Post hoc test for GLM with poisson distribution
On May 14, 2013, at 21:04 , Bel Braz wrote:
Hi R-people,
I performed controlled experiments to evaluated the seeds germination of
two palms under four levels of water treatments. I conducted a generalized
linear model (GLM) with a Poisson distribution to verify whether there were
significant differences in the number of seed germination (NS-count
variable) between treatments and species (explanatory variables). Thus, my
model and output were:
model1<-glm(NS~Treatments*Species, family="poisson")
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 2.56247 0.57544 4.453 8.46e-06 ***
Treatments -2.07267 0.35065 -5.911 3.40e-09 ***
Species -0.00312 0.30527 -0.010 0.992
Treatments:Species 0.90397 0.17896 5.051 4.39e-07 ***
Null deviance: 379.870 on 98 degrees of freedom
Residual deviance: 68.302 on 95 degrees of freedom
There is a significant interaction between treatments:species. Which is the
post hoc test appropriate for this model?
There's not much post hoc testing to do if the effect is described by a single coefficient. Did you forget to code Treatments as a factor variable? Apart from that, it depends on what you want to do. Do you want to know where the interaction comes from, or just within which treatment(s) there is a species effect? Since there is only two species, the easiest way forward is to compute the four species effects, one for each treatment. You can then compare the effects pairwise (6 comparisons) or compare each effect to zero (4 comparison). I don't think you can do much better than simple Bonferroni corrections in either case.
Thanks, Maria Isabel [[alternative HTML version deleted]]
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