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

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.

Author

C. H. Fleming.

Note

The AICc formula is approximated via the Gaussian relation.

See also

Examples

# \donttest{ # 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,trace=2) }
#> Maximizing likelihood.
#> Calculating Hessian.
#> Calculating REML gradient.
#> Calculating REML Hessian.
#> Maximizing likelihood.
#> Calculating Hessian.
#> Calculating REML gradient.
#> Calculating REML Hessian.
#> Maximizing likelihood.
#> Calculating Hessian.
#> Calculating REML gradient.
#> Calculating REML Hessian.
#> Maximizing likelihood.
#> Calculating Hessian.
#> Calculating REML gradient.
#> Calculating REML Hessian.
#> Maximizing likelihood.
#> Calculating Hessian.
#> Calculating REML gradient.
#> Calculating REML Hessian.
#> Maximizing likelihood.
#> Calculating Hessian.
#> Calculating REML gradient.
#> Calculating REML Hessian.
# calculate AKDES on a consistent grid AKDES <- akde(buffalo,FITS,trace=1)
#> Default grid size of 3 minutes chosen for bandwidth(...,fast=TRUE).
#> Bandwidth optimization complete.
#> Default grid size of 2 minutes chosen for bandwidth(...,fast=TRUE).
#> Bandwidth optimization complete.
#> Default grid size of 2 minutes chosen for bandwidth(...,fast=TRUE).
#> Bandwidth optimization complete.
#> Default grid size of 3 minutes chosen for bandwidth(...,fast=TRUE).
#> Bandwidth optimization complete.
#> Default grid size of 2 minutes chosen for bandwidth(...,fast=TRUE).
#> Bandwidth optimization complete.
#> Default grid size of 5 minutes chosen for bandwidth(...,fast=TRUE).
#> Bandwidth optimization complete.
# 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)
#> ΔBIC #> Dirac-δ 0.0000000 #> inverse-Gaussian 0.9580284 #> Dirac-δ + Dirac-δ 1.3561666 #> inverse-Gaussian(CoV) + inverse-Gaussian(CoV) 3.1479260 #> inverse-Gaussian + Dirac-δ 3.1479260 #> Dirac-δ + inverse-Gaussian 3.1479260 #> inverse-Gaussian(CoV₁) + inverse-Gaussian(CoV₂) 4.9396855
#> $P #> Cilla Gabs Mvubu Pepper Queen Toni #> 1 1 1 1 1 1 #> #> $CI #> low est high #> μ₁ (km²) 357.5927 451.6343 556.4884 #> CoV₁ 0.0000 0.0000 Inf #> μ₂ (km²) 357.5927 451.6343 556.4884 #> CoV₂ 0.0000 0.0000 Inf #> P₁ 0.0000 1.0000 1.0000 #> P₂ 0.0000 0.0000 1.0000 #> μ₂/μ₁ 0.0000 1.0000 Inf #>
# }