Wilks.test(rrcov)
Wilks.test()所属R语言包：rrcov
Classical and Robust One-way MANOVA: Wilks Lambda
古典和鲁棒单向变异数分析：威尔克斯拉姆达
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Classical and Robust One-way MANOVA: Wilks Lambda
古典和鲁棒单向变异数分析：威尔克斯拉姆达
用法----------Usage----------
## S3 method for class 'formula'[类formula的方法]
Wilks.test(formula, data, ..., subset, na.action)
## Default S3 method:[默认方法]
Wilks.test(x, grouping, method=c("c", "mcd", "rank"),
approximation=c("Bartlett", "Rao", "empirical"),
xd=NULL, xq=NULL, xfn = NULL, xwl=NULL, nrep=3000, trace=FALSE, ...)
## S3 method for class 'data.frame'
Wilks.test(x, ...)
## S3 method for class 'matrix'
Wilks.test(x, grouping, ..., subset, na.action)
参数----------Arguments----------
参数：formula
A formula of the form groups ~ x1 + x2 + ... That is, the response is the grouping factor and the right hand side specifies the (non-factor) variables.
的公式的形式groups ~ x1 + x2 + ...也就是说，响应分组因子和右手侧的指定的（非-因子）变量。
参数：data
Data frame from which variables specified in formula are to be taken.
数据框从formula是要采取指定的变量。
参数：x
(required if no formula is given as the principal argument.) a matrix or data frame or Matrix containing the explanatory variables.
（如果没有公式的主要参数。）矩阵或数据框或矩阵包含的解释变量。
参数：grouping
grouping variable - a factor specifying the class for each observation (required if no formula argument is given.)
分组变量 - 一个因素，如果没有公式参数指定一个类为每个观测值（需要）。
参数：subset
An index vector specifying the cases to be used.
指定要使用的情况下，索引向量。
参数：na.action
A function to specify the action to be taken if NAs are found. The default action is for the procedure to fail. An alternative is na.omit, which leads to rejection of cases with missing values on any required variable.
如果NAs的函数指定动作。默认的操作是失败的程序。另一种方法是na.omit，从而导致拒绝任何所需的变量的遗漏值的情况下。
参数：method
"c" for standard estimators of the mean and variance, "mcd" for MCD estimators of mean and variances and "rank" for rank based wilks' lambda as proposed by Nath and Pavur (1985).
"c"的标准估计的均值和方差的，"mcd"MCD的均值和方差的估计和"rank"为排名根据Wilks的lambda，如由纳特和Pavur的（1985）提出的。
参数：approximation
"Bartlett" for Bartlett approximation (default), "Rao" for rao approximation (only for method="c") and "empirical" for simulated empirical distribution.
"Bartlett"巴特利特近似（默认），"Rao"为饶近似（唯一的方法=“C”）和"empirical"的模拟经验分布。
参数：xd
multiplication factor for the approximate distribution of the robust Lambda statistic. If xd=NULL the factor will computed by simulation and will be returned in the value (see Details)
强大的Lambda统计量的近似分布的乘法因子。如果xd=NULL的因素通过模拟计算，将返回的值（见详情）
参数：xq
the degrees of freedom for the approximate χ^2 distribution of the robust Lambda statistic. If xq=NULL the degrees of freedom will computed by simulation and will be returned in the value (see Details)
程度的自由近似χ^2鲁棒的Lambda统计分布。如果xq=NULL的自由度将通过模拟计算，将返回值（见详情）
参数：xfn
the empirical distribution function. If xfn=NULL the empirical function will be estimated by simulation and will be returned in the value (see Details)
经验分布函数。如果xfn=NULL的经验功能将通过模拟估计将返回值（见详情）
参数：xwl
the simulated values of the robust statistic. If xwl=NULL the simulation will be performed and the calculated result will be returned in the value (see Details)
模拟值的强大的统计。如果xwl=NULL模拟将被执行，计算结果将返回值（见详情）
参数：nrep
number of trials for the simulations for computing the multiplication factor xd and the degrees of freedom xq. Default is nrep=3000.
用于计算的乘法系数xd和自由xq度的模拟试验的数量。默认是nrep=3000。
参数：trace
whether to print intermediate results. Default is trace = FALSE
是否要打印的中间结果。默认是trace = FALSE
参数：...
arguments passed to or from other methods.
传递的参数或其他方法。
Details
详细信息----------Details----------
The classical Wilks' Lambda statistic for testing the equality of the group means of two or more groups is modified into a robust one through substituting the classical estimates by the highly robust and efficient reweighted MCD estimates, which can be computed efficiently by the FAST-MCD algorithm - see CovMcd. An approximation for the finite sample distribution of the Lambda statistic is obtained, based on matching the mean and variance of a multiple of an χ^2 distribution which are computed by simultaion.
经典Wilks的lambda统计测试组的平等是指两个或两个以上组被修改成一个强大的通过高度可靠和高效率的重加权的MCD的估计，它可以有效地计算FAST-MCD代的经典估计算法 - 看到CovMcd。得到有限的Lambda统计样本分布的近似值基于匹配的均值和方差的倍数的χ^2分布计算simultaion的。
值----------Value----------
A list with class "htest" containing the following components:
列表类"htest"包含以下组件：
参数：statistic
the value of the Wilks' Lambda statistic.
Wilks的lambda值的统计。
参数：parameter
The corresponding approximation of the Wilks' lambda statistic and the degrees of freedom.
相应的近似Wilks的lambda统计和自由度。
参数：p.value
the p-value for the test.
p-值进行测试。
参数：estimate
the estimated mean vectors.
估计出的平均向量。
参数：method
a character string indicating what type of test was performed.
一个字符串，表示什么类型的测试进行。
参数：data.name
a character string giving the name of the data.
给出的数据的名称的字符串。
参数：xd
multiplication factor for the approximate distribution of the robust Lambda statistic.
强大的Lambda统计量的近似分布的乘法因子。
参数：xq
the degrees of freedom for the approximate χ^2 distribution of the robust Lambda statistic.
程度的自由近似χ^2鲁棒的Lambda统计分布。
注意----------Note----------
This function may be called giving either a formula and optional data frame, or a matrix and grouping factor as the first two arguments. All other arguments are optional.
此功能可称为公式和可选的数据框，或一个矩阵和分组因素，前两个参数。所有其它参数是可选的。
（作者）----------Author(s)----------
Valentin Todorov <a href="mailto:valentin.todorov@chello.at">valentin.todorov@chello.at</a>
参考文献----------References----------
submetted to the Journal of Environmetrics.
analysis, Statistical Methods and Applications, 15, 395.407, doi:10.1007/s10260-006-0032-6.
multivariate analysis of variance, Computatational Statistics and Data Analysis, 2, 297–315
参见----------See Also----------
CovMcd, T2.test
CovMcd，T2.test
实例----------Examples----------
library(MASS)
data(anorexia)
grp <- as.factor(anorexia[,1])
x <- as.matrix(anorexia[,2:3])
## Using the default interface, classical test[＃使用默认的界面，经典的测试]
Wilks.test(x, grouping=grp, method="c")
## Using the default interface, rank based test[＃使用默认的界面，排名为基础的测试]
Wilks.test(x, grouping=grp, method="rank")
## For this data set: p=2, n=n1+n2+n3=29+26+17[＃对于这组数据：P = 2，N = N1 + N2 + N3 = 29 +26 +17]
## were computed the following multiplication factor xd and degrees of freedom xq[＃计算如下的倍增因子XD和程度的自由XQ]
## for the MCD estimates with alpha=0.5[＃MCD估计，α= 0.5]
xd <- -0.02162666
xq <- 3.63971
Wilks.test(x, grouping=grp, method="mcd", xd=xd, xq=xq)
## Now the same with the formula interface[＃现在，同样的公式接口]
Wilks.test(Treat~Prewt+Postwt, data=anorexia, method="mcd", xd=xd, xq=xq)
##Iris data with formula interface[＃Iris数据与公式接口]
data(iris)
Wilks.test(Species~., data=iris, method="c")
## and with default interface[＃默认接口]
Wilks.test(iris[,1:4],grouping=iris[,5], method="c")
# hemophilia data - classical, rank and MCD test[血友病数据 - 古典，职级及MCD测试]
data(hemophilia)
hemophilia$gr <- as.factor(hemophilia$gr)
Wilks.test(gr~., data=hemophilia, method="c")
Wilks.test(gr~., data=hemophilia, method="rank")
## already simulated parameters for MCD with alpha=0.5[模拟参数MCD与α= 0.5]
xd <- -0.01805436
xq <- 1.950301
Wilks.test(gr~., data=hemophilia, xd=xd, xq=xq, method="mcd")
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 2012-9-28 20:52:31
