`encounter.Rd`

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,...)

object | A |
---|---|

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. |

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.

A `UD`

object.

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 .

C. H. Fleming

# \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)#>#># }