\Header{acf}{Autocovariance and Autocorrelation Function Estimation}
\alias{ccf}{acf}
\alias{pacf}{acf}
\methalias{pacf.default}{acf}
\methalias{pacf.mts}{acf}
\methalias{pacf.ts}{acf}
\alias{plot.acf}{acf}
\keyword{ts}{acf}
\begin{Description}\relax
The function \code{acf} computes (and by default plots) estimates of
the autocovariance or autocorrelation function.  Function
\code{pacf} is the function used for the partial autocorrelations.

Function \code{ccf} computes the cross-correlation or
cross-covariance of two univariate series.

The generic function \code{plot} has a method for \code{acf} objects.\end{Description}
\begin{Usage}
\begin{verbatim}
acf(x, lag.max = NULL,
    type = c("correlation", "covariance", "partial"),
    plot = TRUE, na.action, demean = TRUE, ...)
pacf(x, lag.max = NULL, plot = TRUE, na.action, ...)
ccf(x, y, lag.max = NULL, type = c("correlation", "covariance"),
    plot = TRUE,na.action, ...)

plot.acf(acf.obj, ci=0.95, ci.col="blue", ci.type=c("white", "ma"), ...)
\end{verbatim}
\end{Usage}
\begin{Arguments}
\begin{ldescription}
\item[\code{x, y}] a univariate or multivariate (not \code{ccf}) time
series object or a numeric vector or matrix.
\item[\code{lag.max}] maximum lag at which to calculate the acf.  Default
is \eqn{10\log_{10}(N)}{10*log10(N)} where \eqn{N}{} is the number
of observations.
\item[\code{plot}] logical. If \code{TRUE} the acf is plotted.
\item[\code{type}] character string giving the type of acf to be computed.
Allowed values are
\code{"correlation"} (the default), \code{"covariance"} or
\code{"partial"}.
\item[\code{na.action}] function to be called to handle missing values.
\item[\code{demean}] logical. Should the covariances be about the sample means?
\item[\code{acf.obj}] an object of class \code{acf}.
\item[\code{ci}] coverage probability for confidence interval.  Plotting of
the confidence interval is suppressed if \code{ci} is
zero or negative.
\item[\code{ci.col}] colour to plot the confidence interval lines.
\item[\code{ci.type}] should the confidence limits assume a white noise
input or for lag \code{k} an MA(\code{k-1}) input?
\item[\code{...}] graphical parameters.
\end{ldescription}
\end{Arguments}
\begin{Details}\relax
For \code{type} = \code{"correlation"} and \code{"covariance"}, the
estimates are based on the sample covariance.

The partial correlation coefficient is estimated by fitting
autoregressive models of successively higher orders up to
\code{lag.max}.\end{Details}
\begin{Value}
An object of class \code{acf}, which is a list with the following
elements:

\begin{ldescription}
\item[\code{lag}] A three dimensional array containing the lags at which
the acf is estimated.
\item[\code{acf}] An array with the same dimensions as \code{lag}
containing the estimated acf.
\item[\code{type}] The type of correlation (same as the \code{type} argument).
\item[\code{n.used}] The number of observations in the time series.
\item[\code{series}] The name of the series \code{x}.
\item[\code{snames}] The series names for a multivariate time series.
\end{ldescription}


The result is returned invisibly if \code{plot} is \code{TRUE}.\end{Value}
\begin{Note}\relax
The confidence interval plotted in \code{plot.acf} is based on an
\emph{uncorrelated} series and should be treated with appropriate
caution. Using \code{ci.type = "ma"} may be less potentially
misleading.\end{Note}
\begin{Author}\relax
Original: Paul Gilbert, Martyn Plummer.  Extensive modifications
and univariate case of \code{pacf} by B.D. Ripley.\end{Author}
\begin{Examples}
\begin{ExampleCode}
## Examples from Venables & Ripley
data(lh)
acf(lh)
acf(lh, type="covariance")
pacf(lh)

data(UKLungDeaths)
acf(ldeaths)
acf(ldeaths, ci.type="ma")
acf(ts.union(mdeaths, fdeaths))
ccf(mdeaths, fdeaths) # just the cross-correlations.
\end{ExampleCode}
\end{Examples}

