simProc

This is a function to simulate a black box process for teaching the use of designed experiments.
The optimal factor settings can be found using a sequential assembly strategy i.e. apply a 2^k factorial design first,
calculate the path of the steepest ascent, again apply a 2^k factorial design and augment a star portion
to find the optimal factor settings. Of course, other strategies are possible.

A comprehensive suite of statistical tools for Quality Management, designed around the Define, Measure, Analyze, Improve, and Control (DMAIC) cycle used in Six Sigma methodology. Based on the discontinued CRAN package 'qualitytools', this package refactors its original design by incorporating 'R6' object-oriented programming for increased flexibility and performance. It replaces traditional graphics with modern, interactive visualizations using 'ggplot2' and 'plotly'. Built on 'tidyverse' principles, it simplifies data manipulation and visualization, offering an intuitive approach to quality science.

Fabian Encarnacion

r6qualitytools

R6-Based Statistical Methods for Quality Science

Andrea Barahona

Miguel Flores

Javier Tarrio-Saavedra

Salvador Naya

simProc function

<dl><dt>x1</dt>
<dd>numeric vector containing the values for factor 1.</dd>
<dt>x2</dt>
<dd>numeric vector containing the values for factor 2.</dd>
<dt>x3</dt>
<dd>numeric vector containing the values for factor 3.</dd>
<dt>noise</dt>
<dd>logical value deciding whether noise should be added or not. Default setting is <code>TRUE</code>.</dd></dl>

Arguments

simProc: Simulated Process — simProc

<dl>

<dt>x1</dt>
<dd>numeric vector containing the values for factor 1.</dd>


<dt>x2</dt>
<dd>numeric vector containing the values for factor 2.</dd>


<dt>x3</dt>
<dd>numeric vector containing the values for factor 3.</dd>


<dt>noise</dt>
<dd>logical value deciding whether noise should be added or not. Default setting is <code>TRUE</code>.</dd>

</dl>

simProc: simProc: Simulated Process

Description

Usage

Value

Arguments

Examples