CovMve(rrcov)
CovMve()所属R语言包：rrcov
Robust Location and Scatter Estimation via MVE
强大的位置与散布估计通过MVE
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Computes a robust multivariate location and scatter estimate with a high breakdown point, using the "MVE" (Minimum Volume Ellipsoid) estimator.
计算一个强大的多变量的位置和分散估计具有较高的击穿点，使用“MVE”（最小体积椭球）估计。
用法----------Usage----------
CovMve(x, alpha = 1/2, nsamp = 500, seed = NULL, trace = FALSE, control)
参数----------Arguments----------
参数：x
a matrix or data frame.
一个矩阵或数据框。
参数：alpha
numeric parameter controlling the size of the subsets over which the determinant is minimized, i.e., alpha*n observations are used for computing the determinant. Allowed values are between 0.5 and 1 and the default is 0.5.
数值参数控制行列式最小的子集的大小，即，alpha*n观测用于计算行列式。允许的值是0.5~1之间，默认为0.5。
参数：nsamp
number of subsets used for initial estimates or "best" or "exact". Default is nsamp = 500. For nsamp="best" exhaustive enumeration is done, as long as the number of trials does not exceed 5000. For "exact", exhaustive enumeration will be attempted however many samples are needed. In this case a warning message will be displayed saying that the computation can take a very long time.
用于初步估计或"best"或"exact"的子集数。默认是nsamp = 500。对于nsamp="best"穷举完成，只要试验的次数不超过5000。对于"exact"，穷举尝试，然而，许多样品需要。在这种情况下，将显示一条警告消息说，计算需要很长的时间。
参数：seed
starting value for random generator. Default is seed = NULL
随机数发生器的初始值。默认是seed = NULL
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：control
a control object (S4) of class CovControlMve-class containing estimation options - same as these provided in the fucntion specification. If the control object is supplied, the parameters from it will be used. If parameters are passed also in the invocation statement, they will override the corresponding elements of the control object.
控制对象（S4）类CovControlMve-class估计选项 - 因为这些在温控功能规格提供相同。如果被供给的控制对象，从它的参数将被使用。如果参数传递的调用语句，它们将覆盖相应元素的控制对象。
Details
详细信息----------Details----------
This function computes the minimum volume ellipsoid estimator of location and scatter and returns an S4 object of class CovMve-class containing the estimates.
此函数计算的最小体积椭球的位置和分散的估计，并返回一个S4对象的类CovMve-class包含的估计。
The approximate estimate is based on a subset of size alpha*n with an enclosing ellipsoid of smallest volume. The mean of the best found subset provides the raw estimate of the location, and the rescaled covariance matrix is the raw estimate of scatter. The rescaling of the raw covariance matrix is by median(dist)/qchisq(0.5, p) and this scale factor is returned in the slot raw.cnp2. Currently no finite sample corrction factor is applied. The Mahalanobis distances of all observations from the location estimate for the raw covariance matrix are calculated, and those points within the 97.5 under Gaussian assumptions are declared to be good. The final (reweightd) estimates are the mean and rescaled covariance of the good points. The reweighted covariance matrix is rescaled by 1/pgamma(qchisq(alpha, p)/2, p/2 + 1)/alpha (see Croux and Haesbroeck, 1999) and this scale factor is returned in the slot cnp2.
的一个子集的大小近似的估计是基于alpha*n一个封闭的椭球体的体积最小。最好的子集的平均提供原始的位置，并重新调整的协方差矩阵的估计是粗略估计的分散。 median(dist)/qchisq(0.5, p)，这个比例系数插槽raw.cnp2返回原始的协方差矩阵的重新调整。目前没有的有限样本corrction因素。马氏距离的位置估计为原料的协方差矩阵的所有观测数据的计算，这些点内的97.5高斯假设下被宣布为好。最后（reweightd）估计的均值和重新调整的协方差的好点。再加权的协方差矩阵的1/pgamma(qchisq(alpha, p)/2, p/2 + 1)/alpha（见克鲁和Haesbroeck，1999年），此比例因子中返回插槽cnp2重新调整。
The search for the approximate solution is made over ellipsoids determined by the covariance matrix of p+1 of the data points and applying a simple but effective improvement of the subsampling procedure as described in Maronna et al. (2006), p. 198. Although there exists no formal proof of this improvement (as for MCD and LTS), simulations show that it can be recommended as an approximation of the MVE.
的近似解的搜索作出了确定的协方差矩阵的椭球p+1的数据点和所述在Maronna等施加子采样过程的一个简单而有效的改善。 （2006），页。 198。尽管不存在正式的证明，这种改进（MCD和LTS），仿真实验表明，它可以作为一个近似的MVE建议。
值----------Value----------
An S4 object of class CovMve-class which is a subclass of the virtual class CovRobust-class.
S4对象的类CovMve-class这是虚拟类CovRobust-class的一个子类。
注意----------Note----------
Main reason for implementing the MVE estimate was that it is the recommended initial estimate for S estimation (see Maronna et al. (2006), p. 199) and will be used by default in CovMest (after removing the correction factors from the covariance matrix and rescaling to determinant 1).
实施的MVE估计主要的原因是，它是推荐的初始估计的S估计（见Maronna等人（2006年），页199），默认情况下，将用于CovMest（取出后的校正因子的协方差矩阵，并从重新缩放行列式为1）。
（作者）----------Author(s)----------
Valentin Todorov <a href="mailto:valentin.todorov@chello.at">valentin.todorov@chello.at</a> and
Matias Salibian-Barrera <a href="mailto:matias@stat.ubc.ca">matias@stat.ubc.ca</a>
参考文献----------References----------
Robust Regression and Outlier Detection. Wiley.
Influence function and efficiency of the minimum covariance determinant scatter matrix estimator. Journal of Multivariate Analysis, 71, 161–190.
Wiley, New York.
An Object Oriented Framework for Robust Multivariate Analysis. Journal of Statistical Software, 32(3), 1–47. URL http://www.jstatsoft.org/v32/i03/.
参见----------See Also----------
cov.mve from package MASS
cov.mve包MASS
实例----------Examples----------
data(hbk)
hbk.x <- data.matrix(hbk[, 1:3])
CovMve(hbk.x)
## the following three statements are equivalent[＃以下三个语句是等价的]
c1 <- CovMve(hbk.x, alpha = 0.75)
c2 <- CovMve(hbk.x, control = CovControlMve(alpha = 0.75))
## direct specification overrides control one:[＃直接指定覆盖控制1：]
c3 <- CovMve(hbk.x, alpha = 0.75,
control = CovControlMve(alpha=0.95))
c1
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注：
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发表于 2012-9-28 20:44:48
