acf: Auto- and Cross- Covariance and -Correlation Function Estimation

Description

This function calls the acf function in the stats package and processes to drop lag-0 of the acf. It only works for univariate time series, so x below should be 1-dimensional.

Usage

acf(x, lag.max = NULL, type = c("correlation", "covariance", "partial")[1], 
plot = TRUE, na.action = na.fail, demean = TRUE, drop.lag.0 = TRUE, ...)

Arguments

a univariate or multivariate (not ccf) numeric time series object or a numeric vector or matrix, or an "acf" object.

lag.max

maximum number of lags at which to calculate the acf. Default is 10*log10(N/m) where N is the number of observations and m the number of series.

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 default) the acf is plotted.

na.action

function to be called to handle missing values. na.pass can be used.

demean

logical. Should the covariances be about the sample means?

drop.lag.0

logical. Should lag 0 be dropped

...

further arguments to be passed to plot.acf.

Value

An object of class "acf", which is a list with the following elements:
lagA three dimensional array containing the lags at which the acf is estimated.
acfAn array with the same dimensions as lag containing the estimated acf.
typeThe type of correlation (same as the type argument).
n.usedThe number of observations in the time series.
seriesThe name of the series x.
snamesThe series names for a multivariate time series.

References

~put references to the literature/web site here ~

Examples

Run this code

data(rwalk)
model1=lm(rwalk~time(rwalk))
summary(model1)
acf(rstudent(model1),main='')