# NOT RUN {
mu <- Sigma <- shape <- list()
mu[[1]] <- c(-3,-4)
mu[[2]] <- c(2,2)
Sigma[[1]] <- matrix(c(3,1,1,4.5), 2,2)
Sigma[[2]] <- matrix(c(2,1,1,3.5), 2,2)
shape[[1]] <- c(-2,2)
shape[[2]] <- c(-3,4)
nu <- c(0,0)
pii <- c(0.6,0.4)
percen <- c(0.1,0.2)
n <- 200
g <- 2
seed <- 654678
set.seed(seed)
test = rMMSN(n = n, pii = pii,mu = mu,Sigma = Sigma,shape = shape,
percen = percen, each = TRUE, family = "SN")
Zij <- test$G
cc <- test$cc
y <- test$y
## left censure ##
LI <-cc
LS <-cc
LI[cc==1]<- -Inf
LS[cc==1]<- y[cc==1]
test_fit.cc0 = fit.FMMSNC(cc, LI, LS, y, mu=mu,
Sigma = Sigma, shape=shape, pii = pii, g = 2, get.init = FALSE,
criteria = TRUE, family = "Normal", error = 0.0001,
iter.max = 200, uni.Gama = FALSE, cal.im = FALSE)
#full analysis may take a few seconds more...
# }
# NOT RUN {
test_fit.cc = fit.FMMSNC(cc, LI, LS, y, mu=mu,
Sigma = Sigma, shape=shape, pii = pii, g = 2, get.init = FALSE,
criteria = TRUE, family = "SN", error = 0.00001,
iter.max = 350, uni.Gama = FALSE, cal.im = TRUE)
## missing data ##
pctmiss <- 0.2 # 20% of missing data in the whole data
missing <- matrix(runif(n*g), nrow = n) < pctmiss
y[missing] <- NA
cc <- matrix(nrow = n,ncol = g)
cc[missing] <- 1
cc[!missing] <- 0
LI <- cc
LS <-cc
LI[cc==1]<- -Inf
LS[cc==1]<- +Inf
test_fit.mis = fit.FMMSNC(cc, LI, LS, y, mu=mu,
Sigma = Sigma, shape=shape, pii = pii, g = 2, get.init = FALSE,
criteria = TRUE, family = "SN", error = 0.00001,
iter.max = 350, uni.Gama = FALSE, cal.im = TRUE)
# }
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