| func.cl.ord.repar {clespr} | R Documentation |
Reparameterized Composite Likelihood Calculation for Spatial Ordinal Data
Description
func.cl.ord calculates the composite log-likelihood for reparameterized spatial ordered probit models. This function is internally called by func.cle.ord.
Usage
func.cl.ord.repar(vec.yobs, mat.X, mat.lattice, radius, n.cat, vec.repar)
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. |
n.cat |
number of categories, at least 2. |
vec.repar |
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
func.cl.ord 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.
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.