encounter | R Documentation |

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

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

`object` |
A |

`debias` |
Approximate bias corrections [IN DEVELOPMENT]. |

`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. |

Encounter probabilities are standardized to 1 meter, and must be multiplied by the square encounter radius (in meters), to obtain other values. 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.

`encounter`

produces an array of standardized encounter probabilities with CIs, while `cde`

produces a single `UD`

object.

Prior to v1.2.0, `encounter()`

calculated the CDE and `rates()`

calculated relative encounter probabilities.

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) \Sexpr[results=rd]{tools:::Rd_expr_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 100-meter encounter probabilities
P <- encounter(UDS)
P$CI * 100^2
# calculate CDE
CDE <- cde(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)
```

Embedding an R snippet on your website

Add the following code to your website.

For more information on customizing the embed code, read Embedding Snippets.