Hi Leena,

Yes, p-values for tests of fixed effects can be found in various ways. See the FAQ here:

http://glmm.wikidot.com/faq

In particular see the sections "What is the best way to test hypotheses on effects in GLMMs?" and "Why doesn't lme4 display denominator degrees of freedom/p values? What other options do I have?"

Note that most of these methods of obtaining p-values will *not* also come with an estimated degrees of freedom. My guess is that just having the p-value will satisfy the editor and the absence of DFs will not be a big deal. But if you decide that you do also want the degrees of freedom, you can use the Kenward-Roger procedure implemented in the pbkrtest package.

Jake

From: leena.hamberg@metla.fi
To: r-sig-mixed-models@r-project.org
Date: Wed, 16 Oct 2013 11:25:18 +0000
Subject: [R-sig-ME] GLMM question

Dear list members,
 
I would like to ask a question relating to generalized linear mixed models. I have used package lme4, function glmer to estimate my models (logit link for occurrences, log for counts and identity for height models). I presented the results of my models in our manuscript (coefficients with SE - significant ones highlighted). However, the editor asked me to add p-values, df:s, and test statistics to the result section every time I am presenting significant or insignificant results. I did as was asked and explained that degrees of freedom were not available for these models and that when normality was assumed (i.e., in the case of t statistics) p-values were not available. However, the editor answered as follows:
 
"In my previous e-mail I've requested you to add details of the statistical results in your MS (e.g., results of the GLM you've done, F-values, Chi2-values, df, P-values, etc.)... ...You did not take this comment fully into account and I disagree with your answer to this request. On the contrary to what you answered me, R (since you used R) provides all the detailed results you are requested to provide...
 
...Also, even if p-values, df and statistics are tightly interrelated, this does to prevent you to give the corresponding information in your published work, at least to help potential readers to verify what you wanted to say. P-values are always available in R - or can easily be found - for Gaussian or not normally distributed traits. So, you have to provide all the needed information is you MS. For example, every single t-test has to come with its df and P-value. If really you are not able to find this in R, then you have to use another program..."
 
 
So how to proceed? Can df:s and p-values be found in any way using the R? If yes, how this can be done? Unfortunately I couldn't solve this problem by myself.
 
Here is an example of GLMMs estimated:
 
tyvivmaxp10P=glmer(Tkvmaxpit~käsittely+tyvilpm+m3haYHT+saastotKAIK+ TKvHirvi+(1|ruutu),family=gaussian(link ="identity"), data=pihlajatE10)
 
summary(tyvivmaxp10P)
 
Linear mixed model fit by REML
Formula: Tkvmaxpit ~ käsittely + tyvilpm + m3haYHT + saastotKAIK + TKvHirvi + (1 | ruutu)
   Data: pihlajatE10
   AIC  BIC logLik deviance REMLdev
994.5 1015 -489.3    999.5   978.5
Random effects:
Groups   Name        Variance Std.Dev.
ruutu    (Intercept)  207.25  14.396
 Residual             1255.73  35.436
Number of obs: 100, groups: ruutu, 8
 
Fixed effects:
             Estimate Std. Error t value
(Intercept)  80.71047   17.52630   4.605
käsittely2  -45.79590   13.98636  -3.274
tyvilpm      22.06164    6.23251   3.540
m3haYHT       0.11672    0.31592   0.369
saastotKAIK   0.09566    0.11513   0.831
TKvHirvi1    12.77524    8.55455   1.493
 
Correlation of Fixed Effects:
            (Intr) ksttl2 tyvlpm m3hYHT ssKAIK
käsittely2  -0.588
tyvilpm     -0.662  0.126
m3haYHT     -0.292  0.223  0.210
saastotKAIK -0.408  0.199 -0.027 -0.192
TKvHirvi1   -0.064 -0.124 -0.127 -0.029  0.006
 
 
 
Kind regards,
 
Leena Hamberg
 
 
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