Nothing
#' @name qPCR
#' @docType data
#' @title
#' qPCR Curve Analysis Methods
#' @description
#' The data set contains 4 classifiers (blocks), i.e.
#' bias, linearity, precision and resolution, for 11
#' different qPCR analysis methods. The null hypothesis
#' is that there is no preferred ranking of the method results
#' per gene for the performance parameters analyzed.
#' The rank scores were obtained by averaging results
#' across a large set of 69 genes in a biomarker data file.
#'
#' @format
#' A data frame with 4 observations on the following 11 variables.
#' \describe{
#' \item{Cy0}{a numeric vector}
#' \item{LinRegPCR}{a numeric vector}
#' \item{Standard_Cq}{a numeric vector}
#' \item{PCR_Miner}{a numeric vector}
#' \item{MAK2}{a numeric vector}
#' \item{LRE_E100}{a numeric vector}
#' \item{5PSM}{a numeric vector}
#' \item{DART}{a numeric vector}
#' \item{FPLM}{a numeric vector}
#' \item{LRE_Emax}{a numeric vector}
#' \item{FPK_PCR}{a numeric vector}
#' }
#'
#' @source
#' Data were taken from Table 2 of Ruijter et al. (2013, p. 38).
#' See also Eisinga et al. (2017, pp. 14--15).
#'
#' @references
#' Eisinga, R., Heskes, T., Pelzer, B., Te Grotenhuis, M. (2017)
#' Exact p-values for pairwise comparison of Friedman rank sums,
#' with application to comparing classifiers.
#' \emph{BMC Bioinformatics}, 18:68.
#'
#' Ruijter, J. M. et al. (2013) Evaluation of qPCR curve analysis
#' methods for reliable biomarker discovery: Bias, resolution,
#' precision, and implications, \emph{Methods} \bold{59}, 32--46.
#' @keywords datasets
NULL
Any scripts or data that you put into this service are public.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.