encounter | R Documentation |

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

rates(object,debias=TRUE,level=0.95,normalize=TRUE,self=TRUE,...) encounter(object,include=NULL,exclude=NULL,debias=FALSE,...)

`object` |
A |

`debias` |
Approximate bias corrections ( |

`level` |
Confidence level for relative encounter rates. |

`normalize` |
Normalize relative encounter rates by the average uncorrelated self-encounter rate. |

`self` |
Fix the self-interaction rate appropriately. |

`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). |

`...` |
Additional arguments for future use. |

If `normalize=FALSE`

, the relative encounter rates have units of *1/m^2* and tend to be very small numbers for very large home-range areas. If `normalize=TRUE`

, the relative encounter rates are normalized by the average uncorrelated self-encounter rate, which is an arbitrary value that provides a convenient scaling.

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.

`rates`

produces an array of relative encounter rates with CIs, while `encounter`

produces a single `UD`

object.

C. H. Fleming

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.

`akde`

, `overlap`

# 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)

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