@@ -54,12 +54,12 @@ predictionRegion(object, copyNumber)

 %% ~Make other sections like Warning with \section{Warning }{....} ~

 \seealso{

-  \code{\link{calculatePosteriorMean}},

+%  \code{\link{calculatePosteriorMean}},

   \code{\link{posteriorProbability}}, \code{\link{genotypes}}

 }

 \examples{

-data(sample.CNSet)

-pr <- predictionRegion(sample.CNSet, copyNumber=0:4)

+data(cnSetExample)

+pr <- predictionRegion(cnSetExample, copyNumber=0:4)

 names(pr)

 ## bivariate normal prediction region for NULL genotype (homozygous deletion)

 str(pr[["NULL"]])

@@ -36,10 +36,10 @@ predictionRegion(object, copyNumber)

   A list named by the genotype.  `NULL' refers to copy number zero, `A'

   is a hemizygous deletion, etc. Each element is a list of the means

-  (mu) and covariance (cov) for each marker.  The covariance for each

-  marker is stored as a vector in the order variance A, correlation,

-  variance B. For nonpolymorphic markers, only the first mu and the

-  first variance are used.

+  (mu) and covariance (cov) for each marker stored as an array.  For

+  `mu', the dimensions of the array are marker x allele (A or B) x

+  batch.  For `cov', the dimensions of the array are marker x 3

+  (varA, cor, and varB) x batch.

 }

@@ -54,13 +54,17 @@ predictionRegion(object, copyNumber)

 %% ~Make other sections like Warning with \section{Warning }{....} ~

 \seealso{

-

+  \code{\link{calculatePosteriorMean}},

+  \code{\link{posteriorProbability}}, \code{\link{genotypes}}

 }

 \examples{

 data(sample.CNSet)

-pr <- predictionRegion(cnSet, copyNumber=0:4)

+pr <- predictionRegion(sample.CNSet, copyNumber=0:4)

+names(pr)

+## bivariate normal prediction region for NULL genotype (homozygous deletion)

+str(pr[["NULL"]])

 }

 % Add one or more standard keywords, see file 'KEYWORDS' in the

 % R documentation directory.

-\keyword{ ~kwd1 }

-\keyword{ ~kwd2 }% __ONLY ONE__ keyword per line

+\keyword{distribution}

+\keyword{list}

\ No newline at end of file

new file mode 100644

@@ -0,0 +1,66 @@

+\name{predictionRegion}

+\alias{predictionRegion}

+\alias{predictionRegion,CNSet,integer-method}

+\title{Prediction regions for integer copy number}

+\description{Bivariate normal prediction regions for integer copy

+  number.  Copy numbers 0-4 allowed.}

+\usage{

+predictionRegion(object, copyNumber)

+}

+%- maybe also 'usage' for other objects documented here.

+\arguments{

+  \item{object}{A \code{CNSet} object.}

+  \item{copyNumber}{Integer vector.  0-4 allowed.}

+}

+\details{

+

+  We fit a linear regression for each allele to the diallic genotype

+  cluster medians.  Denoting the background and slope by nu and phi,

+  respectively, the mean for the bivariate normal prediction region is

+  given by

+

+  mu_A = nu_A + CA * phi_A

+

+  and

+

+  mu_B nu_B + CB * phi_B

+

+  The variance and correlation of the normalized intensities is

+  estimated from the diallelic genotype clusters AA, AB, and BB on the

+  log-scale.  For copy number not equal to two, we assume that the

+  variance is approximately the same for copy number not equal to 2.

+

+}

+

+\value{

+

+  A list named by the genotype.  `NULL' refers to copy number zero, `A'

+  is a hemizygous deletion, etc. Each element is a list of the means

+  (mu) and covariance (cov) for each marker.  The covariance for each

+  marker is stored as a vector in the order variance A, correlation,

+  variance B. For nonpolymorphic markers, only the first mu and the

+  first variance are used.

+

+}

+

+

+\references{

+  Scharpf et al., 2011, Biostatistics.

+}

+\author{

+  R. Scharpf

+}

+

+%% ~Make other sections like Warning with \section{Warning }{....} ~

+

+\seealso{

+

+}

+\examples{

+data(sample.CNSet)

+pr <- predictionRegion(cnSet, copyNumber=0:4)

+}

+% Add one or more standard keywords, see file 'KEYWORDS' in the

+% R documentation directory.

+\keyword{ ~kwd1 }

+\keyword{ ~kwd2 }% __ONLY ONE__ keyword per line