earlywarnings (version 1.0.59)

ch_ews: Description: Conditional Heteroskedasticity

Description

ch_ews is used to estimate changes in conditional heteroskedasticity within rolling windows along a timeseries

Usage

ch_ews(timeseries, winsize = 10, alpha = 0.1, optim = TRUE, lags = 4, logtransform = FALSE, interpolate = FALSE)

Arguments

timeseries

a numeric vector of the observed 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.

winsize

is length of the rolling window expressed as percentage of the timeseries length (must be numeric between 0 and 100). Default is 10%.

alpha

is the significance threshold (must be numeric). Default is 0.1.

optim

logical. If TRUE an autoregressive model is fit to the data within the rolling window using AIC optimization. Otherwise an autoregressive model of specific order lags is selected.

lags

is a parameter that determines the specific order of an autoregressive model to fit the data. Default is 4.

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

ch_ews returns a matrix that contains:

time

the time index.

r.squared

the R2 values of the regressed residuals.

critical.value

the chi-square critical value based on the desired alpha level for 1 degree of freedom divided by the number of residuals used in the regression.

test.result

logical. It indicates whether conditional heteroskedasticity was significant.

ar.fit.order

the order of the specified autoregressive model- only informative if optim FALSE was selected.

In addition, ch_ews plots the original timeseries and the R2 where the level of significance is also indicated.

Details

see ref below

Arguments:

References

Seekell, D. A., et al (2011). 'Conditional heteroscedasticity as a leading indicator of ecological regime shifts.' American Naturalist 178(4): 442-451

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=ch_ews(foldbif, winsize=50, alpha=0.05, optim=TRUE, lags)