R/elnormAltMultiplyCensored.R

In EnvStats: Package for Environmental Statistics, Including US EPA Guidance

elnormAltMultiplyCensored <-
function (x, censored, method = "mle", censoring.side = "left", 
    ci = FALSE, ci.method = "profile.likelihood", ci.type = "two-sided", 
    conf.level = 0.95, n.bootstraps = 1000, pivot.statistic = "z", 
    ...) 
{
    if (!is.vector(x, mode = "numeric")) 
        stop("'x' must be a numeric vector")
    if (!is.vector(censored, mode = "numeric") && !is.vector(censored, 
        mode = "logical")) 
        stop("'censored' must be a logical or numeric vector")
    if (length(censored) != length(x)) 
        stop("'censored' must be the same length as 'x'")
    data.name <- deparse(substitute(x))
    censoring.name <- deparse(substitute(censored))
    if ((bad.obs <- sum(!(ok <- is.finite(x) & is.finite(as.numeric(censored))))) > 
        0) {
        x <- x[ok]
        censored <- censored[ok]
        warning(paste(bad.obs, "observations with NA/NaN/Inf in 'x' and 'censored' removed."))
    }
    if (is.numeric(censored)) {
        if (!all(censored == 0 | censored == 1)) 
            stop(paste("When 'censored' is a numeric vector, all values of", 
                "'censored' must be 0 (not censored) or 1 (censored)."))
        censored <- as.logical(censored)
    }
    if (any(x <= 0)) 
        stop("All values of 'x' (including censored ones) must be positive")
    n.cen <- sum(censored)
    if (n.cen == 0) 
        stop("No censored values indicated by 'censored'.")
    x.no.cen <- x[!censored]
    if (length(unique(x.no.cen)) < 2) 
        stop("'x' must contain at least 2 non-missing, uncensored, distinct values.")
    N <- length(x)
    method <- match.arg(method, c("mle", "qmvue", "bcmle", "rROS", 
        "impute.w.qq.reg", "half.cen.level"))
    if (method == "rROS") 
        method <- "impute.w.qq.reg"
    censoring.side <- match.arg(censoring.side, c("left", "right"))
    x.cen <- x[censored]
    c.vec <- table(x.cen)
    cen.levels <- sort(unique(x.cen))
    K <- length(cen.levels)
    if (method == "half.cen.level" && censoring.side == "right") 
        stop(paste("The method 'half.cen.level' is applicable only for", 
            "left-censored data"))
    ci.method <- match.arg(ci.method, c("delta", "cox", "normal.approx", 
        "bootstrap", "profile.likelihood"))
    ci.type <- match.arg(ci.type, c("two-sided", "lower", "upper"))
    if (ci && ci.method == "profile.likelihood" && method != 
        "mle") 
        stop("When ci.method=\"profile.likelihood\" you must set method=\"mle\"")
    pivot.statistic <- match.arg(pivot.statistic, c("z", "t"))
    if (K == 1 && min(x.no.cen) > cen.levels) {
        ret.list <- elnormAltSinglyCensored(x = x, censored = censored, 
            method = method, censoring.side = censoring.side, 
            ci = ci, ci.method = ci.method, ci.type = ci.type, 
            conf.level = conf.level, n.bootstraps = n.bootstraps, 
            pivot.statistic = pivot.statistic, ...)
        ret.list$data.name <- data.name
        ret.list$censoring.name <- censoring.name
        ret.list$bad.obs <- bad.obs
    }
    else {
        if (ci) {
            if (any(ci.method == c("delta", "cox")) && !any(method == 
                c("mle", "qmvue", "bcmle"))) 
                stop(paste("When ci.method='delta' or ci.method='cox',", 
                  "'method' must be one of 'mle', 'qmvue', or 'bcmle'"))
            if (ci.method == "normal.approx" && !any(method == 
                c("impute.w.qq.reg", "half.cen.level"))) 
                stop(paste("When ci.method='normal.approx', 'method' must be one of", 
                  "'impute.w.qq.reg', or 'half.cen.level'"))
        }
        est.fcn <- paste("elnormAltMultiplyCensored", method, 
            sep = ".")
        if (!ci || ci.method != "bootstrap") {
            param.ci.list <- do.call(est.fcn, list(x = x, censored = censored, 
                N = N, cen.levels = cen.levels, K = K, c.vec = c.vec, 
                n.cen = n.cen, censoring.side = censoring.side, 
                ci = ci, ci.method = ci.method, ci.type = ci.type, 
                conf.level = conf.level, pivot.statistic = pivot.statistic, 
                ...))
        }
        else {
            param.ci.list <- do.call(est.fcn, list(x = x, censored = censored, 
                N = N, cen.levels = cen.levels, K = K, c.vec = c.vec, 
                n.cen = n.cen, censoring.side = censoring.side, 
                ci = FALSE, ci.method = ci.method, ci.type = ci.type, 
                conf.level = conf.level, ...))
            ci.list <- elnormAltMultiplyCensored.bootstrap.ci(x = x, 
                censored = censored, N = N, cen.levels = cen.levels, 
                K = K, c.vec = c.vec, censoring.side = censoring.side, 
                est.fcn = est.fcn, ci.type = ci.type, conf.level = conf.level, 
                n.bootstraps = n.bootstraps, obs.mean = param.ci.list$parameters["mean"], 
                ...)
            param.ci.list <- c(param.ci.list, list(ci.obj = ci.list))
        }
        method.string <- switch(method, mle = "MLE", qmvue = "Quasi-MVUE", 
            bcmle = "Bias-Corrected MLE", impute.w.qq.reg = paste("Imputation with", 
                space(33), "Q-Q Regression (rROS)", sep = ""), 
            half.cen.level = "Half Censoring Level")
        ret.list <- list(distribution = "Lognormal", sample.size = N, 
            censoring.side = censoring.side, censoring.levels = cen.levels, 
            percent.censored = (100 * n.cen)/N, parameters = param.ci.list$parameters, 
            n.param.est = 2, method = method.string, data.name = data.name, 
            censoring.name = censoring.name, bad.obs = bad.obs)
        if (ci) {
            ret.list <- c(ret.list, list(interval = param.ci.list$ci.obj))
            if (!is.null(param.ci.list$var.cov.params)) 
                ret.list <- c(ret.list, list(var.cov.params = param.ci.list$var.cov.params))
        }
        oldClass(ret.list) <- "estimateCensored"
    }
    ret.list
}