 
Dear All,
 
I am working with a large dataset that deals with Plasmodium
infection status of individually sampled birds. Each record is also associated
with individual traits such as the species of the individual bird (16 levels),
the age (2 levels), year at the time of sampling (2 levels) and period at the
time of sampling (broken down into 12 2-week periods across the transmission
season). 
 
I am currently trying to use this dataset to explain
variation in the prevalence of Plasmodium parasites across host species.  Host species in the same community can have
very disparate prevalence levels.  I
would like to test a simple hypothesis, namely that variation in Plasmodium
prevalence is related to mosquito feeding preferences between potential host
species.  In other words, species of
birds that are bit by these vectors more often should higher Plasmodium
prevalence.  I have information on
mosquito feeding preferences measured as Manly selection ratio indices for each
host species (selection ratio = (proportion of mosquito bloodmeals of host
species i)/(proportion of host species i in the host community), basically
use/availability). At some level, I assume this index is proportional to the
amount of successful mosquito bites an individual of host species i receives. 
 
My difficulty in this analysis lies in the hierarchical
nature of the data.  Infection status and
other predictors that likely influence infection status (age, period, year)
apply to the level of the individual bird sampled (Level 1). However, the
selection ratio (the main predictor of interest in this analysis) applies to a
Level 2 group, namely the host species. I have limited experience with mixed
models, but I do understand that that they can be used to handle this
hierarchical structure in the data (and avoid having to average up Level 1 data
to answer a Level 2 problem). I am confused, however about the
implementation.  Would an appropriate
model to include all effects look something like this:
 
m1<-glmer(CMD$plas~1+CMD$yr+CMD$age+CMD$MFI.cx+(CMD$yr|CMD$spe)+(CMD$age|CMD$spe),
family=binomial) 
 
where plas is
infection status, yr is a 2-level-factor (2006/7), age is a 2 level-factor
(AHY/HY), and MFI.cx is the selection ratio (continuous) measured at the
species (spe) level.  Realize,
this is largely a shot in the dark with GLMM. Any insights would be helpful.
 
Thanks immensely for your time,
Matthew Medeiros
University of Missouri-St. Louis
PhD candidate
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