On this Picostat.com statistics page, you will find information about the nuts data set which pertains to nuts. The nuts data set is found in the COUNT R package. You can load the nuts data set in R by issuing the following command at the console data("nuts"). This will load the data into a variable called nuts. If R says the nuts data set is not found, you can try installing the package by issuing this command install.packages("COUNT") and then attempt to reload the data. If you need to download R, you can go to the R project website. You can download a CSV (comma separated values) version of the nuts R data set. The size of this file is about 5,713 bytes.
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.