CovNAMcd(rrcovNA)
CovNAMcd()所属R语言包：rrcovNA
Robust Location and Scatter Estimation via MCD for incomplete data
强大的位置与散布不完整的资料估计通过MCD
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 for incomplete data, using the "Fast MCD" (Minimum Covariance Determinant) estimator.
计算一个强大的多变量的位置和分散估计高的击穿点，不完整的数据，使用“快速MCD（最低协方差的决定因素）的估计。
用法----------Usage----------
CovNAMcd(x, alpha = 1/2, nsamp = 500, seed = NULL, trace = FALSE, use.correction = TRUE, impMeth = c("norm" , "seq", "rseq"), 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：use.correction
whether to use finite sample correction factors. Default is use.correction=TRUE
是否使用有限样本的校正因素。默认是use.correction=TRUE
参数：impMeth
select imputation method to use - choose one of "norm" , "seq" or "rseq". The default is "norm"
选择插补法使用 - 选择一个“行规”，“以下”或“rseq”。默认值是“规范”
参数：control
a control object (S4) of class CovControlMcd-class containing estimation options - same as these provided in the function 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CovControlMcd-class估计选项 - 这些在功能规格相同。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 covariance determinant estimator of location and scatter and returns an S4 object of class CovMcd-class containing the estimates. The implementation of the function is similar to the existing R function covMcd() which returns an S3 object. The MCD method looks for the h (> n/2) observations (out of n) whose classical covariance matrix has the lowest possible determinant. The raw MCD estimate of location is then the average of these h points, whereas the raw MCD estimate of scatter is their covariance matrix, multiplied by a consistency factor and a finite sample correction factor (to make it consistent at the normal model and unbiased at small samples). Both rescaling factors are returned also in the vector raw.cnp2 of length 2. Based on these raw MCD estimates, a reweighting step is performed which increases the finite-sample efficiency considerably - see Pison et al. (2002). The rescaling factors for the reweighted estimates are returned in the vector cnp2 of length 2. Details for the computation of the finite sample correction factors can be found in Pison et al. (2002). The finite sample corrections can be suppressed by setting use.correction=FALSE. The implementation in rrcov uses the Fast MCD algorithm of Rousseeuw and Van Driessen (1999) to approximate the minimum covariance determinant estimator.
此函数计算的最小方差的决定因素估计的位置和分散，并返回一个S4对象的类CovMcd-class估计。现有的R函数covMcd()，它返回一个S3对象实现的功能是类似的。 MCD方法查找h (> n/2)意见（出n），其经典的协方差矩阵具有最低的可能的决定因素。的原料MCD估计的位置，然后这些h点的平均值，而：MCD原始估计的分散是它们的协方差矩阵，乘以由一致性因子和一个有限的样品校正因子（以使其保持一致，在正常的模型和公正的，在小样本）。这两种重新缩放因子也返回向量中的raw.cnp2的长度为2。这些原料MCD估计的基础上，进行权重调整步骤，这大大增加了有限样本的效率 - 皮松等。 （2002年）。重新缩放因素再加权估计中返回向量cnp2长度为2。的有限样本校正因子的计算的细节，可以发现在皮松等。 （2002年）。有限样本的修正，可以抑制，通过设置use.correction=FALSE。实施rrcov使用快速MCD算法的Rousseeuw和Van Driessen的（1999年）的最小方差的决定因素估计。
值----------Value----------
An S4 object of class CovNAMcd-class which is a subclass of the virtual class CovNARobust-class.
S4对象的类CovNAMcd-class这是虚拟类CovNARobust-class的一个子类。
（作者）----------Author(s)----------
Valentin Todorov <a href="mailto:valentin.todorov@chello.at">valentin.todorov@chello.at</a>
参考文献----------References----------
survey data with incomplete information. Advances in Data Analysis and Classification, 5 37–56, 2011.
A fast algorithm for the minimum covariance determinant estimator. Technometrics 41, 212–223.
An Object Oriented Framework for Robust Multivariate Analysis. Journal of Statistical Software, 32(3), 1–47. URL http://www.jstatsoft.org/v32/i03/.
实例----------Examples----------
data(bush10)
mcd <- CovNAMcd(bush10)
mcd
summary(mcd)
plot(mcd)
plot(mcd, which="pairs")
plot(mcd, which="xydistance")
plot(mcd, which="xyqqchi2")
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注：
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发表于 2012-9-28 20:38:46
