Instead I’ll use a Poisson GAM, but in all other respects the analysis follows that from the text book (only the year 2007 data are used, field size transformed to hectares). (2015) uses a Poisson GLM with a quadratic effect of the variable age. # install.packages('blmeco') # first, if not already installed library ( 'blmeco' ) data ( wildflowerfields ) library ( 'ggplot2' ) theme_set ( theme_bw ()) The data are in the blmeco package available on CRAN. The data come from a study looking at how the number of breeding pairs of common whitethroat change as the composition and structure of the plant community changes over time. As a mitigiation effort, wildflower fields are sown and left largely unmanaged for several years. This species likes to inhabit field margins and fallow lands and has been adversely affected by intensive agricultural activities reducing these types of habitat on the landscape. (2015), and concerns the number of breeding pairs of the common whitethroat ( Silvia communis).

The second example - purely because I happen to have it handy from teaching this semester - is from Korner-Nievergelt et al. I’ve broken it into two and in this, the second part, I look at Possion models.

Comments, even on StackOverflow, aren’t a good place for a discussion so I thought I’d post something hereon my blog that went into a bit more detail as to why, for some common types of GLMs, prediction intervals aren’t that useful and require a lot more thinking about what they mean and how they should be calculated. One of my more popular answers on StackOverflow concerns the issue of prediction intervals for a generalized linear model (GLM).

Glm permute vector swizzle