How to deal with "non-integer" warning from negative binomial GLM? I am trying to model the mean intensities of parasites affecting a host in R using a negative binomial model. I keep getting 50 or more warnings that say:  

In dpois(y, mu, log = TRUE) : non-integer x = 251.529000 

How can I deal with this? My code looks like this:  
mst.nb = glm.nb(Larvae+Nymphs+Adults~B.type+Month+Season, data=MI.df)

 A: It's a warning, not a fatal error. glm.nb() is expecting counts as your outcome variable, which are integers. Your data are not integers: 251.529.
R is saying "Hmmm... you might want to check this out and make sure it's OK, because it might not look right to be." If my memory is correct, SPSS doesn't give such a warning. 
If you're sure that you're using the right model, even though you don't have integers, ignore it and keep going. 
A: The negative binomial is a distribution for count data, so you really want your response variable to be counts (that is, non-negative whole numbers).  That said, it is appropriate to account for "different sampling efforts" (I don't know exactly what you are referring to, but I get the gist of it).  However, you should not try to do that by dividing your counts by another number.  Instead, you need to use that other number as an offset.  There is a nice discussion on CV of what an offset is here: When to use an offset in a Poisson regression?  My guess is that your model should be something like:
mst.nb = glm.nb(Larvae+Nymphs+Adults ~ B.type+Month+Season + offset(log(num.hosts)), 
                data=MI.df)

A: I'm an ecological parasitologist. The way you should handle this is by cbind-ing the hosts that were parasitised and the ones that were not, and then using a binomial distribution.
Let's say you want to look at parasitised larvae:
you would have n. of larvae that were healthy, and n. that were parasitised.
For example, given Lh and Lp:
parasitizedL=cbind(Lp, Lh) 
hist(parasitized)

I'm guessing you can just use a regular binomial distribution with glm(), and might not need neg.binomial model.
PLarvae1=glm(parasitizedL~B.type+Month+Season, family=binomial,data=MI.df)

Then do stepwise model reduction to see which of your factors significantly effect parasitism: see this link.
However it looks like you need to have random effects to account for repetitive sampling, so likely your random effect will be (1|Season/Month), but it's hard to tell without knowing your data.
