meta | R Documentation |
These functions estimate population-level mean parameters from individual movement models and related estimates, including AKDE home-range areas, while taking into account estimation uncertainty.
meta(x,variable="area",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",...)
funnel(x,y,variable="area",precision="t",level=0.95,level.UD=0.95,...)
x |
A named list of |
y |
An optional named list of |
variable |
Biological “effect” variable of interest for |
precision |
Precision variable of interest. Can be |
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 ( |
IC |
Information criterion to determine whether or not population variation can be estimated. Can be |
boot |
Perform a parametric bootstrap for confidence intervals and first-order bias correction if |
error |
Relative error tolerance for parametric bootstrap. |
debias |
Apply Bessel's inverse-Gaussian correction and various other bias corrections if |
verbose |
Return a list of both population and meta-population analyses if |
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 |
meta
employs a custom \chi^2
-IG hierarchical model to calculate debiased population mean estimates of positive scale parameters,
including home-range areas, diffusion rates, mean speeds, and autocorrelation timescales.
Model selection is performed between the \chi^2
-IG population model (with population mean and variance) and the Dirac-\delta
population model (population mean only).
Population “coefficient of variation” (CoV) estimates are also provided.
Further details are given in Fleming et al (2022).
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 populations, then meta
returns confidence intervals on the population mean variable
ratios.
The AICc formula is approximated via the Gaussian relation.
Confidence intervals depicted in the forest plot are \chi^2
and may differ from the output of summary()
in the case of mean speed and timescale parameters with small effective sample sizes.
As mean ratio estimates are debiased, reciprocal estimates can differ slightly.
C. H. Fleming.
C. H. Fleming, I. Deznabi, S. Alavi, M. C. Crofoot, B. T. Hirsch, E. P. Medici, M. J. Noonan, R. Kays, W. F. Fagan, D. Sheldon, J. M. Calabrese, “Population-level inference for home-range areas”, Methods in Ecology and Evolution 13:5 1027–1041 (2022) \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1111/2041-210X.13815")}.
akde
, cluster
, ctmm.fit
.
# 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)
}
# calculate AKDES on a consistent grid
AKDES <- akde(buffalo,FITS)
# 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 home-range areas
meta(AKDES,col=c(COL,'black'),sort=TRUE)
# funnel plot to check for sampling bias
funnel(AKDES,buffalo)
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