cluster {ctmm}

Clustering of movement-model parameters

Description

These functions cluster and classify individual movement models and related estimates, including AKDE home-range areas, while taking into account estimation uncertainty.

Usage

cluster(x,level=0.95,level.UD=0.95,debias=TRUE,IC="BIC",units=TRUE,plot=TRUE,sort=FALSE,
        ...)

Arguments

x

A list of ctmm movement-model objects, UD objects, or UD summary output, constituting a sampled population, or a list of such lists, each constituting a sampled sub-population.

level

Confidence level for parameter estimates.

level.UD

Coverage level for home-range estimates. E.g., 50% core home range.

debias

Apply Bessel's inverse-Gaussian correction and various other bias corrections.

IC

Information criterion to determine whether or not population variation can be estimated. Can be "AICc", AIC, or "BIC".

units

Convert result to natural units.

plot

Generate a meta-analysis forest plot with two means.

sort

Sort individuals by their point estimates in forest plot.

...

Further arguments passed to plot.

Details

So-far only the clustering of home-range areas is implemented. More details will be provided in an upcomming manuscript.

Value

A list with elements P and CI, where P is an array of individual membership probabilities for sub-population 1, and CI is a table with rows corresponding to the sub-population means, coefficients of variation, and membership probabilities, and the ratio of sub-population means.

Note

The AICc formula is approximated via the Gaussian relation.

Author(s)

C. H. Fleming.

See Also

akde, ctmm.fit, meta.

Examples

# load package and data
library(ctmm)
data(buffalo)

# fit movement models
FITS <- AKDES <- list()
for(i in 1:length(buffalo))
{
  GUESS <- ctmm.guess(buffalo[[i]],interactive=FALSE)
  # use ctmm.select unless you are certain that the selected model is OUF
  FITS[[i]] <- ctmm.fit(buffalo[[i]],GUESS)
}

# calculate AKDES on a consistent grid
AKDES <- akde(buffalo,FITS)

# color to be spatially distinct
COL <- color(AKDES,by='individual')

# plot AKDEs
plot(AKDES,col.DF=COL,col.level=COL,col.grid=NA,level=NA)

# cluster-analysis of buffalo
cluster(AKDES,sort=TRUE)