These functions estimate population-level parameters from individual movement models and related estimates, including AKDE home-range areas, while taking into account estimation uncertainty.

meta(x,level=0.95,level.UD=0.95,method="MLE",IC="AICc",boot=FALSE,error=0.01,debias=TRUE,
     verbose=FALSE,units=TRUE,plot=TRUE,sort=FALSE,mean=TRUE,col="black",...)

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.

method

Statistical estimator used---either maximum likelihood estimation based ("MLE") or approximate `best linear unbiased estimator' ("BLUE").

IC

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

boot

Perform a parametric bootstrap for confidence intervals and first-order bias correction if debias=TRUE.

error

Relative error tolerance for parametric bootstrap.

debias

Apply Bessel's inverse-Gaussian correction and various other bias corrections if method="MLE", REML if method="BLUE", and an additional first-order correction if boot=TRUE.

verbose

Return a list of both cross-population and sub-population analyses if TRUE and x is a list of sub-population lists.

units

Convert result to natural units.

plot

Generate a meta-analysis forest plot.

sort

Sort individuals by their point estimates in forest plot.

mean

Include population mean estimate in forest plot.

col

Color(s) for individual labels and error bars.

...

Further arguments passed to plot.

Details

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

For both estimator methods, the same underlying model is assumed.

Value

If x constitutes a sampled population, then meta returns a table with rows corresponding to the population mean and coefficient of variation. If x constitutes a list of sampled sub-populations, then meta returns confidence intervals on the sub-population mean ratios.

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)
# meta-analysis of buffalo meta(AKDES,col=c(COL,'black'),sort=TRUE)
#> ΔAICc #> Dirac-δ 0.000000 #> inverse-Gaussian 3.832925
#> low est high #> mean (km²) 357.5927 451.6346 556.4889 #> CoV² (RVAR) 0.0000 0.0000 Inf #> CoV (RSTD) 0.0000 0.0000 Inf
# }