Summarize a range distribution

This function returns a list of biologically interesting parameters in human readable format, as derived from an autocorrelated kernel density estimate.

# S3 method for UD
summary(object,convex=FALSE,level=0.95,level.UD=0.95,units=TRUE,...)

Arguments

object

An akde autocorrelated kernel-density estimate from the output of akde.

convex

Report convex coverage areas if TRUE. By default, the highest density regions (HDRs) are reported.

level

Confidence level for the above area estimate. E.g., the 95% confidence interval of the 50% core area.

level.UD

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

units

Convert result to natural units.

...

Unused options.

Value

A list is returned with the effective sample sizes of various parameter estimates (DOF) and a parameter estimate table CI, with low, point, and high estimates for the following possible parameters:

area

The home-range area with fraction of inclusion level.UD. E.g., the 50% core home range is estimated with level.UD=0.50, and 95% confidence intervals are placed on that area estimate with level=0.95.

This kernel density estimate differs from the Gaussian estimate of summary.ctmm. The Gaussian estimate has more statistical efficiency, but is less related to space use for non-Gaussian processes.

References

C. H. Fleming, J. M. Calabrese. A new kernel-density estimator for accurate home-range and species-range area estimation. Methods in Ecology and Evolution, 8:5, 571-579 (2016) doi:10.1111/2041-210X.12673 .

Author

C. H. Fleming.

Note

Prior to ctmm v0.3.1, AKDEs included only errors due to autocorrelation uncertainty, which are insignificant in cases such as IID data. Starting in v0.3.1, akde calculated an effective sample size DOF.H and used this to estimate area uncertainty under a chi-square approxmation. Starting in v0.3.2, this method was improved to use DOF.area in the Gaussian reference function approximation.

See also

akde.

Examples

# \donttest{
# Load package and data
library(ctmm)
data(buffalo)

# Extract movement data for a single animal
DATA <- buffalo$Cilla

# Fit a movement model
GUESS <- ctmm.guess(DATA,interactive=FALSE)
FIT <- ctmm.fit(DATA,GUESS)

# Estimate and summarize the AKDE
UD <- akde(DATA,FIT)
#> Default grid size of 3 minutes chosen for bandwidth(...,fast=TRUE).
summary(UD)
#> $DOF
#>      area bandwidth 
#>  18.13595  29.25018 
#> 
#> $CI
#>                               low      est     high
#> area (square kilometers) 223.4711 376.2253 568.1175
#> 
#> attr(,"class")
#> [1] "area"
# }