# acf

##### Auto- and Cross- Covariance and -Correlation Function Estimation

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.

- Keywords
- ts

##### Usage

```
acf(x, lag.max = NULL,
type = c("correlation", "covariance", "partial"),
plot = TRUE, na.action = na.fail, demean = TRUE, …)
```pacf(x, lag.max, plot, na.action, …)

# S3 method for default
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, …)

# S3 method for acf
[(x, i, j)

##### Arguments

- x, y
a univariate or multivariate (not

`ccf`

) numeric time series object or a numeric vector or matrix, or an`"acf"`

object.- lag.max
maximum lag at which to calculate the acf. Default is \(10\log_{10}(N/m)\) where \(N\) is the number of observations and \(m\) the number of series. Will be automatically limited to one less than the number of observations in the series.

- type
character string giving the type of acf to be computed. Allowed values are

`"correlation"`

(the default),`"covariance"`

or`"partial"`

. Will be partially matched.- 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?

- …
further arguments to be passed to

`plot.acf`

.- i
a set of lags (time differences) to retain.

- j
a set of series (names or numbers) to retain.

##### Details

For `type`

= `"correlation"`

and `"covariance"`

, the
estimates are based on the sample covariance. (The lag 0 autocorrelation
is fixed at 1 by convention.)

By default, no missing values are allowed. If the `na.action`

function passes through missing values (as `na.pass`

does), the
covariances are computed from the complete cases. This means that the
estimate computed may well not be a valid autocorrelation sequence,
and may contain missing values. Missing values are not allowed when
computing the PACF of a multivariate time series.

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.

There are `print`

and subsetting methods for objects of class
`"acf"`

.

##### Value

An object of class `"acf"`

, which is a list with the following
elements:

A three dimensional array containing the lags at which the acf is estimated.

An array with the same dimensions as `lag`

containing
the estimated acf.

The type of correlation (same as the `type`

argument).

The number of observations in the time series.

The name of the series `x`

.

The series names for a multivariate time series.

The lag k value returned by ccf(x, y) estimates the correlation between x[t+k] and y[t].

The result is returned invisibly if plot is TRUE.

##### References

Venables, W. N. and Ripley, B. D. (2002)
*Modern Applied Statistics with S*. Fourth Edition.
Springer-Verlag.

(This contains the exact definitions used.)

##### See Also

`plot.acf`

, `ARMAacf`

for the exact
autocorrelations of a given ARMA process.

##### Examples

`library(stats)`

```
# NOT RUN {
require(graphics)
## Examples from Venables & Ripley
acf(lh)
acf(lh, type = "covariance")
pacf(lh)
acf(ldeaths)
acf(ldeaths, ci.type = "ma")
acf(ts.union(mdeaths, fdeaths))
ccf(mdeaths, fdeaths, ylab = "cross-correlation")
# (just the cross-correlations)
presidents # contains missing values
acf(presidents, na.action = na.pass)
pacf(presidents, na.action = na.pass)
# }
```

*Documentation reproduced from package stats, version 3.5.0, License: Part of R 3.5.0*