R/ovarianCancer-data.R

In alvaro85gr/DET: Representation of DET Curve with Confidence Intervals

#' 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: NV = (V-Min)/(Max-Min) where NV is the normalized 
#' value, V$ the raw value, $Min$ the minimum intensity and $Max$ the maximum intensity. The normalization is done over 
#' all the 253 samples for all 15154 M/Z identities. After the normalization, each intensity value is to fall within the 
#' range of 0 to 1.
#'
#' @docType data
#'
#' @usage data(ovarianCancer)
#'
#' @format An object of class \code{"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
#' data(ovarianCancer)
#' responses <- data.frame(ovarianCancer$response)
#' predictors <- data.frame(
#'   n1 = as.numeric(as.numeric(ovarianCancer[[2]])),
#'   n2 = as.numeric(as.numeric(ovarianCancer[[3]]))
#' )
#' names(predictors) = c("Protein 1689","Protein 1737")
#' detcurve <- detc(responses,predictors, 
#'                  names = names(predictors),
#'                  positive = "Cancer",
#'                  title = "Proteomic patterns",
#'                  plotROC = TRUE)
#' 
"ovarianCancer"