This function extracts signals from time series by means of Least Trimmed Squares regression in a moving time window.
a numeric vector or (univariate) time series object.
a positive integer defining the window width used for fitting.
a positive integer defining the trimming quantile.
a logical indicating whether the current level estimate is
evaluated at the most recent time within each time window
a logical indicating whether the level
estimations should be extrapolated to the edges of the time series.
lts.filter is suitable for extracting low
frequency components (the signal) from a time series which
may be contaminated with outliers and can contain level shifts.
For this, robust Least Trimmed Squares regression is applied to a moving
window, and the signal level is estimated by the fitted value
either at the end of each time window for online signal
extraction without time delay (
online=TRUE) or in the
centre of each time window (
lts.filter returns an object of class
An object of class
robreg.filter is a list containing the
a data frame containing the extracted signal level.
a data frame containing the corresponding slope within each time window.
In addition, the original input time series is returned as list
y, and the settings used for the analysis are
returned as the list members
Application of the function
plot to an object of class
robreg.filter returns a plot showing the original time series
with the filtered output.
Roland Fried, Karen Schettlinger and Matthias Borowski
Davies, P.L., Fried, R., Gather, U. (2004)
Robust Signal Extraction for On-Line Monitoring Data,
Journal of Statistical Planning and Inference 122,
Gather, U., Schettlinger, K., Fried, R. (2006)
Online Signal Extraction by Robust Linear Regression,
Computational Statistics 21(1),
Schettlinger, K., Fried, R., Gather, U. (2006) Robust Filters for Intensive Care Monitoring: Beyond the Running Median, Biomedizinische Technik 51(2), 49-56.
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Loading required package: robustbase Loading required package: MASS Loading required package: lattice
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