qda_ews: Quick Detection Analysis for Generic Early Warning Signals

In earlywarnings: Early Warning Signals for Critical Transitions in Time Series

Quick Detection Analysis for Generic Early Warning Signals

Description

Estimate autocorrelation, variance within rolling windows along a timeseries, test the significance of their trends, and reconstruct the potential landscape of the timeseries.

Usage

qda_ews(
  timeseries,
  param = NULL,
  winsize = 50,
  detrending = c("no", "gaussian", "linear", "first-diff"),
  bandwidth = NULL,
  boots = 100,
  s_level = 0.05,
  cutoff = 0.05,
  detection.threshold = 0.002,
  grid.size = 50,
  logtransform = FALSE,
  interpolate = 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.
param	values corresponding to observations in timeseries
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%.
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.
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).
boots	the number of surrogate data to generate from fitting an ARMA(p,q) model. Default is 100.
s_level	significance level. Default is 0.05.
cutoff	the cutoff value to visualize the potential landscape
detection.threshold	detection threshold for potential minima
grid.size	grid size (for potential analysis)
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).

Value

qda_ews produces three plots. The first plot contains the original data, the detrending/filtering applied and the residuals (if selected), autocorrelation and variance. For each statistic trends are estimated by the nonparametric Kendall tau correlation. The second plot, returns a histogram of the distributions of the Kendall trend statistic for autocorrelation and variance estimated on the surrogated data. Vertical lines represent the level of significance, whereas the black dots the actual trend found in the time series. The third plot is the reconstructed potential landscape in 2D. In addition, the function returns a list containing the output from the respective functions generic_RShiny (indicators); surrogates_RShiny (trends); movpotential_ews (potential analysis)

Author(s)

Vasilis Dakos, Leo Lahti, March 1, 2013 [e-mail]

References

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;

Examples

data(foldbif)
out <- qda_ews(foldbif, param = NULL, winsize = 50, 
      	          detrending='gaussian', bandwidth=NULL, 
	  boots = 10, s_level = 0.05, cutoff=0.05, 
	  detection.threshold = 0.002, grid.size = 50, 
	  logtransform=FALSE, interpolate=FALSE)

earlywarnings documentation built on Oct. 11, 2022, 1:05 a.m.