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. 2024 Oct 3;19(10):e0308828.
doi: 10.1371/journal.pone.0308828. eCollection 2024.

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

Li-Pang Chen  1 , Cheng-Kuan Lin  1
Affiliations

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

Li-Pang Chen et al. PLoS One. .

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.

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Conflict of interest statement

The authors have declared that no competing interests exist.

Figures

Fig 1
Fig 1. The control charts for the error-free discrete data.
The left panel is for the IC data and the right panel is for the OC data.
Fig 2
Fig 2. The control charts for the error-prone discrete data.
The left panel is for the IC data and the right panel is for the OC data.
Fig 3
Fig 3. The control charts for the error-corrected discrete data.
The left panel is for the IC data and the right panel is for the OC data.
Fig 4
Fig 4. The control charts obtained by the package qcc for the error-prone discrete data.
The left panel is for the IC data and the right panel is for the OC data.
Fig 5
Fig 5. The control charts for the error-free continuous data.
The left panel is for the IC data and the right panel is for the OC data.
Fig 6
Fig 6. The control charts for the error-prone continuous data.
The left panel is for the IC data and the right panel is for the OC data.
Fig 7
Fig 7. The control charts for the error-corrected continuous data.
left panel is for the IC data and the right panel is for the OC data.
Fig 8
Fig 8. The control charts obtained by the package qcc for the error-prone continuous data.
The left panel is for the IC data and the right panel is for the OC data.
Fig 9
Fig 9. The control charts for error-prone variables in the SECOM data.
The left panel is for the IC data and the right panel is for the OC data.
Fig 10
Fig 10. The control charts for error-corrected variables in the SECOM data.
The left column is for the IC data and the right column is for the OC data. The first row represents (π11, π00) = (0.823, 0.918); The second row is based on (π11, π00) = (0.720, 0.870); and the last row is obtained under (π11, π00) = (0.616, 0.821).
Fig 11
Fig 11. The control charts for error-prone variables in the SECOM data are obtained by the package qcc.
The left panel is for the IC data and the right panel is for the OC data.
See this image and copyright information in PMC

References

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    1. Yang S.-F. and Chen L.-P. and Lin C.-K. (2023). Adjustment of measurement error effects on the distribution-free dispersion control chart. Sustainability, 15:4337. doi: 10.3390/su15054337 - DOI
    1. Zou C. and Tsung F. (2010). Likelihood ratio-based distribution-free EWMA control charts. Journal of Quality Technology, 42, 174–196. doi: 10.1080/00224065.2010.11917815 - DOI
    1. Chen L.-P. and Muzayyanah S. and Yang S.-F. and Wang B. and Jiang T.-A., and Gan S. (2022). Monitoring process location and dispersion simultaneously using a control region. DYNA, 97, 71–78. doi: 10.6036/10115 - DOI

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