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#' Partial ODC Estimation and Inference
#'
#' Estimate and infer the area of region under ODC curve with pre-specific FNR constraint (FNR-pODC). See \href{http://www3.stat.sinica.edu.tw/statistica/j27n1/j27n117/j27n117.html}{Yang et al., 2017} for details.
#'
#' @param response a factor, numeric or character vector of responses;
#' typically encoded with 0 (negative) and 1 (positive).
#' Only two classes can be used in a ROC curve. If its levels are not 0/1,
#' the first level will be defaultly regarded as negative.
#'
#' @param predictor a numeric vector of the same length than response, containing the predicted value of each observation. An ordered factor is coerced to a numeric.
#' @param threshold numeric; false negative rate (FNR) constraint.
#' @param method methods to estimate FNR-pODC. \code{MW}: Mann-Whitney statistic. \code{expect}: method in (2.2) \href{http://www.ncbi.nlm.nih.gov/pubmed/20729218}{Wang and Chang, 2011}. \code{jackknife}: jackknife method in \href{http://www3.stat.sinica.edu.tw/statistica/j27n1/j27n117/j27n117.html}{Yang et al., 2017}.
#' @param ci logic; compute the confidence interval of estimation?
#' @param cp numeric; coverage probability of confidence interval.
#' @param smooth if \code{TRUE}, the ODC curve is passed to \code{\link[pROC]{smooth}} to be smoothed.
#'
#' @details This function estimates and infers FNR partial ODC given response, predictor and pre-specific FNR constraint.
#' \code{MW}: Mann-Whitney statistic. \code{expect}: method in \href{http://www3.stat.sinica.edu.tw/statistica/j27n1/j27n117/j27n117.html}{Yang et al., 2017} adapted from \href{http://www.ncbi.nlm.nih.gov/pubmed/20729218}{Wang and Chang, 2011}. \code{jackknife}: jackknife method in \href{http://www3.stat.sinica.edu.tw/statistica/j27n1/j27n117/j27n117.html}{Yang et al., 2017}.
#' @return Estimation and Inference of FNR partial ODC.
#'
#' @author Hanfang Yang, Kun Lu, Xiang Lyu, Feifang Hu, Yichuan Zhao.
#' @seealso \code{\link[tpAUC]{podc.est}}, \code{\link[tpAUC]{podc.ci}}
#'
#' @examples
#'
#' library('pROC')
#' data(aSAH)
#' podc(aSAH$outcome, aSAH$s100b,threshold=0.9, method='expect',ci=TRUE, cp=0.95 )
#'
#' @export
#'
#' @import pROC
#' @importFrom stats ecdf approxfun runif
#' @importFrom graphics lines abline
#'
#'
podc=function(response,predictor,threshold=0.9, method='MW',ci=TRUE, cp=0.95 ,smooth=FALSE) {
podc=podc.est(response,predictor,threshold=threshold, method=method ,smooth=smooth)
l=list(podc=podc)
if (ci==TRUE){
c=podc.ci(response,predictor, cp=cp ,threshold=threshold,method=method )
l[['ci']]=c
}
return(l)
}
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