Squirrel data set (nuts) from Zuur, Hilbe, and Ieno (2013). As originally
reported by Flaherty et al (2012), researchers recorded information about
squirrel behavior and forest attributes across various plots in
Scotland's Abernathy Forest. The study focused on the following variables.
response cones number of cones stripped by red squirrels per plot
predictor sntrees standardized number of trees per plot
sheight standardized mean tree height per plot
scover standardized percentage of canopy cover per plot
The stripped cone count was only taken when the mean diameter of trees was under 0.6m (dbh).
A data frame with 52 observations on the following 8 variables.
number cones stripped by squirrels
number of trees per plot
number DBH per plot
mean tree height per plot
canopy closure (as a percentage)
standardized number of trees per plot
standardized mean tree height per plot
standardized canopy closure (as a percentage)
nuts is saved as a data frame.
Count models use ntrees as response variable. Counts start at 3
Zuur, Hilbe, Ieno (2013), A Beginner's Guide to GLM and GLMM using R, Highlands
Hilbe, Joseph M (2014), Modeling Count Data, Cambridge University Press
Zuur, Hilbe, Ieno (2013), A Beginner's Guide to GLM and GLMM using R, Highlands.
Flaherty, S et al (2012), "The impact of forest stand structure on red
squirrels habitat use", Forestry 85:437-444.
nut <- subset(nuts, dbh < 0.6)
# sntrees <- scale(nuts$ntrees)
# sheigtht <- scale(nuts$height)
# scover <- scale(nuts$cover)
summary(PO <- glm(cones ~ sntrees + sheight + scover, family=quasipoisson, data=nut))
Dataset imported from https://www.r-project.org.