prcomp.robust(mdqc)
prcomp.robust()所属R语言包：mdqc
Principal Components Analysis using Robust Estimators
主成分分析使用鲁棒估计
译者：生物统计家园网 机器人LoveR
描述----------Description----------
A function that performs PCA using the robust estimators "S-estimator","MCD" and "MVE".
函数执行PCA的使用稳健估计“的S-估计”，“MCD的”和“姆韦”。
用法----------Usage----------
prcomp.robust(x, robust = c("S-estimator","MCD", "MVE"),
nsamp = 10*nrow(x), ...)
## S3 method for class 'robust'
prcomp(x, robust = c("S-estimator","MCD", "MVE"),
nsamp = 10*nrow(x), ...)
参数----------Arguments----------
参数：x
a matrix. Contains the data to perform PCA on.
一个矩阵。包含的数据进行主成分分析。
参数：robust
The robust estimator to use. One of "S-estimator", "MCD", or "MVE". The default robust estimator is the S-estimator with 25% breakdown point.
鲁棒估计使用。一个"S-estimator"，"MCD"或"MVE"。默认的强劲估计是25％的击穿点的S-估计。
参数：nsamp
The number of subsamples that the robust estimator should use. This defaults to 10 times the number of rows in the matrix.
强劲的估计应该使用的子样本的数量。这个矩阵中的行数10倍的违约。
参数：...
Further arguments that can be passed to the robust estimator
可以通过进一步的参数的鲁棒估计
Details
详情----------Details----------
The calculation is done by a singular value decomposition of the robust centered and scaled data matrix, not by using eigen on the covariance matrix. This is generally the preferred method for numerical accuracy. The print method for the these objects prints the results in a nice format and the plot method produces a scree plot. The scree plot can be used to determine the number k of principal components preserved in the analysis, looking for the “elbow” or the first important bend in the line. A biplot can also be generated to represent the values of the first two principal components (PCs) and the contribution of each variable to these components in the same plot (see Supplementary Material of Cohen Freue et al. (2007)).
是由一个强大的中心和缩放数据矩阵奇异值分解的计算，而不是使用eigen协方差矩阵。这是一般的数值精度的首选方法。 print对这些对象的方法打印的结果，在一个很好的格式和plot方法产生的碎石图。可以用卵石图，以确定保留的分析，寻找“肘”或第一线的重要弯曲的主要组成部分的数k。一个双标图也可以产生代表首两个主成分（PC）和每个变量的贡献值，在相同的图，这些组件（科恩Freue等补充材料。（2007））。
值----------Value----------
prcomp.robust returns a list with class "prcomp" containing the following components:
prcomp.robust类"prcomp"包含以下组件返回一个列表：
参数：sdev
the standard deviations of the principal components (i.e., the square roots of the eigenvalues of the covariance matrix calculated using the robust argument, though the calculation is actually done with the singular values of the data matrix).
的主要组成部分（即，使用robust参数计算协方差矩阵的特征值的平方根，但实际数据矩阵奇异值计算）的标准偏差。
参数：rotation
the matrix of variable loadings (i.e., a matrix whose columns contain the eigenvectors). The function princomp returns this in the element loadings.
变载荷矩阵（即矩阵的列中包含的特征向量）。函数princomp返回元素loadings。
参数：x
the value of the rotated data (the centered and scaled) data multiplied by the rotation matrix) is returned.
旋转数据（rotation矩阵相乘的中心和缩放）的数据）的返回值。
作者（S）----------Author(s)----------
Justin Harrington <a href="mailto:harringt@stat.ubc.ca">harringt@stat.ubc.ca</a> and Gabriela
V. Cohen Freue <a href="mailto:gcohen@stat.ubc.ca">gcohen@stat.ubc.ca</a>.
参考文献----------References----------
R. and Scherer, A. and McManus, B. and Keown, P. and McMaster, W. R. and Ng, R. T. (2007) ‘MDQC: A New Quality Assessment Method for Microarrays Based on Quality Control Reports’. Bioinformatics 23, 3162 – 3169.
参见----------See Also----------
mdqc, prcomp
mdqc，prcomp
举例----------Examples----------
data(allQC) ## Loads the dataset allQC[＃加载数据集allQC]
prout <- prcomp.robust(allQC)
screeplot(prout, type="line")
biplot(prout)
prout <- prcomp.robust(allQC, robust="MCD")
screeplot(prout, type="line")
biplot(prout)
prout <- prcomp.robust(allQC, robust="MVE")
screeplot(prout, type="line")
biplot(prout)
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。
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发表于 2012-2-26 00:36:45
