[R-sig-ME] (no subject)

[Emily L. De Stigter]

Hello,

I'm just out of undergrad and working in an ecology lab as the leading
statistical investigator for a project studying conifers in Northern
California. I'm using lme4 to do a GLMM. My response variable is binary:
status of the trees (live vs dead). I also have two fixed effects: diameter
at breast height (DBH) and elevation, plus the interaction of the two. My
random effect is plot.

Here are my questions:

   1.

   Do the fixed effects (DBH and elevation) need to be normally
   distributed? Both DBH and elevation are not normally distributed and the
   basic transformations I've tried did not correct the issue.
   2.

   The random effect I have (plot) is not normally distributed as I know
   that it needs to be. I have tried a couple different transformations (log,
   sqrt...) but, again, nothing the corrected the issue. What should I try
   next to fix it?
   3.

   How should I best check that my model is fitting the data appropriately?
   4.

   How do I interpret the deviance of the model?

Here's some of my R code:

PSME stands for Psuedotsuga menziesii, one of the species of interest.

stat<-read.csv("/Users/emilydestigter/Documents/Sawyer/status.csv")

head(stat)

dim(stat)

psme<-glmer(Status~DBH+Elevation+(DBH*Elevation)+(1|Plot),data=stat,family=
"binomial")

summary(psme)

Thanks for reading and let me know if anyone has any follow-up questions. I
appreciate greatly any and all advice I get. I realize these questions are
not exactly related to this forum, so I would also be glad for some
suggestions on resources to check out.

Thanks again,

Emily

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