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,level=0.95,level.UD=0.95,units=TRUE,...)

Arguments

object

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

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

Examples

# \donttest{ # Load package and data library(ctmm) data(buffalo) # Extract movement data for a single animal Cilla <- buffalo$Cilla # Fit a movement model GUESS <- ctmm.guess(Cilla,interactive=FALSE) FIT <- ctmm.fit(Cilla,GUESS) # Estimate and summarize the AKDE UD <- akde(Cilla,FIT) summary(UD)
#> $DOF #> area bandwidth #> 18.13598 29.23444 #> #> $CI #> low est high #> area (square kilometers) 223.4286 376.1535 568.009 #>
# }