Nothing
simID <- function(id=NULL, x1, x2, x3, beta1.true, beta2.true, beta3.true,
alpha1.true, alpha2.true, alpha3.true,
kappa1.true, kappa2.true, kappa3.true, theta.true, SigmaV.true=NULL, cens)
{
if(!is.null(id) & is.null(SigmaV.true)){
stop("SigmaV.true must be given to simulate correated data")
}
else
{
n <- dim(x1)[1]
p1 <- dim(x1)[2]
p2 <- dim(x2)[2]
p3 <- dim(x3)[2]
if(theta.true >0)
{
gamma.true <- rgamma(n, 1/theta.true, 1/theta.true)
}
if(theta.true == 0)
{
gamma.true <- rep(1, n)
}
if(is.null(id))
{
LP1 <- as.vector(beta1.true %*% t(x1))
LP2 <- as.vector(beta2.true %*% t(x2))
LP3 <- as.vector(beta3.true %*% t(x3))
}
if(!is.null(id))
{
J <- length(unique(id))
nj <- as.vector(table(id))
Vmat <- mvrnorm(J, rep(0, 3), SigmaV.true) # J X 3
LP1 <- as.vector(beta1.true %*% t(x1) + rep(Vmat[,1], nj))
LP2 <- as.vector(beta2.true %*% t(x2) + rep(Vmat[,2], nj))
LP3 <- as.vector(beta3.true %*% t(x3) + rep(Vmat[,3], nj))
}
Rind <- NULL
R <- rweibull(n, shape = alpha1.true, scale = exp(-(log(kappa1.true) +
LP1 + log(gamma.true))/alpha1.true))
D <- rweibull(n, shape = alpha2.true, scale = exp(-(log(kappa2.true) +
LP2 + log(gamma.true))/alpha2.true))
yesR <- R < D
D[yesR] <- R[yesR] + rweibull(sum(yesR), shape = alpha3.true,
scale = exp(-(log(kappa3.true) + LP3[yesR] + log(gamma.true[yesR]))/alpha3.true))
delta1 <- rep(NA, n)
delta2 <- rep(NA, n)
y1 <- R
y2 <- D
Cen <- runif(n, cens[1], cens[2])
ind01 <- which(D < R & D < Cen)
y1[ind01] <- D[ind01]
delta1[ind01] <- 0
delta2[ind01] <- 1
ind10 <- which(R < D & R < Cen & D >= Cen)
y2[ind10] <- Cen[ind10]
delta1[ind10] <- 1
delta2[ind10] <- 0
ind00 <- which(R >= Cen & D >= Cen)
y1[ind00] <- Cen[ind00]
y2[ind00] <- Cen[ind00]
delta1[ind00] <- 0
delta2[ind00] <- 0
ind11 <- which(R < Cen & D < Cen & R < D)
delta1[ind11] <- 1
delta2[ind11] <- 1
ret <- data.frame(cbind(y1, delta1, y2, delta2))
return(ret)
}
}
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