lme4 (version 1.1-38)

gopherdat2: Gopher tortoises shell remains

Description

Data of fresh Gopher tortoise shell remains ozgul2009upperlme4.

Usage

data("gopherdat2")

Format

A data frame with 30 observations on the following 7 variables.

Site

a factor representing the sampling site. There are 10 levels:

• BS (Big Shoals State Park).

• CB (Camp Blanding Wildlife Management Area).

• Cent (privately-owned property in central Florida).

• CF (Cecil Field/Branan Field Wildlife and Environmental Area).

• FC (Fort Cooper State Park).

• FE (Flying Eagle Wildlife Management Area).

• GH (Gold Head Branch State Park).

• Old (Perry Oldenburg Wildlife and Environmental Area).

• Ord (Ordway-Swisher Biological Station).

• TE (Tenoroc Fish Management Area).

year

a numeric vector of the sampling year.

shells

a numeric vector of the number of shells found.

Area

a numeric vector representing site area (units unknown).

density

a numeric vector representing population density.

prev

a numeric vector representing the seroprevalence (frequency of antibodies to disease) of M. agassizi.

Details

Details of the study is described in ozgul2009upperlme4 as follows: "The fieldwork was conducted between 2003 and 2006, during late spring/summer (May–September). Systematic surveys were conducted to locate tortoise burrows and shell remains from deceased tortoises at study sites and consisted of a line of four to eight observers spaced 10 m apart walking parallel transects across the study area."

References

ozgul2009upperlme4

Examples

## Simple model gives a singular fit:
gopher_glmer <- glmer(shells ~ factor(year) + prev + offset(log(Area))
                   + (1|Site), data = gopherdat2, family = "poisson")
## The site-level variance for this model is indeed zero:
VarCorr(gopher_glmer)
## So a Poisson GLM gives the same answer here:
gopher_glm <- glm(shells ~ factor(year) + prev + offset(log(Area)),
                  data = gopherdat2, family = "poisson")
all.equal(fixef(gopher_glmer), coef(gopher_glm))