R/elnormAltSinglyCensored.R

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

elnormAltSinglyCensored <-
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)
    }
    n.cen <- sum(censored)
    if (n.cen == 0) 
        stop("No censored values indicated by 'censored'.")
    if (any(x <= 0)) 
        stop("All values of 'x' (including censored ones) must be positive")
    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", "impute.w.qq.reg.w.cen.level", "impute.w.mle", 
        "half.cen.level"))
    if (method == "rROS") 
        method <- "impute.w.qq.reg"
    censoring.side <- match.arg(censoring.side, c("left", "right"))
    T1 <- unique(x[censored])
    if (length(T1) > 1) 
        stop(paste("More than one censoring level.  Use 'elnormAltMultiplyCensored'."))
    if (censoring.side == "left") {
        if (T1 > min(x.no.cen)) 
            stop(paste("For singly left-censored data,", "all uncensored observations must be bigger than", 
                "or equal to the censoring level. ", "Use elnormAltMultiplyCensored."))
    }
    else {
        if (T1 < max(x.no.cen)) 
            stop(paste("For singly right-censored data,", "all uncensored observations must be less than", 
                "or equal to the censoring level. ", "Use elnormAltMultiplyCensored."))
    }
    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"))
    pivot.statistic <- match.arg(pivot.statistic, c("z", "t"))
    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", 
            "impute.w.qq.reg.w.cen.level", "impute.w.mle", "half.cen.level"))) 
            stop(paste("When ci.method='normal.approx', 'method' must be one of", 
                "'impute.w.qq.reg', 'impute.w.qq.reg.w.cen.level', \n\t\t\t'impute.w.mle', or 'half.cen.level'"))
        if (ci.method == "profile.likelihood" && method != "mle") 
            stop("When ci.method=\"profile.likelihood\" you must set method=\"mle\"")
    }
    est.fcn <- paste("elnormAltSinglyCensored", method, sep = ".")
    if (!ci || ci.method != "bootstrap") {
        param.ci.list <- do.call(est.fcn, list(x = x, censored = censored, 
            N = N, T1 = T1, 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, T1 = T1, n.cen = n.cen, censoring.side = censoring.side, 
            ci = FALSE, ci.method = ci.method, ci.type = ci.type, 
            conf.level = conf.level, ...))
        ci.list <- elnormAltSinglyCensored.bootstrap.ci(x = x, 
            censored = censored, N = N, T1 = T1, 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$parameters["mean"], 
            ...)
        param.ci.list <- c(param.ci.list, list(ci.obj = ci.list))
    }
    method.string <- switch(method, mle = "MLE", qmvue = "Quasi-MVUE", 
        bcmle = "Bias-corrected MLE", impute.w.qq.reg = paste("Imputation with\n", 
            space(33), "Q-Q Regression (rROS)", sep = ""), impute.w.qq.reg.w.cen.level = paste("Imputation with\n", 
            space(33), "Q-Q Regression (ROS)\n", space(33), "with Censoring Level", 
            sep = ""), impute.w.mle = "Imputation with MLE", 
        half.cen.level = "Half Censoring Level")
    ret.list <- list(distribution = "Lognormal", sample.size = N, 
        censoring.side = censoring.side, censoring.levels = T1, 
        percent.censored = (100 * n.cen)/N, parameters = param.ci.list$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
}