 
Dear All,
 
I am trying to use a proper regression technique to analyze
an obviously hierarchical problem. I would like to build a model to predict
infection status (binomial, infected vs. not infected) based on a continuous
level 1 variable that measures how many vectors an individual has been exposed
to over a transmission season, and a level 2 variable that measures the
propensity of a vector to bite a particular individual based on what species it
is. The level 1 variable is simply the sum of the vector abundances across the
transmission season from the date of an individuals capture and before until
the beginning of the transmission season.  The level 2 variable is a selection ratio of the vector for a host
species. Specifically, it’s the proportion of blood meals from species i/ the
proportion of species i in the community. As a result, individuals of a given
species have the same selection ratio.  
 
I am particularly interested in the interaction effect
between these two variables.  The
probability of infection should increase with potential exposure to mosquitoes
over the transmission season (level 1 variable), but this increase should be
greater for individuals of preferred species (high selection ratio; level 2
variable).  The reason for the hypothesis
is simple.  If an individual were
generally avoided by vectors based on characteristics of the species, then it
wouldn’t matter how many vectors have been around in the past since many of
those vectors would avoid that host and sought other hosts of preferred
species.   
 
My question concerns the proper structure for the random effects
for testing this interaction. Should I model a random slope and intercept, or
just an intercept?
 
model <-glmer(plas~potential exposure + selection ratio +
potential exposure* selection ratio +  (1|species), family="binomial", data=CMD) 
 
model <-glmer(plas~potential exposure + selection ratio +
potential exposure* selection ratio +  (1+potential exposure|species), family="binomial", data=CMD) 
 
How does the random intercept here affect the interaction
term? I think I understand how they test different things, but I cannot seem to
settle whether these things are redundant.
 
Matthew Medeiros
PhD. student
University of Missouri-St. Louis
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