# 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

 `1` ```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.

John Lawson

## References

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

## Examples

 ```1 2 3 4 5 6 7 8``` ```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
 "UCL="

\$value
 8.66

\$name
 "Covariance matrix="

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

\$name
 "mean vector"

\$value
 0 0

\$name
 "UCL="

\$value
 8.66

\$name
 "Covariance matrix="

\$value
x1        x2
x1 1.1352444 0.3797667
x2 0.3797667 1.1642933

\$name
 "mean vector"

\$value
x1    x2
0.260 1.124

\$name
 "UCL="

\$value
 8.66

\$name
 "Covariance matrix="

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

\$name
 "mean vector"

\$value
 -0.314  0.320
```

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