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).
Details
see ref below
Arguments:
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.
Author(s)
T. Cline, modified by V. Dakos
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