difference | R Documentation |
Given a pair of telemetry
objects and ctmm
movement models, predict their location differences or midpoints at shared times and estimate their distances.
difference(data,CTMM,t=NULL,...)
midpoint(data,CTMM,t=NULL,complete=FALSE,...)
distances(data,CTMM,t=NULL,level=0.95,...)
proximity(data,CTMM,t=NULL,GUESS=ctmm(error=TRUE),debias=TRUE,level=0.95,...)
data |
A |
CTMM |
A |
t |
An optional vector of times or range of times over which to predict the location differences. |
complete |
Additionally calculate timestamps and geographic coordinates. |
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 midpoint
function predicts the location midpoints, (x_A+x_B,y_A+y_B)/2
, for a pair of individuals. 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
and midpoint
output telemetry
objects of the location differences and midpoints 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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