\name{cor}


\title{Time Series Correlations}


\alias{cor}
\alias{cov}
\alias{cor.timeSeries}
\alias{cov.timeSeries}


\description{
    
    Functions and methods dealing with correlations between
    'timeSeries' objects. 
    \cr
    
    \tabular{ll}{
    \code{cov} \tab Computes Covariance from a 'timeSeries' object, \cr
    \code{cor} \tab Computes Correlations from a 'timeSeries' object.}
      
}


\usage{
\method{cov}{timeSeries}(x, y = NULL, use = "all.obs", 
    method = c("pearson", "kendall", "spearman"))
    
\method{cor}{timeSeries}(x, y = NULL, use = "all.obs", 
    method = c("pearson", "kendall", "spearman"))
}


\arguments{
        
    \item{method}{ 
        a character string indicating which correlation coefficient (or 
        covariance) is to be computed. One of \code{"pearson"} (default), 
        \code{"kendall"}, or \code{"spearman"}, can be abbreviated.
        }
    \item{use}{
        an optional character string giving a method for computing
        covariances in the presence of missing values. This must 
        be (an abbreviation of) one of the strings \code{"all.obs"},
        \code{"complete.obs"} or \code{"pairwise.complete.obs"}.
        }
    \item{x}{
        an univariate object of class \code{timeSeries}.
        }
    \item{y}{
        NULL (default) or a \code{timeSeries} object with compatible 
        dimensions to \code{x}. The default is equivalent to y = x (but 
        more efficient).
        }  
       
}


\value{

    returns the covariance or correlation matrix.
        
}


\examples{
## data - 
   x = as.timeSeries(data(msft.dat))[, 1:4]
   x = 100*returnSeries(x)

## cov -
   cov(x[, "Open"], x[, "Close"])
   cov(x)
}


\keyword{chron}