.png){kind=icon}
.png){kind=icon}
Return
PH1XBAR: An R package for univariate Phase I Shewhart-type control charts for the mean
.png){kind=icon}
PRE
AI
Y
Yuhui Yao*C
Chase HolcombeT
Tyler ThomasS
S. ChakrabortiJ
Jason Parton DOI：10.1080/00224065.2026.2641154.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Control charts are used in diverse areas of applications, from manufacturing to healthcare to cybersecurity, to name a few, for statistical process monitoring and control. Among these charts, Phase I control charts are designed to retrospectively monitor process data. As has been recognized in the literature, these charts are very important in practice for process analysis and improvement. They serve as a foundation for Phase II control charts that are used for prospective monitoring by providing, for example, reference data, parameter estimates, and ideas about the shape of the underlying distribution. Shewhart control charts are typically recommended in Phase I. This paper introduces the R package PH1XBAR that can be used to construct Phase I Shewhart-type control charts for the mean, which is a common problem in practice. In addition to handling i.i.d. data, this package implements some recently published methodological advances and fills a gap in the currently available software literature, by dealing with individual autocorrelated data as well as within-subgrouped equally correlated (variance components) data. The Phase I control charts are designed (constructed) for a nominal False Alarm Probability, which is the recommended in-control performance metric in Phase I. In addition to a review of the methodology, this paper introduces the contents of the package, illustrates the uses, and presents several applied data examples with relevant code. It may be observed that the proposed methods in the package are useful in many real world applications.
Keywords:
Autocorrelation
normal distribution
statistical process monitoring
subgrouped and individual data
unknown parameters
Journal
IF:
2.2
Papers:
57
Citations:
2.9K
Organization
Cited Papers