Nothing
elnormAltSinglyCensored.impute.w.qq.reg.w.cen.level <-
function (x, censored, N, T1, n.cen, censoring.side, ci, ci.method = "normal.approx",
ci.type, conf.level, plot.pos.con = 0.375, ci.sample.size = N -
n.cen, lb.impute = 0, ub.impute = Inf, pivot.statistic = c("z",
"t"))
{
log.parameters <- enormSinglyCensored.qq.reg.w.cen.level(x = log(x),
censored = censored, N = N, T1 = log(T1), n.cen = n.cen,
censoring.side = censoring.side, ci = FALSE, plot.pos.con = plot.pos.con)$parameters
x.obs <- x[!censored]
index <- 1:n.cen
if (censoring.side == "right")
index <- N - index + 1
E.norm <- qnorm(ppoints(N, a = plot.pos.con))[index]
x.impute <- exp(E.norm * log.parameters[2] + log.parameters[1])
if (any(index <- x.impute < lb.impute))
x.impute[index] <- lb.impute
if (any(index <- x.impute > ub.impute))
x.impute[index] <- ub.impute
new.x <- c(x.impute, x.obs)
muhat <- mean(new.x)
sdhat <- sd(new.x)
parameters <- c(mean = muhat, cv = sdhat/muhat)
ret.list <- list(parameters = parameters, plot.pos.con = plot.pos.con)
if (ci) {
ci.method <- match.arg(ci.method)
pivot.statistic <- match.arg(pivot.statistic)
ci.obj <- ci.normal.approx(theta.hat = muhat, sd.theta.hat = sdhat/sqrt(ci.sample.size),
n = ci.sample.size, df = ci.sample.size - 1, ci.type = ci.type,
alpha = 1 - conf.level, lb = 0, test.statistic = pivot.statistic)
ci.obj$parameter <- "mean"
ret.list <- c(ret.list, list(ci.obj = ci.obj))
}
ret.list
}
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