This function allows the point estimates and confidence intervals of an initial estimated movement model to be improved by parametric boostrap, as described in Fleming et al (2019).
ctmm.boot(data,CTMM,method=CTMM$method,iterate=FALSE,robust=FALSE,error=0.01,cores=1,
trace=TRUE,...)Timeseries data represented as a telemetry object.
A ctmm movement-model object from the output of ctmm.fit containing the initial parameter estimates.
Fitting method to use: "ML", "HREML", "pREML", "pHREML", or "REML". See ctmm.fit for descriptions.
Iteratively solve for the parameters such that the average estimate (of method) is that of the data, whereas with iterate=FALSE only the first-order correction is calculated from the initial estimate.
Uses robust estimates of the average and covariation for debiasing. Useful when parameters are near boundaries.
Relative standard error target for bootstrap ensemble estimates and nonlinear iterations.
Number of simulations to run in parallel. cores=NULL will use all cores, while cores<0 will reserve abs(cores).
Report progress updates. Can be among 0:2 with increasing detail.
Further arguments passed to ctmm.fit.
A model fit object with relatively unbiased estimates of location covariance, and autocorrelation timescales (and more accurate CIs than ctmm.fit).