acf(x, lag.max = NULL, type = c("correlation", "covariance", "partial"), plot = TRUE, na.action, ...) 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 type of acf. Allowed values are "correlation" (the default), "covariance" or "partial". |
na.action
| function to be called to handle missing values. |
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. |
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 acf
objects.
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
.
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
.
plot.acf
is based on an
uncorrelated series and should be treated with appropriate
caution. Using ci.type = "ma"
may be less potentially misleading.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.