cluster | R Documentation |
These functions cluster and classify individual movement models and related estimates, including AKDE home-range areas, while taking into account estimation uncertainty.
cluster(x,level=0.95,level.UD=0.95,debias=TRUE,IC="BIC",units=TRUE,plot=TRUE,sort=FALSE,
...)
x |
A list of |
level |
Confidence level for parameter estimates. |
level.UD |
Coverage level for home-range estimates. E.g., 50% core home range. |
debias |
Apply Bessel's inverse-Gaussian correction and various other bias corrections. |
IC |
Information criterion to determine whether or not population variation can be estimated. Can be |
units |
Convert result to natural units. |
plot |
Generate a meta-analysis forest plot with two means. |
sort |
Sort individuals by their point estimates in forest plot. |
... |
Further arguments passed to |
So-far only the clustering of home-range areas is implemented. More details will be provided in an upcomming manuscript.
A list
with elements P
and CI
,
where P
is an array of individual membership probabilities for sub-population 1,
and CI
is a table with rows corresponding to the sub-population means, coefficients of variation, and membership probabilities, and the ratio of sub-population means.
The AICc formula is approximated via the Gaussian relation.
C. H. Fleming.
akde
, ctmm.fit
, meta
.
# 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)
# cluster-analysis of buffalo
cluster(AKDES,sort=TRUE)
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