[R] GLMM post- hoc comparisons

[Helios de Rosario]

>>> El día 08/01/2013 a las 12:40, Silvina Velez
<svelez at mendoza-conicet.gob.ar>
escribió:
> Hi All,
> I have data about seed predation (SP) in fruits of three differents
colors 
> (yellow, motted, dark) and in two fruiting seasons (2007, 2008). I
performed 
> a GLMM (lmer function, lme4 package) and the outcome showed that the

> interaction term (color:season) was significant, and some
combinations of 
> this interaction have significant Pr(>|z|), but I don't think they
are the 
> right significant combinations, because when I look the bwplot, this

> combinations seems to be very different from the other ones. So, I
would like 
> to know if there is any test "a posteriori" to know the p-values for
each 
> combination of color:season, and thereby be able to know what
conbination/s 
> is/are really significant.
> 
> m1=lmer(SP ~ color + season:color +(1|Site:tree), data=datosfl, 
> family="poisson")
> AIC   BIC logLik deviance
> 178.3 196.6 -81.14    162.3
> Random effects:
> Groups      Name        Variance Std.Dev.
> obsBR       (Intercept) 0.064324 0.25362 
> Site:tree   (Intercept) 0.266490 0.51623 
> Number of obs: 73, groups: obsBR, 73; Site:tree, 37
> 
>                     Estimate Std. Error z value Pr(>|z|)    
> (Intercept)            2.5089     0.2750   9.125   <2e-16 ***
> colorM                -0.1140     0.3242  -0.352   0.7250    
> colorD                -0.6450     0.4178  -1.544   0.1227    
> Season2008            -0.7343     0.3104  -2.365   0.0180 *  
> colorM:Season2008      0.2505     0.4352   0.576   0.5648    
> colorD:Season2008      1.1445     0.5747   1.992   0.0464 * 

Hi Silvina,

What do you exactly mean with "what combination(s) is/are significant"?
If you mean "what combinations have significantly greater SP than the
baseline combination (yellow:2007)", the table that you have copied may
be what you actually want. If you want to test other contrasts between
color:season combinations, perhaps you can use the function
testInteractions() from package "phia". For instance:

testInteractions(m1)

will give you a test of all the pairwise contrasts between color and
season. You can also test simple main effects, or other specific
contrasts by adding further arguments (see the documentation and the
package vignette). Anyway, the calculation of p-values in mixed models
must always be taken with care.

Helios De Rosario-Martinez
Instituto de Biomecánica de Valencia



INSTITUTO DE BIOMECÁNICA DE VALENCIA
Universidad Politécnica de Valencia • Edificio 9C
Camino de Vera s/n • 46022 VALENCIA (ESPAÑA)
Tel. +34 96 387 91 60 • Fax +34 96 387 91 69
www.ibv.org

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