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 = na.fail, demean = TRUE, ...) pacf(x, lag.max = NULL, plot = TRUE, na.action = na.fail, ...) ccf(x, y, lag.max = NULL, type = c("correlation", "covariance"), plot = TRUE, na.action = na.fail, ...)
x, y |
a univariate or multivariate (not ccf ) time
series object or a numeric vector or matrix. |
lag.max |
maximum number of lags 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"
.
The lag is returned and plotted in units of time, and not numbers of observations.
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.