{clespr}R Documentation

Reparameterized Composite Likelihood Calculation for Spatial Ordinal Data

Description calculates the composite log-likelihood for reparameterized spatial ordered probit models. This function is internally called by func.cle.ord.

Usage, mat.X, mat.lattice, radius,, vec.repar)



a vector of observed responses for all N sites.


regression (design) matrix, including intercepts.


a data matrix containing geographical information of sites. The i th row constitutes a set of geographical coordinates.


weight radius.

number of categories, at least 2.


a vector of parameters consecutively as follows: a reparameterized vector (tau's) for latent responses, a vector of covariate parameters, a parameter 'sigmasq' modeling covariance matrix, 0<=sigmasq<=1, and a parameter 'rho' reflecting spatial correlation, abs(rho)<=1.

Value returns a list: number of categories, sum of weights, composite log-likelihood, a vector of scores, and a matrix of first-order partial derivatives for vec.par.


Feng, Xiaoping, Zhu, Jun, Lin, Pei-Sheng, and Steen-Adams, Michelle M. (2014) Composite likelihood Estimation for Models of Spatial Ordinal Data and Spatial Proportional Data with Zero/One values. Environmetrics 25(8): 571–583.

[Package clespr version 1.1.2 Index]