difference | R Documentation |

Given a pair of `telemetry`

objects and `ctmm`

movement models, predict their location differences at shared times and estimate their mean-square distance.

difference(data,CTMM,t=NULL,...) distances(data,CTMM,t=NULL,level=0.95,...) proximity(data,CTMM,GUESS=ctmm(error=TRUE),debias=TRUE,level=0.95,...)

`data` |
A |

`CTMM` |
A |

`t` |
An optional vector of times over which to predict the location differences. |

`level` |
Confidence level for the distance/proximity estimate. |

`GUESS` |
An optional |

`debias` |
Include inverse- |

`...` |
Options passed to |

The `difference`

function predicts the location difference vectors, *(x_A-x_B,y_A-y_B)*, for a pair of individuals, *\{A,B\}*, at overlapping times. The `distances`

function further estimates the instantaneous distances between individuals. The `proximity`

function fits an autocorrelation model to the output of `difference`

, and then compares the mean-square distance between the individuals to what you would expect if the two individuals were moving independently.

`difference`

outputs a `telemetry`

object of the location differences with prediction covariances. `distances`

outputs a `data.frame`

of distance estimates with confidence intervals. `proximity`

outputs a ratio estimate with confidence intervals, where values `<1`

indiciate that the two individuals are closer on average than expected for independent movement, `1`

is consistent with independent movement, and values `>1`

indicate that the individuals are farther from each other on average than expected for independent movement. Therefore, if the CIs contain 1, then the distance is insignificant with a p-value threshold of `1-level`

(two-sided) or half that for a one-sided test.

C. H. Fleming.

`ctmm.select`

, `predict.ctmm`

#Load package library(ctmm) # load buffalo data data(buffalo) # select two buffalo that overlap in space and time DATA <- buffalo[c(1,3)] # plot the two buffalo plot(DATA,col=c('red','blue')) FITS <- list() for(i in 1:2) { GUESS <- ctmm.guess(DATA[[i]],interactive=FALSE) # in general, you want to use ctmm.select FITS[[i]] <- ctmm.fit(DATA[[i]],GUESS) } # calculate difference vectors DIFF <- difference(DATA,FITS) # plot the difference vectors with prediction-error ellipses plot(DIFF) # calculate the proximity statistic # disabling location error for speed proximity(DATA,FITS,GUESS=ctmm(error=FALSE))

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