pastecs (version 1.3.21)

decmedian: Time series decomposition using a running median

Description

This is a nonlinear filtering method used to smooth, but also to segment a time series. The isolated peaks and pits are leveraged by this method.

Usage

decmedian(x, type="additive", order=1, times=1, ends="fill")

Value

a 'tsd' object

Arguments

x

a regular time series ('rts' under S+ and 'ts' under R)

type

the type of model, either type="additive" (by default), or type="multiplicative"

order

the window used for the running median corresponds to 2*order + 1

times

the number of times the running median is applied. By default, 1

ends

the method used to calculate ends. Either "NAs" (fill extremes, non-calculable values with NAs), or "fill" (fill these extremes with the closest calculable median)

Author

Frédéric Ibanez (), Philippe Grosjean ()

References

Gebski, V.J., 1985. Some properties of splicing when applied to non-linear smoothers. Comp. Stat. Data Anal., 3:151-157.

Philips, L. & R. Blomme, 1973. Analyse chronologique. Université Catholique de Louvain. Vander ed. 339 pp.

Tukey, J.W., 1977. Exploratory Data Analysis. Reading Massachusetts: Addison-Wesley.

See Also

tsd, tseries, decaverage, deccensus, decdiff, decevf, decreg, decloess

Examples

data(marbio)
ClausoB.ts <- ts(log(marbio$ClausocalanusB + 1))
ClausoB.dec <- decmedian(ClausoB.ts, order=2, times=10, ends="fill")
plot(ClausoB.dec, col=c(1, 4, 2), xlab="stations")
# This is a transect across a frontal zone:
plot(ClausoB.dec, col=c(0, 2), xlab="stations", stack=FALSE, resid=FALSE)
lines(c(17, 17), c(0, 10), col=4, lty=2)
lines(c(25, 25), c(0, 10), col=4, lty=2)
lines(c(30, 30), c(0, 10), col=4, lty=2)
lines(c(41, 41), c(0, 10), col=4, lty=2)
lines(c(46, 46), c(0, 10), col=4, lty=2)
text(c(8.5, 21, 27.5, 35, 43.5, 57), 8.7, labels=c("Peripheral Zone", "D1",
        "C", "Front", "D2", "Central Zone"))

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