getNormalLimitsFromList

Gets the 100p
NB: stddev used, not variance!

internal

An implementation of the adaptive forgetting factor scheme described in Bodenham and Adams (2016) <doi:10.1007/s11222-016-9684-8> which adaptively estimates the mean and variance of a stream in order to detect multiple changepoints in streaming data. The implementation is in 'C++' and uses 'Rcpp'. Additionally, implementations of the fixed forgetting factor scheme from the same paper, as well as the classic cumulative sum ('CUSUM') and exponentially weighted moving average ('EWMA') methods, are included.

Dean Bodenham

ffstream

Forgetting Factor Methods for Change Detection in Streaming Data

getNormalLimitsFromList function

<dl><dt>burnvec</dt>
<dd>A vector of unnamed values, consisting of:<dl>
 <dt>p</dt>
<dd>First component - value of threshold.</dd></dl></dd>
 <dt>mu</dt>
<dd>Second component - value of the mean.</dd>
 <dt>sd</dt>
<dd>Third component - value of the standardi
 deviation.</dd>
 
</dl>
<dt>u_new</dt>
<dd>The value of <code>u</code>, the quantity used to scale the 
forgetting factor variance.</dd>

Arguments

Compute confidence limits assuming the normal distribution — getNormalLimitsFromList

<dl>

<dt>burnvec</dt>
<dd>A vector of unnamed values, consisting of:<dl>
 <dt>p</dt>
<dd>First component - value of threshold.</dd>


 <dt>mu</dt>
<dd>Second component - value of the mean.</dd>


 <dt>sd</dt>
<dd>Third component - value of the standardi
 deviation.</dd>


 
</dl></dd>


<dt>u_new</dt>
<dd>The value of <code>u</code>, the quantity used to scale the 
forgetting factor variance.</dd>

</dl>

getNormalLimitsFromList: Compute confidence limits assuming the normal distribution

Description

Usage

Arguments