mqcc(qcc)
mqcc()所属R语言包：qcc
Multivariate Quality Control Charts
多元质量控制图
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Create an object of class 'mqcc' to perform multivariate statistical quality control.
创建的对象类的mqcc“进行多元统计质量控制。
用法----------Usage----------
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, ...)
参数----------Arguments----------
参数：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.
对于抽血的数据列表中每个变量的数据框或矩阵进行监控。的数据框的每行或矩阵是指一个样品或“理由”组。对于个人的观察，其中每个样品具有单一的观察，用户可以提供一个与一个数据框或具有单个列的矩阵，或一个数据框或一个矩阵，其中每个行是指样品和列变量列表。见的例子。
参数：type
a character string specifying the type of chart:
一个字符串指定类型的图表：
<table summary="Rd table"> <tr> <td align="left"> </td><td align="left"> Chart description </td> </tr> <tr> <td align="left"> "T2" </td><td align="left"> Hotelling T^2 chart for subgrouped data </td> </tr> <tr> <td align="left"> "T2.single" </td><td align="left"> Hotelling T^2 chart for individual observations </td> </tr> <tr> <td align="left"> </td> </tr> </table>
<table summary="Rd table"> <TR> <td ALIGN="LEFT"> </ TD> <TD ALIGN="LEFT">图描述</ TD> </ TR> <TR> <TD对齐=“左“> "T2" </ TD> <TD ALIGN="LEFT">霍特林T^2抽血的数据图表</ TD> </ TR> <TR> <td ALIGN="LEFT"> "T2.single" </ TD> <TD ALIGN="LEFT">霍特林T^2图的个别意见</ TD> </ TR> <TR> <td ALIGN="LEFT"> </ TD> < / TR> </ TABLE>
参数：center
a vector of values to use for center of input variables.
为中心使用的输入变量的值的向量。
参数：cov
a matrix of values to use for the covariance matrix of input variables.
用于输入变量的协方差矩阵的值的矩阵。
参数：limits
a logical indicating if control limits (Phase I) must be computed (by default using limits.T2 or limits.T2.single) and plotted, or a two-values vector specifying control limits.
逻辑，表示如果必须计算控制界限（一期）（默认情况下，使用limits.T2或limits.T2.single）和策划，或二值向量确定控制界限。
参数：pred.limits
a logical indicating if prediction limits (Phase II) must be computed (by default using limits.T2 or limits.T2.single) and plotted, or a two-values vector specifying prediction limits.
逻辑如果的预测限制（二期）必须被计算（默认情况下，使用limits.T2或limits.T2.single）策划，或二值向量确定预测的限制。
参数：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.
一个字符串，指定的变量，它的名称上出现的图。如果未提供被作为数据从给定的对象。
参数：labels
a character vector of labels for each group.
字符向量的每个组的标签。
参数：newdata
a data frame, matrix or vector, as for the data argument, providing further data to plot but not included in the computations.
提供进一步的数据的一个数据框，矩阵或向量，为的data参数，要绘制的，但不包括在计算中。
参数：newlabels
a character vector of labels for each new group defined in the argument newdata.
为每一个新的标签组中定义的参数newdata字符向量。
参数：confidence.level
a numeric value between 0 and 1 specifying the confidence level of the computed probability limits. By default is set at (1 - 0.0027)^p where p is the number of variables, and 0.0027 is the probability of Type I error for a single Shewhart chart at the usual 3-sigma control level.
介于0和1之间的一个数值，所计算的概率限制指定的置信水平。默认情况下是在(1 - 0.0027)^pp是多少变数，0.0027是I类错误的概率为一个单一的休哈特图在通常的3个sigma的控制水平。
参数：rules
a function of rules to apply to the chart. By default, the shewhart.rules function is used.
一功能的规则应用到图表中。默认情况下，shewhart.rules功能。
参数：plot
logical. If TRUE a quality chart is plotted.
逻辑。如果TRUE的质量图表绘制。
参数：add.stats
a logical value indicating whether statistics and other information should be printed at the bottom of the chart.
一个逻辑值，该值指示是否应印在底部的图表数据和其他资料。
参数：chart.all
a logical value indicating whether both statistics for data and for newdata (if given) should be plotted.
一个逻辑值，该值指示是否都统计data和newdata（如果有）应绘制。
参数：label.limits
a character vector specifying the labels for control limits (Phase I).
字符向量：指定控制限的标签（第一期）。
参数：label.pred.limits
a character vector specifying the labels for prediction control limits (Phase II).
指定一个字符向量预测控制界限（二期）的标签。
参数：title
a string giving the label for the main title.
一个字符串，给出的主标题的标签。
参数：xlab
a string giving the label for the x-axis.
一个字符串，给出的x轴的标签。
参数：ylab
a string giving the label for the y-axis.
一个字符串，给出的y轴的标签。
参数：ylim
a numeric vector specifying the limits for the y-axis.
一个数值向量为y-轴指定的限制。
参数：axes.las
numeric in {0,1,2,3} specifying the style of axis labels. See help(par).
数字在{0,1,2,3}指定轴标签的风格。见help(par)。
参数：digits
the number of significant digits to use when add.stats = TRUE.
的数量显著位数时使用的add.stats = TRUE。
参数：restore.par
a logical value indicating whether the previous par settings must be restored. If you need to add points, lines, etc. to a control chart set this to FALSE.
一个逻辑值，该值指示是否par设置必须恢复。如果你需要添加点，线，控制图设置为FALSE。
参数：object
an object of class 'mqcc'.
对象类的mqcc。
参数：x
an object of class 'mqcc'.
对象类的mqcc。
参数：...
additional arguments to be passed to the generic function.
额外的参数传递给泛型函数。
值----------Value----------
Returns an object of class 'mqcc'.
返回一个对象类的mqcc“。
（作者）----------Author(s)----------
Luca Scrucca <a href="mailto:luca@stat.unipg.it">luca@stat.unipg.it</a>
参考文献----------References----------
Mason, R.L. and Young, J.C. (2002) Multivariate Statistical Process Control with Industrial Applications, SIAM. <br> Montgomery, D.C. (2005) Introduction to Statistical Quality Control, 5th ed. New York: John Wiley & Sons. <br> Ryan, T. P. (2000), Statistical Methods for Quality Improvement, 2nd ed. New York: John Wiley & Sons, Inc.
参见----------See Also----------
stats.T2, stats.T2.single, limits.T2, limits.T2.single, ellipseChart, qcc
stats.T2，stats.T2.single，limits.T2，limits.T2.single，ellipseChart，qcc
实例----------Examples----------
##[＃]
## Subgrouped data[＃抽血的数据]
##[＃]
# Ryan (2000, Table 9.2) data with p = 2 variables, m = 20 samples, n = 4 sample size:[瑞安（2000年，表9.2）P = 2变量的数据，M = 20个样本，n = 4的样本量：]
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 [瑞安（2000）讨论了XBAR图调整计算单变量]
# confidence level of the T^2 chart:[置信水平的T ^ 2控制图：]
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[产生新的“控制”]
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[产生新的“控制”的数据]
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[＃个别观测数据]
##[＃]
data(boiler)
q = mqcc(boiler, type = "T2.single", confidence.level = 0.999)
summary(q)
# generate new "in control" data[产生新的“控制”]
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[产生新的“控制”的数据]
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[“稳健”的手段和协方差矩阵的估计]
library(MASS)
rob = cov.rob(boiler)
qrob = mqcc(boiler, type = "T2.single", center = rob$center, cov = rob$cov)
summary(qrob)
转载请注明:出自 生物统计家园网(http://www.biostatistic.net)。
注：
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发表于 2012-9-24 21:22:46
