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

Geoff Brookshire broog731 at newschool.edu
Thu Sep 13 05:24:23 CEST 2012

Hi there,

I asked a similar question about a year ago:

To test for the significance of a, you could either use pvals.mcmc (in
the languageR package) to get an MCMC p-value, or do Wald chi-square
tests of model fits without the interaction term like so:

m1 <- glmer(y ~ a + b + (1|ind), family=poisson)
m2 <- glmer(y ~ a + (1|ind), family=poisson)
anova(m1, m2)

(Make sure you use the pipe character | for random effects.)


On Tue, Sep 11, 2012 at 3:29 PM, Leonel Lopez <llopezt2004 at yahoo.co.uk> 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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