generic_ews: Description: Generic Early Warning Signals

In earlywarnings: Early Warning Signals Toolbox for Detecting Critical Transitions in Timeseries

Description

generic_ews is used to estimate statistical moments within rolling windows along a timeserie

Usage

  generic_ews(timeseries, winsize = 50,
    detrending = c("no", "gaussian", "linear", "first-diff"),
    bandwidth = NULL, logtransform = FALSE,
    interpolate = FALSE, AR_n = FALSE,
    powerspectrum = FALSE)

Arguments

timeseries	a numeric vector of the observed univariate timeseries values or a numeric matrix where the first column represents the time index and the second the observed timeseries values. Use vectors/matrices with headings. If the powerspectrum is to be plotted as well, the timeseries lenght should be even number.
winsize	is the size of the rolling window expressed as percentage of the timeseries length (must be numeric between 0 and 100). Default is 50%.
bandwidth	is the bandwidth used for the Gaussian kernel when gaussian filtering is applied. It is expressed as percentage of the timeseries length (must be numeric between 0 and 100). Alternatively it can be given by the bandwidth selector bw.nrd0 (Default).
detrending	the timeseries can be detrended/filtered prior to analysis. There are four options: gaussian filtering, linear detrending and first-differencing. Default is no detrending.
logtransform	logical. If TRUE data are logtransformed prior to analysis as log(X+1). Default is FALSE.
interpolate	logical. If TRUE linear interpolation is applied to produce a timeseries of equal length as the original. Default is FALSE (assumes there are no gaps in the timeseries).
AR_n	logical. If TRUE the best fitted AR(n) model is fitted to the data. Default is FALSE.
powerspectrum	logical. If TRUE the power spectrum within each rolling window is plotted. Default is FALSE.

Details

see ref below

Value

generic_ews returns a matrix that contains:

tim	the time index.
ar1	the autoregressive coefficient ar(1) of a first order AR model fitted on the data within the rolling window.
sd	the standard deviation of the data estimated within each rolling window.
sk	the skewness of the data estimated within each rolling window.
kurt	the kurtosis of the data estimated within each rolling window.
cv	the coefficient of variation of the data estimated within each rolling window.
returnrate	the return rate of the data estimated as 1-ar(1) cofficient within each rolling window.
densratio	the density ratio of the power spectrum of the data estimated as the ratio of low frequencies over high frequencies within each rolling window.
acf1	the autocorrelation at first lag of the data estimated within each rolling window.

In addition, generic_ews returns three plots. The first plot contains the original data, the detrending/filtering applied and the residuals (if selected), and all the moment statistics. For each statistic trends are estimated by the nonparametric Kendall tau correlation. The second plot, if asked, quantifies resilience indicators fitting AR(n) selected by the Akaike Information Criterion. The third plot, if asked, is the power spectrum estimated by spec.ar for all frequencies within each rolling window.

Author(s)

Vasilis Dakos [e-mail]

References

Ives, A. R. (1995). "Measuring resilience in stochastic systems." Ecological Monographs 65: 217-233

Dakos, V., et al (2008). "Slowing down as an early warning signal for abrupt climate change." Proceedings of the National Academy of Sciences 105(38): 14308-14312

Dakos, V., et al (2012)."Methods for Detecting Early Warnings of Critical Transitions in Time Series Illustrated Using Simulated Ecological Data." PLoS ONE 7(7): e41010. doi:10.1371/journal.pone.0041010

See Also

generic_ews; ddjnonparam_ews; bdstest_ews; sensitivity_ews; surrogates_ews; ch_ews; movpotential_ews; livpotential_ews

earlywarnings documentation built on May 2, 2019, 5:48 p.m.