{clespr}R Documentation

Composite Likelihood Calculation for Spatial Proportional Data

Description calculates the composite log-likelihood for spatial Tobit models.

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



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.


a vector of parameters consecutively as follows: a cutoff point 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 of 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.


# True parameter
alpha <- 4; vec.beta <- c(1, 2, 1, 0, -1); sigmasq <- 0.8; rho <- 0.6; radius <- 5
vec.par <- c(alpha, vec.beta, sigmasq, rho)

# Coordinate matrix
n.lati <- 30; n.long <- 30 <- n.lati * n.long
mat.lattice <- cbind(rep(1:n.lati, n.long), rep(1:n.long, each=n.lati))
mat.dist <- as.matrix(dist(mat.lattice, upper=TRUE, diag=TRUE))
mat.cov <- sigmasq * rho^mat.dist


# Generate regression (design) matrix with intercept
mat.X <- cbind(rep(1,,scale(matrix(rnorm(*(length(vec.beta)-1)),
vec.Z <- t(chol(mat.cov)) %*% rnorm( + mat.X %*% vec.beta
vec.epsilon <- diag(sqrt(1-sigmasq), %*% rnorm(
vec.ylat <- as.numeric(vec.Z + vec.epsilon)

# Convert to the vector of observation
vec.yobs <- func.obs.prop(vec.ylat, alpha=alpha)

# Use
ls <-, mat.X, mat.lattice, radius, vec.par)

[Package clespr version 1.1.2 Index]