 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
-- 
Heather M. C. Moylett
MSc Candidate
Entomology Department
North Carolina State University
Raleigh, NC 27609

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