sim.rord | R Documentation |
sim.rord
Simulate replications of spatial ordinal data
sim.rord( n.subject, n.site, n.rep = 100, midalpha, beta, phi, sigma2, covar, location )
n.subject |
number of subjects. |
n.site |
number of spatial sites for each subject. |
n.rep |
number of simulation. Parameter inputs include: |
midalpha |
cutoff parameter (excluding -Inf and +Inf); |
beta |
regression coefficient; |
phi |
dependence parameter for spatial dependence; and |
sigma2 |
sigma^2 (variance) for the spatial dependence. |
covar |
regression (design) matrix, including intercepts. |
location |
a matrix contains spatial location of sites within each subject. |
sim.rord
returns a list (length n.rep
) of matrix (n.subject*n.site
) with the underlying parameter as inputs.
set.seed(1203) n.subject <- 100 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) ; phi <- 0.8 ; sigma2 <- 0.7 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, phi, sigma2, covar=VV, location) length(sim.data) head(sim.data[[1]]) dim(sim.data[[1]]) hist(sim.data[[1]])
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