IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0308828.html

EATME: An R package for EWMA control charts with adjustments of measurement error

Author

Listed:
  • Li-Pang Chen
  • Cheng-Kuan Lin

Abstract

In this paper, we introduce an R package EATME, which is known as Exponentially weighted moving average (EWMA) control chart with Adjustments To Measurement Error. The main purpose of this package is to correct for measurement error effects in continuous or binary random variables and develop the corrected control charts based on the EWMA statistic. In addition, the corrected control charts can detect out-of-control process accurately. The package contains a function to generate synthetic data and includes functions to determine the reasonable coefficient of control limit as well as estimate average run length. Moreover, for the visualization, we also provide the control charts to show the monitoring of in-control and out-of-control process. Finally, the functions in this package are clearly demonstrated, and numerical studies show the validity of the package.

Suggested Citation

  • Li-Pang Chen & Cheng-Kuan Lin, 2024. "EATME: An R package for EWMA control charts with adjustments of measurement error," PLOS ONE, Public Library of Science, vol. 19(10), pages 1-23, October.
    • Handle: RePEc:plo:pone00:0308828
    DOI: 10.1371/journal.pone.0308828
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0308828
    Download Restriction: no
    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0308828&type=printable
    Download Restriction: no
    File URL: https://libkey.io/10.1371/journal.pone.0308828?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Li‐Pang Chen & Grace Y. Yi, 2021. "Analysis of noisy survival data with graphical proportional hazards measurement error models," Biometrics, The International Biometric Society, vol. 77(3), pages 956-969, September.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Lu, Huimin & Wang, Yilong & Bing, Heming & Wang, Shuying & Li, Niya, 2025. "Efficient regularized estimation of graphical proportional hazards model with interval-censored data," Computational Statistics & Data Analysis, Elsevier, vol. 209(C).
    2. Li-Pang Chen, 2022. "Classification and prediction for multi-cancer data with ultrahigh-dimensional gene expressions," PLOS ONE, Public Library of Science, vol. 17(9), pages 1-25, September.
    3. Jie Jian & Peijun Sang & Mu Zhu, 2024. "Two Gaussian Regularization Methods for Time-Varying Networks," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 29(4), pages 853-873, December.
    4. Li-Pang Chen, 2022. "Network-Based Discriminant Analysis for Multiclassification," Journal of Classification, Springer;The Classification Society, vol. 39(3), pages 410-431, November.
    5. Mengyue Zhang & Shishu Zhao & Shuying Wang & Xiaolin Xu, 2025. "Regression analysis of a graphical proportional hazards model for informatively left-truncated current status data," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 31(3), pages 498-542, July.
    6. Qihuang Zhang & Grace Y. Yi, 2023. "Generalized network structured models with mixed responses subject to measurement error and misclassification," Biometrics, The International Biometric Society, vol. 79(2), pages 1073-1088, June.
    7. Li‐Pang Chen, 2024. "Estimation of Graphical Models: An Overview of Selected Topics," International Statistical Review, International Statistical Institute, vol. 92(2), pages 194-245, August.
    8. Li-Pang Chen, 2022. "BOOME: A Python package for handling misclassified disease and ultrahigh-dimensional error-prone gene expression data," PLOS ONE, Public Library of Science, vol. 17(10), pages 1-23, October.
    9. Li-Pang Chen, 2026. "Variable selection via penalized ridge regression with error-prone variables," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 78(2), pages 225-261, April.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pone00:0308828. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.