Create an object of class 'mqcc' to perform multivariate statistical quality control.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 | ```
mqcc(data, type = c("T2", "T2.single"), center, cov,
limits = TRUE, pred.limits = FALSE,
data.name, labels, newdata, newlabels,
confidence.level = (1 - 0.0027)^p, rules = shewhart.rules,
plot = TRUE, ...)
## S3 method for class 'mqcc'
print(x, ...)
## S3 method for class 'mqcc'
summary(object, digits = getOption("digits"), ...)
## S3 method for class 'mqcc'
plot(x, add.stats = TRUE, chart.all = TRUE,
label.limits = c("LCL", "UCL"), label.pred.limits = c("LPL", "UPL"),
title, xlab, ylab, ylim, axes.las = 0,
digits = getOption("digits"), restore.par = TRUE, ...)
``` |

`data` |
For subgrouped data a list with a data frame or a matrix for each variable to monitor. Each row of the data frame or matrix refers to a sample or ”rationale” group. For individual observations, where each sample has a single observation, users can provide a list with a data frame or a matrix having a single column, or a data frame or a matrix where each rows refer to samples and columns to variables. See examples. | |||||||

`type` |
a character string specifying the type of chart:
| |||||||

`center` |
a vector of values to use for center of input variables. | |||||||

`cov` |
a matrix of values to use for the covariance matrix of input variables. | |||||||

`limits` |
a logical indicating if control limits (Phase I) must be computed (by default using | |||||||

`pred.limits` |
a logical indicating if prediction limits (Phase II) must be computed (by default using | |||||||

`data.name` |
a string specifying the name of the variable which appears on the plots. If not provided is taken from the object given as data. | |||||||

`labels` |
a character vector of labels for each group. | |||||||

`newdata` |
a data frame, matrix or vector, as for the | |||||||

`newlabels` |
a character vector of labels for each new group defined in the argument | |||||||

`confidence.level` |
a numeric value between 0 and 1 specifying the confidence level of the computed probability limits.
By default is set at | |||||||

`rules` |
a function of rules to apply to the chart. By default, the | |||||||

`plot` |
logical. If | |||||||

`add.stats` |
a logical value indicating whether statistics and other information should be printed at the bottom of the chart. | |||||||

`chart.all` |
a logical value indicating whether both statistics for | |||||||

`label.limits` |
a character vector specifying the labels for control limits (Phase I). | |||||||

`label.pred.limits` |
a character vector specifying the labels for prediction control limits (Phase II). | |||||||

`title` |
a string giving the label for the main title. | |||||||

`xlab` |
a string giving the label for the x-axis. | |||||||

`ylab` |
a string giving the label for the y-axis. | |||||||

`ylim` |
a numeric vector specifying the limits for the y-axis. | |||||||

`axes.las` |
numeric in {0,1,2,3} specifying the style of axis labels. See | |||||||

`digits` |
the number of significant digits to use when | |||||||

`restore.par` |
a logical value indicating whether the previous | |||||||

`object` |
an object of class 'mqcc'. | |||||||

`x` |
an object of class 'mqcc'. | |||||||

`...` |
additional arguments to be passed to the generic function. |

Returns an object of class 'mqcc'.

Luca Scrucca luca@stat.unipg.it

Mason, R.L. and Young, J.C. (2002) *Multivariate Statistical Process Control with Industrial Applications*, SIAM.

Montgomery, D.C. (2005) *Introduction to Statistical Quality Control*, 5th ed. New York: John Wiley & Sons.

Ryan, T. P. (2000), *Statistical Methods for Quality Improvement*, 2nd ed. New York: John Wiley & Sons, Inc.

Scrucca, L. (2004). qcc: an R package for quality control charting and statistical process control. *R News* 4/1, 11-17.
Wetherill, G.B. and Brown, D.W. (1991) *Statistical Process Control*. New York: Chapman & Hall.

`stats.T2`

, `stats.T2.single`

, `limits.T2`

, `limits.T2.single`

, `ellipseChart`

, `qcc`

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 | ```
##
## Subgrouped data
##
# Ryan (2000, Table 9.2) data with p = 2 variables, m = 20 samples, n = 4 sample size:
X1 = matrix(c(72, 56, 55, 44, 97, 83, 47, 88, 57, 26, 46,
49, 71, 71, 67, 55, 49, 72, 61, 35, 84, 87, 73, 80, 26, 89, 66,
50, 47, 39, 27, 62, 63, 58, 69, 63, 51, 80, 74, 38, 79, 33, 22,
54, 48, 91, 53, 84, 41, 52, 63, 78, 82, 69, 70, 72, 55, 61, 62,
41, 49, 42, 60, 74, 58, 62, 58, 69, 46, 48, 34, 87, 55, 70, 94,
49, 76, 59, 57, 46), ncol = 4)
X2 = matrix(c(23, 14, 13, 9, 36, 30, 12, 31, 14, 7, 10,
11, 22, 21, 18, 15, 13, 22, 19, 10, 30, 31, 22, 28, 10, 35, 18,
11, 10, 11, 8, 20, 16, 19, 19, 16, 14, 28, 20, 11, 28, 8, 6,
15, 14, 36, 14, 30, 8, 35, 19, 27, 31, 17, 18, 20, 16, 18, 16,
13, 10, 9, 16, 25, 15, 18, 16, 19, 10, 30, 9, 31, 15, 20, 35,
12, 26, 17, 14, 16), ncol = 4)
X = list(X1 = X1, X2 = X2)
q = mqcc(X, type = "T2")
summary(q)
ellipseChart(q)
ellipseChart(q, show.id = TRUE)
q = mqcc(X, type = "T2", pred.limits = TRUE)
# Ryan (2000) discussed Xbar-charts for single variables computed adjusting the
# confidence level of the T^2 chart:
q1 = qcc(X1, type = "xbar", confidence.level = q$confidence.level^(1/2))
summary(q1)
q2 = qcc(X2, type = "xbar", confidence.level = q$confidence.level^(1/2))
summary(q2)
require(MASS)
# generate new "in control" data
Xnew = list(X1 = matrix(NA, 10, 4), X2 = matrix(NA, 10, 4))
for(i in 1:4)
{ x = mvrnorm(10, mu = q$center, Sigma = q$cov)
Xnew$X1[,i] = x[,1]
Xnew$X2[,i] = x[,2]
}
qq = mqcc(X, type = "T2", newdata = Xnew, pred.limits = TRUE)
summary(qq)
# generate new "out of control" data
Xnew = list(X1 = matrix(NA, 10, 4), X2 = matrix(NA, 10, 4))
for(i in 1:4)
{ x = mvrnorm(10, mu = 1.2*q$center, Sigma = q$cov)
Xnew$X1[,i] = x[,1]
Xnew$X2[,i] = x[,2]
}
qq = mqcc(X, type = "T2", newdata = Xnew, pred.limits = TRUE)
summary(qq)
##
## Individual observations data
##
data(boiler)
q = mqcc(boiler, type = "T2.single", confidence.level = 0.999)
summary(q)
# generate new "in control" data
boilerNew = mvrnorm(10, mu = q$center, Sigma = q$cov)
qq = mqcc(boiler, type = "T2.single", confidence.level = 0.999,
newdata = boilerNew, pred.limits = TRUE)
summary(qq)
# generate new "out of control" data
boilerNew = mvrnorm(10, mu = 1.01*q$center, Sigma = q$cov)
qq = mqcc(boiler, type = "T2.single", confidence.level = 0.999,
newdata = boilerNew, pred.limits = TRUE)
summary(qq)
# provides "robust" estimates of means and covariance matrix
library(MASS)
rob = cov.rob(boiler)
qrob = mqcc(boiler, type = "T2.single", center = rob$center, cov = rob$cov)
summary(qrob)
``` |

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