fit_pl: Maximum Pseudo-likelihood fitting of MRFs on 2d lattices.

In mrf2d: Markov Random Field Models for Image Analysis

Description

Parameter estimation for Markov random fields via Pseudo-Likelihood function optimization. See pl_mrf2d for information on the Pseudo-Likelihood function.

Usage

fit_pl(
  Z,
  mrfi,
  family = "onepar",
  init = 0,
  optim_args = list(method = "BFGS"),
  return_optim = FALSE
)

Arguments

Z	A matrix object containing the observed MRF. NA values can be used to create a subregion of the lattice for non-rectangular data.
mrfi	A mrfi object representing the interaction structure.
family	The family of parameter restrictions to potentials. Families are: 'onepar', 'oneeach', 'absdif', 'dif' or 'free'. See mrf2d-familiy.
init	The initial value to be used in the optimization. It can be: A valid array of parameter values according to family. 0. If set to 0 an array with '0“ in all entries is created.
optim_args	Additional parameters passed to optim().
return_optim	logical indicating whether information from the optim() call are returned.

Value

An object of class mrfout with elements:

• theta: The estimated array of potential values.

• mrfi: The interaction structure considered.

• family: The parameter restriction family considered.

• method: The estimation method ("Pseudolikelihood").

• value: The optimal pseudo-likelihood value.

• opt.xxx(if return_optim is TRUE): Information returned by the optim() function used for the optimization.

Author(s)

Victor Freguglia

See Also

A paper with detailed description of the package can be found at doi: 10.18637/jss.v101.i08.

Examples

fit_pl(Z_potts, mrfi(1), family = "onepar")
fit_pl(Z_potts, mrfi(1), family = "oneeach")
fit_pl(Z_potts, mrfi(2), family = "onepar")

mrf2d documentation built on Jan. 26, 2022, 1:06 a.m.