R/podc.R

In tpAUC: Estimation and Inference of Two-Way pAUC, pAUC and pODC

#' 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)
}