Dear all,

This is my first post to the list as I have been unsuccessful in getting an answer elsewhere. I am also quite new to R.

I am investigating variation in pollinator visitation rate (number of visits per inflorescence) with treatment and time category as fixed factors. Block is a random factor. Following Zuur et al (2009), I used the number of visits as response variable with the log(number of inflorescences) as offset variable. A poisson model was overdispersed, and therefore I opted for a negative binomial model in lme4, as follows:

model1 = glmer.nb(visits ~ treat + timecat + offset(log(infl)) + (1|block))


I am specifically interested in differences in visitation rates between treatments. I therefore performed a post hoc test:

OPexp1 =  glht(model1,mcp(treat = "Tukey"))
plot(cld(OPexp1))

When I plot these results, I get number of visits on the y axis. But what I want is visitation rate (visits per inflorescence). How do I specifiy that the post hoc test should be performed using visitation rate?

I assume what is happening is that fitted values are currently expressed as $B&L(B $B!_(B V, but how to I specify that they should be expressed as $B&L(B (visits per inflorescence) only? On p 240, Zuur et al (2009) mentions that this is possible, but I have not been able to find an example.

Any advice would be much appreciated.

Kind regards,

Caroli de Waal

PhD Student
University of Stellenbosch, South Africa
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