cl.rord | R Documentation |
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.
cl.rord(theta, response, covar, location, radius = 4)
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. |
cl.rord
returns a list: negative composite log-likelihood, a vector of first-order partial derivatives for theta
.
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)
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