mpmf: fit the move persistence model to regularized location data

In foieGras: Fit Continuous-Time State-Space and Latent Variable Models for Quality Control of Argos Satellite (and Other) Telemetry Data and for Estimating Movement Behaviour

Description

generates initial values for model parameters and unobserved gamma's; structures data and initial values for C++ TMB template; fits move persistence model; minimizes the joint log-likelihood via the selected optimizer (nlminb or optim); structures and passes output object to fit_mpm

Usage

mpmf(
  x,
  model = c("jmpm", "mpm"),
  control = mpm_control(),
  inner.control = NULL
)

Arguments

x	temporally regularized location data, eg. output from fit_ssm
model	specify whether MPM is to be fit with unpooled ("mpm") or pooled ("jmpm") RW variance(s).
control	list of control settings for the outer optimizer (see mpm_control for details)
inner.control	list of control settings for the inner optimization (see ?TMB::MakeADFUN for additional details)
verbose	use ssm_control(verbose = 1) instead, see ssm_control for details
optim	use ssm_control(optim = "optim") instead, see ssm_control for details
optMeth	use ssm_control(method = "L-BFGS-B") instead, see ssm_control for details

Details

called by fit_mpm, not intended for general use. see ?fit_mpm.

foieGras documentation built on April 27, 2021, 1:05 a.m.