Calculate the conditional location distribution of ecounters

Functions to calculate the location distribution of where encounters take place, conditional on said encounters taking place, as described in Noonan et al (2021).

encounter(object,include=NULL,exclude=NULL,debias=FALSE,...)

Arguments

object	A `list` of aligned `UD` objects.
include	A matrix of interactions to include in the calculation (see Details below).
exclude	A matrix of interactions to exclude in the calculation (see Details below).
debias	Approximate GRF bias correction (in development).
...	Additional arguments for future use.

Details

The include argument is a matrix that indicates which interactions are considered in the calculation. By default, include = 1 - diag(length(object)), which implies that all interactions are considered aside from self-interactions. Alternatively, exclude = 1 - include can be specified, and is by-default exclude = diag(length(object)), which implies that only self-encounters are excluded.

Value

A UD object.

References

M. J. Noonan, R. Martinez-Garcia, G. H. Davis, M. C. Crofoot, R. Kays, B. T. Hirsch, D. Caillaud, E. Payne, A. Sih, D. L. Sinn, O. Spiegel, W. F. Fagan, C. H. Fleming, J. M. Calabrese, ``Estimating encounter location distributions from animal tracking data'', Methods in Ecology and Evolution (2021) doi: 10.1111/2041-210X.13597 .

Author

C. H. Fleming

Examples

# \donttest{
# Load package and data
library(ctmm)
data(buffalo)

# fit models for first two buffalo
GUESS <- lapply(buffalo[1:2], function(b) ctmm.guess(b,interactive=FALSE) )
# in general, you should use ctmm.select here
FITS <- lapply(1:2, function(i) ctmm.fit(buffalo[[i]],GUESS[[i]]) )
names(FITS) <- names(buffalo[1:2])

# create aligned UDs
UDS <- akde(buffalo[1:2],FITS)

# calculate CDE
CDE <- encounter(UDS)

# plot data and encounter distribution
plot(buffalo[1:2],col=c('red','blue'),UD=CDE,col.DF='purple',col.level='purple',col.grid=NA)
#> DOP values missing. Assuming DOP=1.
#> DOP values missing. Assuming DOP=1.
# }