Dear R Ecology Group Members,

I have data on parasite prevalences (coded as 0s or 1s) for several species
of parasites of one host species, and I am interested in seeing if these
parasites can predict health parameters that I measured in the hosts.  I
wanted to tackle this with a multiple regression approach. I used the MASS
package's stepAIC function to first figure out what parasites might be good
predictors, if any.  Code is:

x <- lm(HealthVar ~ Par1 + Par2 + Par3 + Par4 + Par5 + Par6, data= mydata)

 step <- stepAIC(x, direction= "both")

step$anova

The problem with this method is it does not take into account interactions
between parasites.  I have tried rewriting the code to look for
interactions:

x <- lm(HealthVar ~ Par1 * Par2 * Par3 * Par4 * Par5 * Par6, data= mydata)

step <- stepAIC(x, direction= "both")
step$anova

The resulting models from this code, however, don't make much sense (lots
and lots of terms, and many two, and three way interactions).  I would try
to code for interactions manually, but I have no a prior predictions about
which parasites might be interacting, nor do I have any sense about what
parasites might be making hosts sick.  It just seems reasonable to assume
that there may be interactions between parasites, even if I don't know
which ones would be involved.

Any thoughts on how to attack a dataset like this would be much appreciated.

Thanks so much!!

Vincenzo

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