Nothing
bootsfitMESA <- function(i, N = 200,
beta = c(80, -3, -3, 0.5, 5),
tau = c(2, 1, 1, 0.05, 0.3),
gamma1 = c(0.1, -0.3),
gamma2 = c(0.1, -0.3),
alpha1 = c(0, -0.03, 0),
alpha2 = c(-0.02, 0, 0.02),
vee1 = 0.7,
vee2 = 0.8,
covbw = matrix(c(200, -70, -70, 10,
-70, 150, 70, -2,
-70, 70, 150, -2,
10, -2, -2, 0.7),
nrow = 4,
ncol = 4),
lambda1 = 1e-5,
lambda2 = 3e-5,
lambdaC = 1e-2,
incremin = 1,
incremax = 2.5,
Cmax = 18,
seed = 100, maxiter = 1000,
quadpoint = 6) {
data <- simJMMESA(seed = seed + i, N = N, beta = beta,
tau = tau,
gamma1 = gamma1,
gamma2 = gamma2,
alpha1 = alpha1,
alpha2 = alpha2,
vee1 = vee1,
vee2 = vee2,
lambda1 = lambda1,
lambda2 = lambda2,
lambdaC = lambdaC,
incremin = incremin,
incremax = incremax,
Cmax = Cmax,
covbw = covbw)
ydata <- data$ydata
cdata <- data$cdata
rate <- data$rate
a <- proc.time()
fit <- try(JMMLSM(cdata = cdata, ydata = ydata,
long.formula = Y ~ timens1 + timens2 + Z1 + Z2,
surv.formula = Surv(survtime, cmprsk) ~ var1 + var2,
variance.formula = ~ timens1 + timens2 + Z1 + Z2,
quadpoint = quadpoint, random = ~ timens1 + timens2|ID,
initial.para = list(beta = beta,
tau = tau,
gamma1 = gamma1,
gamma2 = gamma2,
alpha1 = alpha1,
alpha2 = alpha2,
vee1 = vee1,
vee2 = vee2,
Sig = covbw),
epsilon = 1e-3), silent = TRUE)
b <- proc.time()
time <- (b - a)[3]
coef <- vector()
coefSE <- vector()
count <- 1
if ('try-error' %in% class(fit)) {
coef <- rep(NA, 34)
coefSE <- rep(NA, 32)
coef <- list(coef, coefSE, rate)
names(coef) <- c("coef", "coefSE", "rate")
return(coef)
} else if (is.null(fit$beta)) {
coef <- rep(NA, 34)
coefSE <- rep(NA, 32)
coef <- list(coef, coefSE, rate)
names(coef) <- c("coef", "coefSE", "rate")
return(coef)
} else if (fit$iter == maxiter) {
coef <- rep(NA, 34)
coefSE <- rep(NA, 32)
coef <- list(coef, coefSE, rate)
names(coef) <- c("coef", "coefSE", "rate")
return(coef)
} else {
for (j in 1:length(fit$beta)) {
coef[count] <- fit$beta[j]
coefSE[count] <- fit$sebeta[j]
count <- count + 1
}
for (j in 1:length(fit$tau)) {
coef[count] <- fit$tau[j]
coefSE[count] <- fit$setau[j]
count <- count + 1
}
for (j in 1:length(fit$gamma1)) {
coef[count] <- fit$gamma1[j]
coefSE[count] <- fit$segamma1[j]
count <- count + 1
}
for (j in 1:length(fit$gamma2)) {
coef[count] <- fit$gamma2[j]
coefSE[count] <- fit$segamma2[j]
count <- count + 1
}
for (j in 1:length(fit$alpha1)) {
coef[count] <- fit$alpha1[j]
coefSE[count] <- fit$sealpha1[j]
count <- count + 1
}
for (j in 1:length(fit$alpha2)) {
coef[count] <- fit$alpha2[j]
coefSE[count] <- fit$sealpha2[j]
count <- count + 1
}
for (j in 1:length(fit$vee1)) {
coef[count] <- fit$vee1[j]
coefSE[count] <- fit$sevee1[j]
count <- count + 1
}
for (j in 1:length(fit$vee2)) {
coef[count] <- fit$vee2[j]
coefSE[count] <- fit$sevee2[j]
count <- count + 1
}
for (j in 1:nrow(fit$Sig)) {
coef[count] <- fit$Sig[j, j]
coefSE[count] <- fit$seSig[j, j]
count <- count + 1
}
for (i in 1:(nrow(fit$Sig)-1)) {
for (j in 0:(nrow(fit$Sig)-i-1)) {
coef[count] <- fit$Sig[j+1,i+j+1]
coefSE[count] <- fit$seSig[j+1,i+j+1]
count <- count + 1
}
}
coef[count] <- time
count <- count + 1
coef[count] <- fit$iter
coef <- list(coef, coefSE, rate)
names(coef) <- c("coef", "coefSE", "rate")
return(coef)
}
}
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