| 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.
The generic function plot has a method for objects of class
"acf".
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"), ...)
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. |
plot |
logical. If TRUE the acf is plotted. |
type |
character string giving the type of acf to be computed.
Allowed values are
"correlation" (the default), "covariance" or
"partial". |
na.action |
function to be called to handle missing values. |
demean |
logical. Should the covariances be about the sample means? |
acf.obj |
an object of class "acf". |
ci |
coverage probability for confidence interval. Plotting of
the confidence interval is suppressed if ci is
zero or negative. |
ci.col |
colour to plot the confidence interval lines. |
ci.type |
should the confidence limits assume a white noise input or for lag k an MA(k-1) input? |
... |
graphical parameters. |
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.
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.
The confidence interval plotted in plot.acf is based on an
uncorrelated series and should be treated with appropriate
caution. Using ci.type = "ma" may be less potentially
misleading.
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.