Hi,
I have three years of species presence/absence data for a set of ~100
ponds, with a list of associated habitat characteristics (park, vegetation,
hydroperiod, etc.). The datasets differ slightly by year because some ponds
were dry in some years and not in others. My goal is to look at habitat
associations for a couple focal species.
For each year, I have a data table that looks like (an expanded version of)
this:
pond park hydroperiod elevation veg.cover fish odonate beetle
bullfrog newt tiger.sal . . . etc.
1 DV perennial 536 25 1 1
1 1 0 0
2 DV perennial 722 0 0 1
1 0 1 1
3 SN perennial 643 30 0 1
1 0 1 0
4 SN seasonal 1238 0 1 1
1 0 0 0
5 SN seasonal 2406 90 0 1 1
1 0 1
.
.
.
etc.
So far I've analyzed each year individually using (example for 2004):
newts2004 <- glm(newts ~ ., data=ponds2004, family="binomial")
step(newts2004)
I'd like to do a holistic analysis for all three years but am unclear how to
do logistic regression with repeated measures in R. Also not sure if
missing data (from dry ponds) will be a problem.
Thanks very much for any advice.
Cheers, Maureen
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