect) were tested
using pairwise tests. Perhaps from the glht() package? I noticed table 5 in
the appendix using AIC and LRT to test the significance of each factor in
different models, but I dont see how it is incorporated into your results.
Also, after thumbing through Zuur 2009, I dont see AIC used for testing
significance of main and interaction effects, but for validating models.
best,
Colin
On Mon, Aug 29, 2011 at 1:05 AM, Kay Cecil Cichini
<Kay.Cichini at uibk.ac.at>wrote:
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
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