\name{calculateSimilarity}
\alias{calculateSimilarity}
\title{Calculate similarities (distances) between a vector and the rows (columns) of a matrix}
\description{
  \code{calculateSimilarity} calculates the similarity (distance) between the submitted numeric vector and all rows respectively columns of a numerical matrix using the specified similarity (distance) measurement method. 
}
\usage{
calculateSimilarity(vector,matrix,orientation="h",distance.fun=distance.euclidian,include.values=FALSE)
}
\arguments{
  \item{vector}{The numerical vector for which the similarities should be calculated.}
  \item{matrix}{Numerical matrix. Similarities are calculated for the input vector (\code{vector} parameter) with all rows (or columns, depending on the \code{orientation} parameter) of this matrix.}
  \item{orientation}{If the vector should be compared with the rows (\code{"h"}) or columns (\code{"v"}) of the matrix.}
  \item{distance.fun}{Function to calculate the similarity.}
  \item{include.values}{Include input values in the result. If FALSE only a vector with the distances will be returned.}
}
\details{
This function is a simple and quick way to calculate similarities of a numerical vector and the columns (or rows) of a numerical matrix. \code{calculateSimilarity} can be used for example to search for genes with a common (similar) expression or regulation pattern than a input gene.
}
\references{}
\author{Johannes Rainer}

\seealso{
	\code{\link{distance.pearson}}
	\code{\link{distance.euclidian}}
	\code{\link{distance.spearman}}
	\code{\link{dbSearchSimilarPattern}}
}

\examples{
In <- runif(5)
Testset <- rbind(a=runif(5,min=-1,max=2),b=runif(5,min=0,max=2),c=runif(5))
In
Testset
calculateSimilarity(vector=In,matrix=Testset,orientation="h")

}
\keyword{data}