# [R-sig-ME] Question on significancy of terms

Emmanuel Curis curis at pharmacie.univ-paris5.fr
Wed Sep 12 22:19:39 CEST 2012

```Hello Leonel,

I think your problem is not specific of GLMM. The (fixed-part) models
a*b = a+b+a:b and b+a:b will lead to similar results, but expressed
with a different set of coefficients.

Assume a and b are two-levels factors (a1,a2 and b1,b2) for sake of
simplicity : the first model will have coefficients
- associated to a   : for a2
- associated to   b : for b2
- associated to a:b : for a2:b2

The second model will have coefficients
- associated to   b : for b2
- associated to a:b : for a1:b2 and a2:b2

(you can check this also with the simple lm function, by the way).

So they have the same number of coefficients and, I think, express the
same linear relationship but in a different basis.

So the likelyhood will be the same...

I never tested that with GLMM, but I guess it is the same problem as
with lm and lmer...

Best regards,

On Tue, Sep 11, 2012 at 08:29:03PM +0100, Leonel Lopez wrote:
« Hi lme4 users:
« I am new to mixed models in R and started using lme4 and apart of all I?m not an statistician.
« I am developing a GLMM model like the one below which contains the independent effect and interaction of two factors, plus the repeated measurement effect on individuals.
« Y~a*b+(1/Ind)I am using theglmer( function, family=poisson.?This model has three terms (a+b+a:ab). If I want to test the significance of the three terms I am using the?likelihood ratio test (anova) comparing the model?with the term and without the term
« Let say for example:
« m1<-glmer(y~a*b+(1/ind), family=poisson)
« m2<-glmer(y~a+b+(1/ind),?family=poisson)
« anova(m1,m2)
« but, how to test the significancy of only "a" or only "b"
« For example to test a:
«
« m1<-glmer(y~a*b+(1/ind),?family=poisson)
« m3<-glmer(y~b+a:b+(1/ind),?family=poisson)
« anova(m1,m3)
«
« This seems to be a incorrect model development as the LRT gave exacttly the same values for the two models.
«
«
« I am only considering transform my variable, get a normal distribution and conduct the analysis with the lmer() function.
«
« I?ll be very grateful for your help!!
«
« Cheers
«
« Leo
« 	[[alternative HTML version deleted]]
«

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--
Emmanuel CURIS
emmanuel.curis at univ-paris5.fr

Page WWW: http://emmanuel.curis.online.fr/index.html

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