MEWMA: Multivariate EWMA Control Chart

In IAcsSPCR: Data and Functions for "An Intro. to Accept. Samp. & SPC/R"

Description

Computes a MEWMA using the method of Lowry, Woodall, Champ and Rigdon. The number of variables p must be between 2 and 10, r is fixed at .1

Usage

MEWMA(X,Sigma=NULL,mu=NULL,Sigma.known=TRUE)

Arguments

X	input - this is a matrix or data frame containing the multivariate data. One line for each observation and one column for each variable or quality characteristic being monitored.
Sigma	input this is the known (or estimate from a Phase I study) covariance matrix of the variables
mu	input this is the known (or estimate from a Phase I study) mean vector of the variables
Sigma.known	input this is a logical variable, if TRUE, Sigma, and mu must be supplied, if FALSE the function will estimate them from the data in X

Value

returned list containing the upper control limit, the covariance matrix and the mean vector.

Author(s)

John Lawson

References

Lowry, Woodall, Champ and Rigdon(1992)<https://www.tandfonline.com/doi/abs/10.1080/00401706.1992.10485232.>

Examples

data(Lowry)
Sigma<-matrix(c(1, .5, .5, 1), nrow=2, ncol=2)
mu<-c(0,0)
MEWMA(Lowry,Sigma,mu,Sigma.known=TRUE)
MEWMA(Lowry,Sigma.known=FALSE)
mu5<-c(-.314,.32)
Sig5<-matrix(c(1.16893, -.3243, -.3243, 1.16893), nrow=2, ncol=2)
MEWMA(Lowry,Sig5,mu5,Sigma.known=TRUE)

Example output

Registered S3 method overwritten by 'DoE.base':
  method           from       
  factorize.factor conf.design
$name
[1] "UCL="

$value
[1] 8.66

$name
[1] "Covariance matrix="

$value
     [,1] [,2]
[1,]  1.0  0.5
[2,]  0.5  1.0

$name
[1] "mean vector"

$value
[1] 0 0

$name
[1] "UCL="

$value
[1] 8.66

$name
[1] "Covariance matrix="

$value
          x1        x2
x1 1.1352444 0.3797667
x2 0.3797667 1.1642933

$name
[1] "mean vector"

$value
   x1    x2 
0.260 1.124 

$name
[1] "UCL="

$value
[1] 8.66

$name
[1] "Covariance matrix="

$value
         [,1]     [,2]
[1,]  1.16893 -0.32430
[2,] -0.32430  1.16893

$name
[1] "mean vector"

$value
[1] -0.314  0.320

IAcsSPCR documentation built on Nov. 23, 2020, 5:07 p.m.