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.
data(nuts) 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))