segmentation

Calculates the optimal positioning and number of common breakpoints for multiple signals.

Traditional methods typically detect breakpoints from individual signals, which means that when applied separately to multiple signals, the breakpoints are not aligned.
However, this package implements a common breakpoint detection approach for multiple piecewise constant signals, resulting in increased detection sensitivity and specificity.
By employing various techniques, optimal performance is ensured, and computation is accelerated. We hope that this package will be beneficial for researchers in signal processing, bioinformatics, economy, and other related fields.
The segmentation(), lambda_estimator() functions are the main functions of this package.

Xuanyu Liu

MSigSeg

Multiple SIGnal SEGmentation

Junbo Duan

segmentation function

<dl><dt>Y</dt>
<dd>An data.frame/matrix containing the data to be segmented. Each column stores a signal.</dd>
<dt>lambda</dt>
<dd>A penalty term, small value leads to large number of breakpoints, and vice versa.</dd>
<dt>flag</dt>
<dd>Logical. If True then use th PELT method. If False then use the OP method.</dd>
<dt>return_smooth_signals</dt>
<dd>Logical. If True then smoothed signals are returned.</dd></dl>

Arguments

Detecting common change points for multiple signals. — segmentation

<dl>

<dt>Y</dt>
<dd>An data.frame/matrix containing the data to be segmented. Each column stores a signal.</dd>


<dt>lambda</dt>
<dd>A penalty term, small value leads to large number of breakpoints, and vice versa.</dd>


<dt>flag</dt>
<dd>Logical. If True then use th PELT method. If False then use the OP method.</dd>


<dt>return_smooth_signals</dt>
<dd>Logical. If True then smoothed signals are returned.</dd>

</dl>

segmentation: Detecting common change points for multiple signals.

Description

Usage

Value

Arguments

Details

Examples