--- a
+++ b/man/predictionAccuracyByCv.Rd
@@ -0,0 +1,49 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/pRRophetic.R
+\name{predictionAccuracyByCv}
+\alias{predictionAccuracyByCv}
+\title{Cross validation on training dataset}
+\usage{
+predictionAccuracyByCv(
+  trainingExprData,
+  trainingPtype,
+  testExprData = -1,
+  cvFold = -1,
+  powerTransformPhenotype = TRUE,
+  batchCorrect = "eb",
+  removeLowVaryingGenes = 0.2,
+  minNumSamples = 10,
+  selection = 1
+)
+}
+\arguments{
+\item{trainingExprData}{The training data. A matrix of expression levels, rows contain genes and columns contain samples, "rownames()" must be specified and must contain the same type of gene ids as "testExprData"}
+
+\item{trainingPtype}{The known phenotype for "trainingExprData". A numeric vector which MUST be the same length as the number of columns of "trainingExprData".}
+
+\item{testExprData}{The test data where the phenotype will be estimted. It is a matrix of expression levels, rows contain genes and columns contain samples, "rownames()" must be specified and must contain the same type of gene ids as "trainingExprData".}
+
+\item{cvFold}{Specify the "fold" requried for cross validation. "-1" will do leave one out cross validation (LOOCV)}
+
+\item{powerTransformPhenotype}{Should the phenotype be power transformed before we fit the regression model? Default to TRUE, set to FALSE if the phenotype is already known to be highly normal.}
+
+\item{batchCorrect}{How should training and test data matrices be homogenized. Choices are "eb" (default) for ComBat, "qn" for quantiles normalization or "none" for no homogenization.}
+
+\item{removeLowVaryingGenes}{What proportion of low varying genes should be removed? 20 precent be default}
+
+\item{minNumSamples}{How many training and test samples are requried. Print an error if below this threshold}
+
+\item{selection}{How should duplicate gene ids be handled. Default is -1 which asks the user. 1 to summarize by their or 2 to disguard all duplicates.}
+}
+\value{
+An object of class "pRRopheticCv", which is a list with two members, "cvPtype" and "realPtype", which correspond to the cross valiation predicted phenotype and the  user provided measured phenotype respectively.
+}
+\description{
+This function does cross validation on a training set to estimate prediction accuracy on a training set.
+If the actual test set is provided, the two datasets can be subsetted and homogenized before the
+cross validation analysis is preformed. This may improve the estimate of prediction accuracy.
+}
+\author{
+Paul Geeleher, Nancy Cox, R. Stephanie Huang
+}
+\keyword{internal}