window_ar.ols: Compute autocorrelation over a sliding window by a least...
In cboettig/earlywarning: Detection of Early Warning Signals for Catastrophic Bifurcations
Description
Usage
Arguments
Value
See Also
Description
Compute autocorrelation over a sliding window by a least
squares approach
Usage
1
2
window_ar.ols(X, windowsize = length(X)/2,
demean = FALSE)
Arguments
X
a numeric containing evenly sampled values from
a time series
windowsize
the size of the sliding time window (in
# of pts) used to compute the statistic.
Value
a numeric of size length(X)-windowsize of values of the
statistic calculated in the sliding window.
See Also
warningtrend, ar.ols
cboettig/earlywarning documentation built on May 13, 2019, 2:07 p.m.
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Description Usage Arguments Value See Also
Description
Compute autocorrelation over a sliding window by a least squares approach
Usage
1 2 | window_ar.ols(X, windowsize = length(X)/2,
demean = FALSE)
|
Arguments
X |
a numeric containing evenly sampled values from a time series |
windowsize |
the size of the sliding time window (in # of pts) used to compute the statistic. |
Value
a numeric of size length(X)-windowsize of values of the statistic calculated in the sliding window.
See Also
warningtrend, ar.ols
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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