covOGK(robustbase)
covOGK()所属R语言包：robustbase
Orthogonalized Gnanadesikan-Kettenring (OGK) Covariance Matrix Estimation
的正交化格纳纳德西肯 - Kettenring（OGK）协方差矩阵估计
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Computes the orthogonalized pairwise covariance matrix estimate described in in Maronna and Zamar (2002). The pairwise proposal goes back to Gnanadesikan and Kettenring (1972).
计算了的正交两两协方差矩阵估计描述的，在Maronna和Zamar（2002）。成对的建议可以追溯到格纳纳德西肯和Kettenring（1972年）。
用法----------Usage----------
covOGK(X, n.iter = 2, sigmamu, rcov = covGK, weight.fn = hard.rejection,
keep.data = FALSE, ...)
covGK (x, y, scalefn = scaleTau2, ...)
s_mad(x, mu.too = FALSE, na.rm = FALSE)
s_IQR(x, mu.too = FALSE, na.rm = FALSE)
参数----------Arguments----------
参数：X
data in something that can be coerced into a numeric matrix.
可以强制转换成数字矩阵中的一些数据。
参数：n.iter
number of orthogonalization iterations. Usually 1 or 2; values greater than 2 are unlikely to have any significant effect on the estimate (other than increasing the computing time).
正交迭代。通常为1或2;大于2的值是不会有任何显着的影响估计（增加了计算时间以外）。
参数：sigmamu, scalefn
a function that computes univariate robust location and scale estimates. By default it should return a single numeric value containing the robust scale (standard deviation) estimate. When mu.too is true, sigmamu() should return a numeric vector of length 2 containing robust location and scale estimates. See scaleTau2, s_Qn, s_Sn, s_mad or s_IQR for examples to be used as sigmamu argument.
一个函数，计算单变量的强大的位置和规模的估计。默认情况下，它应该返回一个单一的数值，包含强大的规模（标准差）估计。当mu.too是真实的，sigmamu()应该返回一个数值向量的长度为2包含强大的位置和规模的估计。 scaleTau2，s_Qn，s_Sn，s_mad或s_IQR被用来作为sigmamu参数的例子。
参数：rcov
function that computes a robust covariance estimate between two vectors. The default, Gnanadesikan-Kettenring's covGK, is simply (s^2(X+Y) - s^2(X-Y))/4 where s() is the scale estimate sigmamu().
函数，计算两个向量之间的一个强大的协方差估计。默认情况下，格纳纳德西肯Kettenring的covGK，简直是(s^2(X+Y) - s^2(X-Y))/4其中s()是规模估计sigmamu()。
参数：weight.fn
a function of the robust distances and the number of variables p to compute the weights used in the reweighting step.
强劲的距离的函数的变量的数量p计算中的权重调整步骤中所使用的权重。
参数：keep.data
logical indicating if the (untransformed) data matrix X should be kept as part of the result.
逻辑说明（未变形）数据矩阵X应保持作为结果的一部分。
参数：...
additional arguments; for covOGK to be passed to sigmamu() and weight.fn(); for covGK passed to scalefn.
额外的参数covOGK要传递给sigmamu()和weight.fn()covGK传递给scalefn。
参数：x,y
numeric vectors of the same length, the covariance of which is sought in covGK (or the scale, in s_mad or s_IQR).
相同的长度，其中的协方差的数值向量寻求在covGK（或规模，：在s_mad或s_IQR）。
参数：mu.too
logical indicating if both location and scale should be returned or just the scale (when mu.too=FALSE as by default).
逻辑的位置和规模，应退回或只是规模（当mu.too=FALSE，因为默认情况下）。
参数：na.rm
if TRUE then NA values are stripped from x before computation takes place.
如果TRUE然后NA值剥离x之前计算的地方。
Details
详细信息----------Details----------
Typical default values for the function arguments sigmamu, rcov, and weight.fn, are available as well, see the Examples below, but their names and calling sequences are still subject to discussion and may be changed in the future.
典型的功能参数的默认值sigmamu，rcov和weight.fn，可为好，看下面的例子，但他们的名字和调用序列，仍有待讨论，可能是在未来发生变更。
The current default, weight.fn = hard.rejection corresponds to the proposition in the litterature, but Martin Maechler strongly believes that the hard threshold currently in use is too arbitrary, and further that soft thresholding should be used instead, anyway.
目前默认情况下，weight.fn = hard.rejection对应的命题在LITTERATURE，但马丁Maechler的强烈认为，目前使用的硬阈值是太乱了，和进一步的软阈值应，反正。
值----------Value----------
covOGK() currently returns a list with components
covOGK()返回一个列表的组件
参数：center
robust location: numeric vector of length p.
强大的位置：数字矢量的长度p。
参数：cov
robust covariance matrix estimate: p x p matrix.
强大的协方差矩阵的估计：p x p矩阵。
参数：wcenter, wcov
re-weighted versions of center and cov.
重新加权版本的center和cov。
参数：weights
the robustness weights used.
所使用的鲁棒性的权重。
参数：distances
the mahalanobis distances computed using center and cov.
马氏距离计算使用center和cov。
...... <br> but note that this might be radically changed to returning an S4 classed object!
...... <BR>但要注意，这可能会从根本上改变返回S4类对象！
covGK() is a trivial 1-line function returning the covariance estimate
covGK()是一个简单的1-LINE函数返回的协方差估计
where s^(u) is the scale estimate of u specified by scalefn.
s^(u)是u指定的scalefn的规模估计。
s_mad(), and s_IQR() return the scale estimates mad or IQR respectively, where the s_* functions return a length-2 vector (mu, sig) when mu.too = TRUE, see also scaleTau2.
s_mad()和s_IQR()返回的规模估计mad或IQR，s_*函数返回一个长度为2个向量（亩，SIG）当 mu.too = TRUE，看到scaleTau2。
（作者）----------Author(s)----------
Kjell Konis <a href="mailto:konis@stats.ox.ac.uk">konis@stats.ox.ac.uk</a>, with modifications by
Martin Maechler.
参考文献----------References----------
Robust estimates of location and dispersion of high-dimensional datasets; Technometrics 44(4), 307–317.
Robust estimates, residuals, and outlier detection with multiresponse data. Biometrics 28, 81–124.
参见----------See Also----------
scaleTau2, covMcd, cov.rob.
scaleTau2，covMcd，cov.rob。
实例----------Examples----------
data(hbk)
hbk.x <- data.matrix(hbk[, 1:3])
cO1 <- covOGK(hbk.x, sigmamu = scaleTau2)
cO2 <- covOGK(hbk.x, sigmamu = s_Qn)
cO3 <- covOGK(hbk.x, sigmamu = s_Sn)
cO4 <- covOGK(hbk.x, sigmamu = s_mad)
cO5 <- covOGK(hbk.x, sigmamu = s_IQR)
data(toxicity)
cO1tox <- covOGK(toxicity, sigmamu = scaleTau2)
cO2tox <- covOGK(toxicity, sigmamu = s_Qn)
## nice formatting of correlation matrices:[＃很好的相关矩阵格式：]
as.dist(round(cov2cor(cO1tox$cov), 2))
as.dist(round(cov2cor(cO2tox$cov), 2))
## "graphical"[＃“图形”]
symnum(cov2cor(cO1tox$cov))
symnum(cov2cor(cO2tox$cov), legend=FALSE)
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。
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发表于 2012-9-27 22:08:07
