| acf {ts} | R Documentation |
The function acf computes (and by default plots) estimates of
the autocovariance or autocorrelation function. Function pacf
is the function used for the partial autocorrelations. Function
ccf computes the cross-correlation or cross-covariance of two
univariate series.
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, ...)
x, y |
a univariate or multivariate (not ccf) time
series object or a numeric vector or matrix. |
lag.max |
maximum lag at which to calculate the acf. Default is 10*log10(N) where N is the number of observations. |
type |
character string giving the type of acf to be computed.
Allowed values are
"correlation" (the default), "covariance" or
"partial". |
plot |
logical. If TRUE the acf is plotted. |
na.action |
function to be called to handle missing values. |
demean |
logical. Should the covariances be about the sample means? |
... |
further arguments to be passed to plot.acf. |
For type = "correlation" and "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
lag.max.
The generic function plot has a method for objects of class
"acf".
An object of class "acf", which is a list with the following
elements:
lag |
A three dimensional array containing the lags at which the acf is estimated. |
acf |
An array with the same dimensions as lag containing
the estimated acf. |
type |
The type of correlation (same as the type
argument). |
n.used |
The number of observations in the time series. |
series |
The name of the series x. |
snames |
The series names for a multivariate time series. |
The result is returned invisibly if plot is TRUE.
Original: Paul Gilbert, Martyn Plummer.
Extensive modifications and univariate case of pacf by
B.D. Ripley.
## 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.