[R-sig-ME] ANOVA type lll ss table for GLMER?

[Henrik Singmann]

Dear Heather,

you could try to use mixed from the afex package which will give you Type III p-values for the effects via Chi^2 tests (or alternatively via parametric bootstrap):

require(afex)
(spden2 <- mixed(SpDens~(treat*samp)-1+(1|TRANSECT),family=poisson, data=rm, nAGQ = 9, method = "LRT")

Note however, that loading afex changes your overall contrasts, to reset the default contrasts use:
options(contrasts=c('contr.treatment', 'contr.poly'))

Furthermore, (g)lmer doesn't break the factors done by *all* levels. It removes the first levels (usually). Hence the parameters cannot directly be interpreted if this level is "significant".

Hope this helps,
Henrik

Am 14.05.2014 15:45, schrieb Heather Moylett:
>   Hello group,
>
> This is my first time posting, so I hope I have explained my needs clearly
> below.
>
> I am running a repeated measure analysis with a raw species count data set
> (SpDens). I have run different model types (zeroinfl, glm, glmer) and have
> identified glmer to have the best fit. The output generated by GLMER breaks
> my between groups (treat) and within groups (samp) factors down by levels.
> In addition to this, I would like to look at the effect of treat and samp
> overall, something similar to an ANOVA table (Type lll SS). When I use
> Anova(object) I receive an ANOVA table with an F val and no P-vals. I would
> prefer to stick with the z-stat and p-vals. I have seen this reported in
> other papers, so I know it can be done...just can't figure out how to do it!
>
> Components of the model:
> samp: 23 sampling dates is the repeated measure (within groups)
> treat: 4 levels (between groups)
> TRANSECT: experimental unit (subject), 4/treat and data collected from all
> 16 every sampling date
>
> When I run this code:
>
> RM <- read.csv("C:/Users/heatbell/Desktop/Walthour-Moss/STATS/CH
> 1/Final/R/RM.csv")
>
> View(RM)
>
> rm <- subset(RM, SAMPLE >= 2)
>
>
>
> rm<- within(rm, {
>
>    samp<-factor(SAMPLE)
>
>    yr<-factor(YEAR)
>
>    treat<-factor(TREAT)
>
> })
>
> summary(rm)
>
>
> summary(spden<-glmer(SpDens~(treat*samp)-1+(1|TRANSECT),family=poisson,
> data=rm, nAGQ = 9))
>
> Thank you for the help!
> Heather
>

-- 
Dr. Henrik Singmann
Albert-Ludwigs-Universität Freiburg, Germany
http://www.psychologie.uni-freiburg.de/Members/singmann