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-χ^2 bias corrections. |
... |
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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