decevf: Time series decomposition using eigenvector filtering (EVF)
In pastecs: Package for Analysis of Space-Time Ecological Series

decevf

R Documentation

Time series decomposition using eigenvector filtering (EVF)

Description

The eigenvector filtering decomposes the signal by applying a principal component analysis (PCA) on the original signal and a certain number of copies of it incrementally lagged, collected in a multivariate matrix. Reconstructing the signal using only the most representative eigenvectors allows filtering it.

Usage

decevf(x, type="additive", lag=5, axes=1:2)

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"`
`lag`	The maximum lag used. A PCA is run on the matrix constituted by vectors lagged from 0 to `lag`. The defaulf value is 5, but a value corresponding to no significant autocorrelation, on basis of examination of the autocorrelation plot obtained by `acf` in the library 'ts' should be used (Lag at first time the autocorrelation curve crosses significance lines multiplied by the frequency of the series).
`axes`	The principal axes to use to reconstruct the filtered signal. For instance, to use axes 2 and 4, use `axes=c(2,4)`. By default, the first two axes are considered (`axes=1:2`)

Value

a 'tsd' object

Author(s)

Frédéric Ibanez (ibanez@obs-vlfr.fr), Philippe Grosjean (phgrosjean@sciviews.org)

References

Colebrook, J.M., 1978. Continuous plankton records: zooplankton and environment, North-East Atlantic and North Sea 1948-1975. Oceanologica Acta, 1:9-23.

Ibanez, F. & J.C. Dauvin, 1988. Long-term changes (1977-1987) on a muddy fine sand Abra alba - Melinna palmate population community from the Western English Channel. J. Mar. Prog. Ser., 49:65-81.

Ibanez, F., 1991. Treatment of data deriving from the COST 647 project on coastal benthic ecology: The within-site analysis. In: B. Keegan (ed.) Space and time series data analysis in coastal benthic ecology. Pp. 5-43.

Ibanez, F. & M. Etienne, 1992. Le filtrage des séries chronologiques par l'analyse en composantes principales de processus (ACPP). J. Rech. Océanogr., 16:27-33.

Ibanez, F., J.C. Dauvin & M. Etienne, 1993. Comparaison des évolutions à long-terme (1977-1990) de deux peuplements macrobenthiques de la Baie de Morlaix (Manche Occidentale): relations avec les facteurs hydroclimatiques. J. Exp. Mar. Biol. Ecol., 169:181-214.

Examples

data(releve)
melo.regy <- regul(releve$Day, releve$Melosul, xmin=9, n=87,
        units="daystoyears", frequency=24, tol=2.2, methods="linear",
        datemin="21/03/1989", dateformat="d/m/Y")
melo.ts <- tseries(melo.regy)
acf(melo.ts)
# Autocorrelation is not significant after 0.16
# That corresponds to a lag of 0.16*24=4 (frequency=24)
melo.evf <- decevf(melo.ts, lag=4, axes=1)
plot(melo.evf, col=c(1, 4, 2))
# A superposed graph is better in the present case
plot(melo.evf, col=c(1, 4), xlab="stations", stack=FALSE, resid=FALSE,
        lpos=c(0, 60000))

pastecs documentation built on May 29, 2024, 5:56 a.m.