cov.rob(MASS)
cov.rob()所属R语言包：MASS
Resistant Estimation of Multivariate Location and Scatter
耐估计多元的位置与散布
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Compute a multivariate location and scale estimate with a high breakdown point – this can be thought of as estimating the mean and covariance of the good part of the data. cov.mve and cov.mcd are compatibility wrappers.
计算一个多元的高击穿点的位置和规模估计 - 这可以被认为是good部分数据的均值和方差估计。 cov.mve和cov.mcd兼容性包装。
用法----------Usage----------
cov.rob(x, cor = FALSE, quantile.used = floor((n + p + 1)/2),
method = c("mve", "mcd", "classical"),
nsamp = "best", seed)
cov.mve(...)
cov.mcd(...)
参数----------Arguments----------
参数：x
a matrix or data frame.
矩阵或数据框。
参数：cor
should the returned result include a correlation matrix?
返回的结果应包括相关矩阵？
参数：quantile.used
the minimum number of the data points regarded as good points.
被视为good点的数据点的最低数量。
参数：method
the method to be used – minimum volume ellipsoid, minimum covariance determinant or classical product-moment. Using cov.mve or cov.mcd forces mve or mcd respectively.
要使用的方法 - 最小体积椭球，最小方差行列式或古典的产品时刻。使用cov.mve或cov.mcd力量mve或mcd分别。
参数：nsamp
the number of samples or "best" or "exact" or "sample". If "sample" the number chosen is min(5*p, 3000), taken from Rousseeuw and Hubert (1997). If "best" exhaustive enumeration is done up to 5000 samples: if "exact" exhaustive enumeration will be attempted however many samples are needed.
样本数或"best"或"exact"或"sample"。如果"sample"选择min(5*p, 3000)，从Rousseeuw和Hubert（1997）。如果"best"做详尽的列举了5000个样本：如果"exact"然而，许多样品需要将试图穷尽枚举。
参数：seed
the seed to be used for random sampling: see RNGkind. The current value of .Random.seed will be preserved if it is set.
采用随机抽样的种子：RNGkind。电流值的.Random.seed将被保留，如果它被设置。
参数：...
arguments to cov.rob other than method.
cov.rob比method其他的参数。
Details
详情----------Details----------
For method "mve", an approximate search is made of a subset of size quantile.used with an enclosing ellipsoid of smallest volume; in method "mcd" it is the volume of the Gaussian confidence ellipsoid, equivalently the determinant of the classical covariance matrix, that is minimized. The mean of the subset provides a first estimate of the location, and the rescaled covariance matrix a first estimate of scatter. The Mahalanobis distances of all the points from the location estimate for this covariance matrix are calculated, and those points within the 97.5% point under Gaussian assumptions are declared to be good. The final estimates are the mean and rescaled covariance of the good points.
对于方法"mve"，近似搜索的大小quantile.used封闭椭球的体积最小的子集的方法"mcd"这是高斯信心椭球的体积，等效经典的协方差矩阵，最小的决定因素。子集的平均值提供了第一的位置估计，重新调整的协方差矩阵，初步估算了分散。从这个协方差矩阵的位置估计所有点的马氏距离计算，并宣布下高斯假设的97.5％，在这些点是good。最后估计是平均good点和重标方差。
The rescaling is by the appropriate percentile under Gaussian data; in addition the first covariance matrix has an ad hoc finite-sample correction given by Marazzi.
缩放功能是通过适当的百分根据高斯数据;第一的协方差矩阵中除了有特设Marazzi给出有限样本校正。
For method "mve" the search is made over ellipsoids determined by the covariance matrix of p of the data points. For method "mcd" an additional improvement step suggested by Rousseeuw and van Driessen (1999) is used, in which once a subset of size quantile.used is selected, an ellipsoid based on its covariance is tested (as this will have no larger a determinant, and may be smaller).
方法"mve"搜索由p数据点的协方差矩阵确定的椭球。 方法，因为这将"mcd"额外的改善步骤，由Rousseeuw和面包车Driessen的（1999）的建议，在这一次的大小quantile.used的一个子集被选中，根据其方差的椭球测试（有没有更大的一个决定因素，可能会更小）。
值----------Value----------
A list with components
与组件列表
参数：center
the final estimate of location.
最终的位置估计。
参数：cov
the final estimate of scatter.
分散的最终估计。
参数：cor
(only is cor = TRUE) the estimate of the correlation matrix.
（仅是cor = TRUE）的相关矩阵的估计。
参数：sing
message giving number of singular samples out of total
消息给奇异样本数总
参数：crit
the value of the criterion on log scale. For MCD this is the determinant, and for MVE it is proportional to the volume.
日志规模标准值。为MCD的，这是决定因素，姆韦它的体积成正比。
参数：best
the subset used. For MVE the best sample, for MCD the best set of size quantile.used.
使用子集。对于姆韦MCD的最好的样本，大小quantile.used最好的一套。
参数：n.obs
total number of observations. </table>
观测的总数。 </ TABLE>
参考文献----------References----------
Robust Regression and Outlier Detection. Wiley.
Algorithms, Routines and S Functions for Robust Statistics. Wadsworth and Brooks/Cole.
multivariate outliers and leverage points, Journal of the American Statistical Association, 85, 633–639.
minimum covariance determinant estimator. Technometrics 41, 212–223.
L1-Statistical Procedures and Related Topics ed Y. Dodge, IMS Lecture Notes volume 31, pp. 201–214.
参见----------See Also----------
lqs
lqs
举例----------Examples----------
set.seed(123)
cov.rob(stackloss)
cov.rob(stack.x, method = "mcd", nsamp = "exact")
转载请注明:出自 生物统计家园网(http://www.biostatistic.net)。
注：
注1：为了方便大家学习，本文档为生物统计家园网机器人LoveR翻译而成，仅供个人R语言学习参考使用，生物统计家园保留版权。
注2：由于是机器人自动翻译，难免有不准确之处，使用时仔细对照中、英文内容进行反复理解，可以帮助R语言的学习。
注3：如遇到不准确之处，请在本贴的后面进行回帖，我们会逐渐进行修订。
|
发表于 2012-2-17 10:26:58
