ctmm: meta – R documentation

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meta

Meta-analysis of movement-model parameters

Description

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

Usage

meta(x,level=0.95,level.UD=0.95,method="MLE",IC="AICc",boot=FALSE,error=0.01,debias=TRUE,
     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 if `method="MLE"`, REML if `method="BLUE"`, and an additional first-order correction if `boot=TRUE`.
`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.

Note

The AICc formula is approximated via the Gaussian relation.

Author(s)

C. H. Fleming.

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,trace=2)
}

# calculate AKDES on a consistent grid
AKDES <- akde(buffalo,FITS,trace=1)

# 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)

ctmm

Continuous-Time Movement Modeling

v0.6.0

GPL-3

Authors

Christen H. Fleming [aut, cre], Justin M. Calabrese [aut], Xianghui Dong [ctb], Kevin Winner [ctb], Guillaume Péron [ctb], Michael J. Noonan [ctb], Bart Kranstauber [ctb], Eliezer Gurarie [ctb], Kamran Safi [ctb], Paul C. Cross [dtc], Thomas Mueller [dtc], Rogério C. de Paula [dtc], Thomas Akre [dtc], Jonathan Drescher-Lehman [dtc], Autumn-Lynn Harrison [dtc], Ronaldo G. Morato [dtc]

Initial release

2021-01-08