fitting_model: Fit continuous-time movement models
In movedesign: Study Design Toolbox for Movement Ecology Studies

fitting_model

R Documentation

Fit continuous-time movement models

Description

This function fits continuous-time movement models to simulated location data using the ctmm package. It estimates movement parameters for each simulated trajectory, optionally running in parallel for efficiency.

Usage

fitting_model(
  obj,
  set_target = c("hr", "ctsd"),
  .dur = NULL,
  .dti = NULL,
  .tau_p = NULL,
  .tau_v = NULL,
  .error_m = NULL,
  .check_sampling = FALSE,
  .rerun = FALSE,
  .parallel = TRUE,
  .trace = FALSE
)

Arguments

`obj`	A list of simulated movement datasets.
`set_target`	A character vector indicating the research target(s). Options: `"hr"` - Home range estimation. `"ctsd"` - Speed and distance estimation.
`.dur`	Numeric, sampling duration of the simulated data (required if `.check_sampling = TRUE`).
`.dti`	Numeric, sampling interval of simulated data (required if `.check_sampling = TRUE`).
`.tau_p`	List, position autocorrelation timescale (optional).
`.tau_v`	List, velocity autocorrelation timescale (optional).
`.error_m`	Numeric, if simulating a dataset with location error (in meters).
`.check_sampling`	Logical; if `TRUE`, checks if the sampling schedule is optimal for ctmm.fit().
`.rerun`	Logical; if `TRUE`, re-runs model selection if effective sample sizes fall below threshold.
`.parallel`	Logical; if `TRUE`, enables parallel computation for efficiency. Default is `TRUE`.
`.trace`	Logical; if `TRUE`, prints additional information.

Details

The function first generates initial parameter estimates using ctmm::ctmm.guess(). It then selects the best movement model for each simulation using par.ctmm.select(). The function ensures that each fitted model is centered at the origin (x = 0, y = 0) before returning.