In practical applications of control charts the in-control state and the corresponding chart parameters are usually estimated based on some past in-control data. The estimation error then needs to be accounted for. In this paper we present an R package, spcadjust, which implements a bootstrap based method for adjusting monitoring schemes to take into account the estimation error. By bootstrapping the past data this method guarantees, with a certain probability, a conditional performance of the chart. In spcadjust the method is implement for various types of Shewhart, CUSUM and EWMA charts, various performance criteria, and both parametric and non-parametric bootstrap schemes. In addition to the basic charts, charts based on linear and logistic regression models for risk adjusted monitoring are included, and it is easy for the user to add further charts. Use of the package is demonstrated by examples.
spcadjust, surveillance, spc, qcc, IQCC, qcr, edcc, MSQC
Environmetrics, SpatioTemporal, TimeSeries
Text and figures are licensed under Creative Commons Attribution CC BY 4.0. The figures that have been reused from other sources don't fall under this license and can be recognized by a note in their caption: "Figure from ...".
For attribution, please cite this work as
Gandy & Kvaløy, "The R Journal: spcadjust: An R Package for Adjusting for Estimation Error in Control Charts", The R Journal, 2017
BibTeX citation
@article{RJ-2017-014,
author = {Gandy, Axel and Kvaløy, Jan Terje},
title = {The R Journal: spcadjust: An R Package for Adjusting for Estimation Error in Control Charts},
journal = {The R Journal},
year = {2017},
note = {https://doi.org/10.32614/RJ-2017-014},
doi = {10.32614/RJ-2017-014},
volume = {9},
issue = {1},
issn = {2073-4859},
pages = {458-476}
}