acf {stats} | 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.

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, ...) ## Default S3 method: 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, ...) acf.obj[i, j]

`x, y` |
a univariate or multivariate (not `ccf` ) numeric time
series object or a numeric vector or matrix. |

`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? |

`...` |
further arguments to be passed to `plot.acf` . |

`acf.obj` |
an object of class `"acf"` resulting from a call
to `acf` . |

`i` |
a set of lags to retain. |

`j` |
a set of series to retain. |

For `type`

= `"correlation"`

and `"covariance"`

, the
estimates are based on the sample covariance.

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"`

.

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`

.

Original: Paul Gilbert, Martyn Plummer.
Extensive modifications and univariate case of `pacf`

by
B.D. Ripley.

`plot.acf`

, `ARMAacf`

for the exact
autocorrelations of a given ARMA process.

## 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) # just the cross-correlations. presidents # contains missing values acf(presidents, na.action = na.pass) pacf(presidents, na.action = na.pass)

[Package *stats* version 2.2.1 Index]