R/ovarianCancer-data.R

In DET: Representation of DET Curve with Confidence Intervals

#' Data on Ovarian Cancer (NCI PBSII Data)
#'
#' The database used correspond to proteomic spectra, generated by mass spectroscopy. This data dates from 6-19-02, 
#' and includes 91 controls (Normal) and 162 ovarian cancers. The raw spectral data of each sample contains the relative 
#' amplitude of the intensity at each molecular mass/charge (M/Z) identity. There are total 15154 M/Z identities. 
#' The intensity values were normalized according to the formula: \eqn{NV = (V-Min)/(Max-Min)} where \eqn{NV} is the normalized 
#' value, \eqn{V} the raw value, \eqn{Min} the minimum intensity and \eqn{Max} the maximum intensity. The normalization is done over 
#' all the 253 samples for all 15154 M/Z identities. After the normalization, each intensity value falls within the 
#' range of 0 to 1.
#'
#' @docType data
#'
#' @usage data(ovarianCancer)
#'
#' @format An object of class "data.frame".
#'
#' @keywords datasets ovarian cancer
#'
#' @references Emanuel F Petricoin et al. (2002) The Lancet 359:572-577
#' (\href{http://leo.ugr.es/elvira/DBCRepository/OvarianCancer/OvarianCancer-NCI-PBSII.html}{PubMed})
#'
#' @source \href{http://leo.ugr.es/elvira/DBCRepository/OvarianCancer/Ovarian-PBSII-061902.zip}{ZIP Archive}
#'
#' @examples
#' library(DET)
#' data(ovarianCancer)
#' response = as.factor(ovarianCancer$response)
#' predictors = matrix(c(as.numeric(ovarianCancer[[2]]),
#'                       as.numeric(ovarianCancer[[3]])), ncol = 2)
#' colnames(predictors) = c("Protein 1689", "Protein 1737")
#' detCurves =
#'   detc(
#'     response,
#'     predictors,
#'     names = colnames(predictors),
#'     positive = "Cancer"
#'   )
#' plot(detCurves, main = "Proteomic patterns")
#' 
"ovarianCancer"