| func.cl.prop {clespr} | R Documentation |
Composite Likelihood Calculation for Spatial Proportional Data
Description
func.cl.prop calculates the composite log-likelihood for spatial Tobit models.
Usage
func.cl.prop(vec.yobs, mat.X, mat.lattice, radius, vec.par)
Arguments
vec.yobs |
a vector of observed responses for all N sites. |
mat.X |
regression (design) matrix, including intercepts. |
mat.lattice |
a data matrix containing geographical information of sites. The i th row constitutes a set of geographical coordinates. |
radius |
weight radius. |
vec.par |
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
func.cl.prop 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.
References
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.
Examples
# 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.site <- 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
set.seed(1228)
# Generate regression (design) matrix with intercept
mat.X <- cbind(rep(1, n.site),scale(matrix(rnorm(n.site*(length(vec.beta)-1)),nrow=n.site)))
vec.Z <- t(chol(mat.cov)) %*% rnorm(n.site) + mat.X %*% vec.beta
vec.epsilon <- diag(sqrt(1-sigmasq), n.site) %*% rnorm(n.site)
vec.ylat <- as.numeric(vec.Z + vec.epsilon)
# Convert to the vector of observation
vec.yobs <- func.obs.prop(vec.ylat, alpha=alpha)
# Use func.cl.prop()
ls <- func.cl.prop(vec.yobs, mat.X, mat.lattice, radius, vec.par)
ls$log.lkd