robacf {tsqn}

Robust autocorrelation or autocovariance function estimation

Description

This function computer and plots(by default) the robust estimates of the autocovariance or the autocorrelation function based on the Qn.

Usage

robacf(x, lag.max = NULL, type = c("correlation", "covariance"),
  plot = TRUE, na.action = na.fail, demean = TRUE, ...)

Arguments

x

a numeric vector or matrix.

lag.max

maximum lag 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. 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) or "covariance". Accepts parcial names.

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.

Value

An object of class "robacf", 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.

Author(s)

Higor Cotta, Valderio Reisen and Pascal Bondon

References

Cotta, H. and Reisen, V. A. and Bondon, P. and Stummer, W. (2017) Robust Estimation of Covariance and Correlation Functions of a Stationary Multivariate Process. To appear in 2017 25th European Signal Processing Conference (EUSIPCO 2017).

Ma, Y. and Genton, M. G. (2000) Highly robust estimation of the autocovariance function. Journal of Time Series Analysis, 21, 663–684.

Ma, Y. and Genton, M. G. (2001) Highly robust estimation of dispersion matrices. Journal of Multivariate Analysis, 78, 11–36.

Rousseeuw, P. J. and Croux, C. (1993) Alternatives to the median absolute deviation. Journal of the American Statistical Association, 88, 1273–1283.

Examples

data.set <- cbind(fdeaths,mdeaths)
robacf(data.set)
robacf(data.set,type="covariance",lag.max=10)