astsa (version 2.3)

spec.ic: Estimate Spectral Density of a Time Series from AR Fit

Description

Fits an AR model to data and computes (and by default plots) the spectral density of the fitted model based on AIC (default) or BIC.

Usage

spec.ic(xdata, BIC=FALSE, order.max=NULL, main=NULL, plot=TRUE, detrend=TRUE, 
         lowess=FALSE, method=NULL, cex.main=NULL, xlab=NULL, ...)

Value

[[1]]

Matrix with columns: ORDER, AIC, BIC

[[2]]

Matrix with columns: freq, spec

Arguments

xdata

a univariate time series.

BIC

if TRUE, fit is based on BIC. If FALSE (default), fit is based on AIC.

order.max

maximum order of model to fit. Defaults (if NULL) to the minimum of 100 and 10% of the number of observations.

main

plot title. Defaults to name of series, method and chosen order.

plot

if TRUE (default) produces a graphic of the estimated AR spectrum.

detrend

if TRUE (default), detrends the data first. If FALSE, the series is demeaned.

lowess

if TRUE, detrends using lowess. Default is FALSE.

method

method of estimation - a character string specifying the method to fit the model chosen from the following: "yule-walker", "burg", "ols", "mle", "yw". Defaults to "yule-walker".

cex.main

magnification for main title; default is 1.

xlab

label for frequency axis; if NULL (default), a totally awesome label is generated for your viewing pleasure.

...

additional graphical arguments.

Author

D.S. Stoffer

Details

Uses ar to fit the best AR model based on pseudo AIC or BIC. Using method='mle' will be slow. The minimum centered AIC and BIC values and the spectral and frequency ordinates are returned silently.

References

You can find demonstrations of astsa capabilities at FUN WITH ASTSA.

The most recent version of the package can be found at https://github.com/nickpoison/astsa/.

In addition, the News and ChangeLog files are at https://github.com/nickpoison/astsa/blob/master/NEWS.md.

The webpages for the texts and some help on using R for time series analysis can be found at https://nickpoison.github.io/.

See Also

ar, spec.ar

Examples

if (FALSE) {
# AIC
spec.ic(soi)  
spec.ic(sunspotz, method='burg', col=4)   

# BIC after detrending on log scale
dog <- spec.ic(soi, BIC=TRUE, detrend=TRUE, log='y')  

# plot AIC and BIC  (dog from previous example)
tsplot(dog[[1]][,1], dog[[1]][,2:3], col=2*1:2, type='o', pch=1:2, xlab='order',
        spag=TRUE, addLegend=TRUE)  
}