lsp: Lomb-Scargle Periodogram

Description Usage Arguments Details Value Note Author(s) References See Also Examples

View source: R/lspfunctions.R

Description

Computes the Lomb-Scargle periodogram for a time series with irregular (or regular) sampling intervals. Allows selecting a frequency range to be inspected, as well as the spacing of frequencies scanned.

Usage

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lsp(x, times = NULL, from = NULL, to = NULL, type = c("frequency", "period"),
    ofac = 1, alpha = 0.01, plot = TRUE, ...)

Arguments

x

The data to be analysed. x can be either a two-column numerical dataframe or matrix, with sampling times in columnn 1 and measurements in column 2, a single numerical vector containing measurements, or a single vector ts object (which will be converted to a numerical vector).

times

If x is a single vector, times can be provided as a numerical vector of equal length containing sampling times. If x is a vector and times is NULL, the data are assumed to be equally sampled and times is set to 1:length(x).

from

The starting frequency (or period, depending on type) to begin scanning for periodic components.

to

The highest frequency (or period, depending on type) to scan.

type

Either “frequency” (the default) or “period”. Determines the type of the periodogram x-axis.

ofac

The oversampling factor. Must be an integer>=1. Larger values of ofac lead to finer scanning of frequencies but may be time-consuming for large datasets and/or large frequency ranges (from...to).

alpha

The significance level. The periodogram plot shows a horizontal dashed line. Periodogram peaks exceeding this line can be considered significant at alpha. Defaults to 0.01. Only used if plot=TRUE.

plot

Logical. If plot=TRUE the periodogram is plotted.

...

Further graphical parameters affecting the periodogram plot.

Details

The p-value for the significance of the largest peak in the periodogram is computed from the exponential distribution, as outlined in Press et al. (1994), see below. For a more robust - but potentially time-consuming estimation of p-values see randlsp.

Significance levels in both lsp and randlsp increase with the number of frequencies inspected. Therefore, if the frequency-range of interest can be narrowed down a priori, use arguments “from” and “to” to do so.

Value

A named list with the following components:

scanned

A vector containing the frequencies/periods scanned.

power

A vector containing the normalised power corresponding to scanned frequencies/periods.

data

Names of the data vectors analysed.

n

The length of the data vector.

type

The periodogram type used, either "frequency" or "period".

ofac

The oversampling factor used.

n.out

The length of the output (powers). This can be >n if ofac >1.

alpha

The false alarm probability used.

sig.level

Powers > sig.level can be considered significant peaks at p=alpha.

peak

The maximum power in the frequency/period interval inspected.

peak.at

The frequency/period at which the maximum peak occurred.

p.value

The probability that the maximum peak occurred by chance.

Note

For a description of the properties of the Lomb-Scargle Periodogram, its computation and comparsion with other methods see Ruf, T. (1999). Function lsp uses the algorithm given by Press et al (1994). The Lomb-Scargle Periodogram was originaly proposed by Lomb N.R. (1976) and furher extended by Scargle J.D. (1982).

Author(s)

Thomas Ruf thomas.ruf@vetmeduni.ac.at based on code by Press et al (1994).

References

Lomb N.R. (1976) Least-squares frequency analysis of unequally spaced data. Astrophysics and Space Science 39:447–462

Press W.H., Teukolsky S.A., Vetterling S.T., Flannery, B.P. (1994) Numerical recipes in C: the art of scientific computing.2nd edition. Cambridge University Press, Cambridge, 994pp.

Ruf, T. (1999) The Lomb-Scargle Periodogram in Biological Rhythm Research: Analysis of Incomplete and Unequally Spaced Time-Series. Biological Rhythm Research 30: 178–201.

Scargle J.D. (1982) Studies in astronomical time series. II. Statistical aspects of spectral analysis of unevenly spaced data. The Astrophysical Journal 302: 757–763.

See Also

randlsp summary.lsp

Examples

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# ibex contains an unevenly sampled time series
data(ibex)
lsp(ibex[2:3],)
lsp(ibex$temp,times=ibex$hours,type='period',ofac=5)

# lynx contains evenly sampled data
lsp(lynx)
lynx.spec <- lsp(lynx,type='period',from=2,to=20,ofac=5)
summary(lynx.spec)

lomb documentation built on Dec. 1, 2019, 1:10 a.m.