cl.rord: Composite Likelihood Calculation for Replciations of Spatial...

In clordr: Composite Likelihood Inference and Diagnostics for Replicated Spatial Ordinal Data

Composite Likelihood Calculation for Replciations of Spatial Ordinal Data (for illustration)

Description

cl.rord Calculate the negative composite log-likelihood value for replications of spatial ordinal data at given value of parameter value. Note that this function is not directly used in cle.rord but illustration only.

Usage

cl.rord(theta, response, covar, location, radius = 4)

Arguments

theta	a vector of parameter value
response	a matrix of observation (row: spatial site and column: subject).
covar	regression (design) matrix, including intercepts.
location	a matrix contains spatial location of sites within each subject
radius	radius for selecting pairs for the composite likelihood estimation.

Value

cl.rord returns a list: negative composite log-likelihood, a vector of first-order partial derivatives for theta.

Examples

set.seed(1203)
n.subject <- 10
n.lat <- n.lon <- 10
n.site <- n.lat*n.lon

beta <- c(1,2,-1) # First 1 here is the intercept
midalpha <- c(1.15, 2.18) ; sigma2 <- 0.7 ; phi <- 0.8

true = c(midalpha,beta,sigma2,phi)

Xi = rnorm(n.subject,0,1) ; Xj <- rbinom(n.site,1,0.6)

 VV <- matrix(NA, nrow = n.subject*n.site, ncol = 3)

 for(i in 1:n.subject){ for(j in 1:n.site){
     VV[(i-1)*n.site+j,] <- c(1,Xi[i],Xj[j])
       }
 }

location = cbind(rep(seq(1,n.lat,length=n.lat),n.lat),rep(1:n.lon, each=n.lon))
sim.data <- sim.rord(n.subject, n.site, n.rep = 2, midalpha, beta, sigma2, phi, covar=VV, location)

cl.rord(theta=true,response=sim.data[[1]], covar=VV, location, radius = 4)

clordr documentation built on March 25, 2022, 1:06 a.m.