| 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 acf objects.
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.
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.