loop_redi

Compute the evolution of REDI over successive inputs

Implementation of the Robust Exponential Decreasing Index (REDI),
proposed in the article by Issa Moussa, Arthur Leroy et al. (2019)
<https://bmjopensem.bmj.com/content/bmjosem/5/1/e000573.full.pdf>.
The REDI represents a measure of cumulated workload, robust to missing data,
providing control of the decreasing influence of workload over time.
Various functions are provided to format data, compute REDI, and
visualise results in a simple and convenient way.

Alexia Grenouillat

REDI

Robust Exponential Decreasing Index

Arthur Leroy

loop_redi function

<dl><dt>data</dt>
<dd>A tibble or data frame, containing an <code>Input</code> column (with the
Date format) and an <code>Output</code> column. A simple vector of workload values,
pre-sorted by chronological order can also be provided.</dd>
<dt>coef</dt>
<dd>A number corresponding to the lambda coefficient, controlling the
decay of the exponential weights. Default is 0.1.</dd></dl>

Arguments

Compute the evolution of REDI over successive inputs — loop_redi

<dl>

<dt>data</dt>
<dd>A tibble or data frame, containing an <code>Input</code> column (with the
Date format) and an <code>Output</code> column. A simple vector of workload values,
pre-sorted by chronological order can also be provided.</dd>


<dt>coef</dt>
<dd>A number corresponding to the lambda coefficient, controlling the
decay of the exponential weights. Default is 0.1.</dd>

</dl>

loop_redi: Compute the evolution of REDI over successive inputs

Description

Usage

Value

Arguments

Examples