R语言 rrcov包 Pca-class()函数中文帮助文档(中英文对照)

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Pca-class(rrcov)
Pca-class()所属R语言包：rrcov

                                        Class "Pca" - virtual base class for all classic and robust PCA classes
                                         类“前列腺癌” - 所有的传统和强大的PCA类的虚基类

                                         译者：生物统计家园网 机器人LoveR

描述----------Description----------

The class Pca searves as a base class for deriving all other  classes representing the results of the classical and robust Principal  Component Analisys methods
类Pca的searves为代表的古典和健壮主成分Analisys的方法，结果所有其他类派生的基类


类对象----------Objects from the Class----------

A virtual Class: No objects may be created from it.
可能会从它创建一个虚拟类：没有对象。


插槽----------Slots----------




call: Object of class "language"
call：对象类"language"的




center: Object of class "vector" the center of the data
center：类的对象"vector"的中心的数据




scale: Object of class "vector" the scaling applied to each variable of the data
scale：类的对象"vector"的比例换算到每个变量的数据




loadings: Object of class "matrix" the matrix
loadings：对象类"matrix"矩阵




eigenvalues: Object of class "vector" the eigenvalues
eigenvalues：对象类"vector"的特征值




scores: Object of class "matrix" the scores - the value  of the projected on the space of the principal components data (the centred  (and scaled if requested) data multiplied  by the loadings matrix) is returned.  Hence, cov(scores)
scores：对象的类"matrix"的分数 - 空间的主要组成部分数据（居中（，规模化的要求）的数据乘以loadings矩阵的预计值）被返回。因此，cov(scores)




k: Object of class "numeric" number of (choosen) principal components
k：类"numeric"（选定的）的对象主要组成部分




sd: Object of class "Uvector" Score distances within the robust PCA subspace
sd：对象的类"Uvector"的分数距离内强劲的PCA子空间




od: Object of class "Uvector" Orthogonal distances to the robust PCA subspace
od：对象类"Uvector"正交距离的强劲的PCA子空间




cutoff.sd: Object of class "numeric" Cutoff value for the score distances
cutoff.sd：对象类"numeric"临界值的分数距离




cutoff.od: Object of class "numeric" Cutoff values for the orthogonal distances
cutoff.od：对象的类"numeric"的临界值的垂直距离




flag: Object of class "Uvector" The observations whose score distance is larger  than cutoff.sd or whose orthogonal distance is larger than cutoff.od can be considered  as outliers and receive a flag equal to zero.
flag：对象类"Uvector"观测，其的得分距离为较大比cutoff.sd或正交距离为大于可以被视为cutoff.od离群值和接收的标志等于零。




n.obs: Object of class "numeric" the number of observations
n.obs：类的对象"numeric"的若干意见


方法----------Methods----------




getCenter signature(obj = "Pca"): center of the data
getCentersignature(obj = "Pca")的数据中心




getScale signature(obj = "Pca"): return the scaling applied to each variable
getScale signature(obj = "Pca")：返回的比例换算为每个变量




getEigenvalues signature(obj = "Pca"): the eigenvalues of the  covariance/correlation matrix, though the calculation is actually done
getEigenvalues signature(obj = "Pca")的协方差/相关矩阵的特征值，但计算实际完成




getLoadings signature(obj = "Pca"): returns the matrix  loadings (i.e., a matrix whose columns contain the eigenvectors).
getLoadings signature(obj = "Pca")：返回矩阵loadings（即矩阵的列中包含的特征向量）。




getPrcomp signature(obj = "Pca"): returns an S3 object prcomp  for compatibility with the functions prcomp() and princomp(). Thus the
getPrcomp signature(obj = "Pca")：返回一个S3对象prcomp的功能prcomp（）和主成分法（）的相容性。因此，




getScores signature(obj = "Pca"):  returns the rotated data (the centred
getScores signature(obj = "Pca")：返回旋转的数据（居中




getSdev signature(obj = "Pca"): returns the standard deviations of the  principal components (i.e., the square roots of the eigenvalues of the  covariance/correlation matrix, though the calculation is actually done
getSdev signature(obj = "Pca")：返回标准差计算的主要组成部分（即协方差/相关矩阵的特征值的平方根，虽然实际上是




plot signature(x = "Pca"): produces a distance plot (if k=rank) or
图signature(x = "Pca")：产生一个距离图（如果k=rank）或




print signature(x = "Pca"): prints the results. The difference to the show()
打印signature(x = "Pca")：输出结果。差异的show（）




show signature(object = "Pca"): prints the results
显示signature(object = "Pca")：打印结果




predict signature(object = "Pca"): calculates prediction using the results in  object. An optional data frame or matrix in which to look for variables with which  to predict. If omitted, the scores are used. If the original fit used a formula or  a data frame or a matrix with column names, newdata must contain columns with the  same names. Otherwise it must contain the same number of columns,  to be used in the same order.  See also predict.prcomp and
预测signature(object = "Pca")：计算预测的结果object。一个可选的数据框或矩阵中寻找变量，用以预测。如果省略，则分数。 ，如果原始适合用一个公式或数据框或矩阵的列名，newdata必须包含列具有相同的名称。否则，它必须包含相同的列的数目，可以使用以相同的顺序。 predict.prcomp和




screeplot signature(x = "Pca"): plots the variances against the  number of the principal component. See also plot.prcomp and
screeplotsignature(x = "Pca")：图反对数的主要成分的方差。 plot.prcomp和


（作者）----------Author(s)----------


Valentin Todorov <a href="mailto:valentin.todorov@chello.at">valentin.todorov@chello.at</a>



参考文献----------References----------

An Object Oriented Framework for Robust Multivariate Analysis. Journal of Statistical Software, 32(3), 1&ndash;47. URL http://www.jstatsoft.org/v32/i03/.

参见----------See Also----------

PcaClassic, PcaClassic-class, PcaRobust-class
PcaClassic，PcaClassic-class，PcaRobust-class


实例----------Examples----------


showClass("Pca")

转载请注明:出自 生物统计家园网(http://www.biostatistic.net)。


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