mean.ctmm  R Documentation 
These functions calculate population averages of continuoustime movement models and utilization distributions.
## S3 method for class 'ctmm'
mean(x,weights=NULL,sample=TRUE,debias=TRUE,IC="AIC",trace=TRUE,...)
## S3 method for class 'UD'
mean(x,weights=NULL,sample=TRUE,...)
x 
A list of 
weights 
A vector of numeric weights with the same length as 
sample 

debias 
Include 
IC 
Model selection criterion for the anisotropy of the distribution of mean locations and covariance matrices. 
trace 
Report location and autocovariance model selection results. 
... 
Additional arguments for future use. 
When applied to a list of ctmm
objects, mean
calculates an average movement model with populaton variability estimates.
The population model is taken to be multivariate normal and lognormal.
The population mean location represents an arithmetic mean, while the population mean homerange areas, RMS speeds, and diffusion rates represent geometric means.
Locationerror estimates are not correctly averaged yet.
When applied to a list of UD
objects, mean
calculates a weighted average of autocorrelated kernel density homerange estimates from akde
. The point estimates are correct, but the confidenceinterval calculation is not yet complete.
By default, uniform weights are used (weights=rep(1,length(x))
). This can be sensible for averaging over individuals. For averaging over periods of time, users should consider weighting by the proportion of time spent in each distribution. For example, if an animal spends 4 months in its winter range, x[[1]]
, and 7 months in its summer range, x[[2]]
, then the annual range (sans migration corridor) would be calculated with weights=c(4,7)
.
All UDs need to be calculated on the same grid (see overlap
for an example).
When applied to a list of ctmm
objects, mean
returns a ctmm
object with additional population variability parameter estimates.
When applied to a list of UD
objects, mean
returns a UD
object: a list with the sampled grid line locations r$x
and r$y
, the extent of each grid cell dr
, the probability density and cumulative distribution functions evaluated on the sampled grid locations PDF
& CDF
, the optimal bandwidth matrix H
, and the effective sample size of the data in DOF.H
.
C. H. Fleming
akde
, ctmm.select
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