\HeaderA{table.Correlation}{calculate correlalations of multicolumn data}{table.Correlation}
\keyword{ts}{table.Correlation}
\keyword{multivariate}{table.Correlation}
\keyword{distribution}{table.Correlation}
\keyword{models}{table.Correlation}
\begin{Description}\relax
This is a wrapper for calculating correlation and significance against each column of the data provided.
\end{Description}
\begin{Usage}
\begin{verbatim}
table.Correlation(Ra, Rb, ...)
\end{verbatim}
\end{Usage}
\begin{Arguments}
\begin{ldescription}
\item[\code{Ra}] a vector of returns to test, e.g., the asset to be examined 
\item[\code{Rb}] a matrix, data.frame, or timeSeries of benchmark(s) to test the asset against. 
\item[\code{...}] any other passthru parameters to \code{\LinkA{cor.test}{cor.test}} 
\end{ldescription}
\end{Arguments}
\begin{Value}
A dataframe with correlation and significance against each column of the data provided as \code{Rb}.
\end{Value}
\begin{Author}\relax
Peter Carl
\end{Author}
\begin{SeeAlso}\relax
\code{\LinkA{cor.test}{cor.test}}
\end{SeeAlso}
\begin{Examples}
\begin{ExampleCode}
# First we load the data
data(managers)
table.Correlation(managers[,1:6],managers[,7:8])
ctable = table.Correlation(managers[,1:6],managers[,8,drop=FALSE], conf.level=.99)
dotchart(ctable[,1],labels=rownames(ctable),xlim=c(-1,1))
\end{ExampleCode}
\end{Examples}

