princomp(stats)
princomp()所属R语言包：stats
Principal Components Analysis
主成分分析
译者：生物统计家园网 机器人LoveR
描述----------Description----------
princomp performs a principal components analysis on the given numeric data matrix and returns the results as an object of class princomp.
princomp执行一个给定的数字数据矩阵的主成分分析，并返回一个对象类princomp的结果。
用法----------Usage----------
princomp(x, ...)
## S3 method for class 'formula'[类formula的方法]
princomp(formula, data = NULL, subset, na.action, ...)
## Default S3 method:[默认方法]
princomp(x, cor = FALSE, scores = TRUE, covmat = NULL,
subset = rep(TRUE, nrow(as.matrix(x))), ...)
## S3 method for class 'princomp'
predict(object, newdata, ...)
参数----------Arguments----------
参数：formula
a formula with no response variable, referring only to numeric variables.
一个没有响应变量的公式，只指数字变量。
参数：data
an optional data frame (or similar: see model.frame) containing the variables in the formula formula. By default the variables are taken from environment(formula).
一个可选的数据框（或类似：看到model.frame）包含公式formula的变量。默认情况下采取的变量从environment(formula)。
参数：subset
an optional vector used to select rows (observations) of the data matrix x.
一个可选的向量用于选择数据矩阵x行（观察）。
参数：na.action
a function which indicates what should happen when the data contain NAs. The default is set by the na.action setting of options, and is na.fail if that is unset. The "factory-fresh" default is na.omit.
一个函数，它表示数据时，包含NA的，应该发生什么。默认设置na.actionoptions设置，是na.fail如果没有设置。工厂新鲜的默认是na.omit。
参数：x
a numeric matrix or data frame which provides the data for the principal components analysis.
数字矩阵或数据框提供了主成分分析的数据。
参数：cor
a logical value indicating whether the calculation should use the correlation matrix or the covariance matrix. (The correlation matrix can only be used if there are no constant variables.)
一个逻辑值，该值指示是否计算应使用相关矩阵或协方差矩阵。 （相关矩阵只能被使用，如果没有不断的变量。）
参数：scores
a logical value indicating whether the score on each principal component should be calculated.
一个逻辑值，该值指示是否应计算各主要组成部分的得分。
参数：covmat
a covariance matrix, or a covariance list as returned by cov.wt (and cov.mve or cov.mcd from package MASS). If supplied, this is used rather than the covariance matrix of x.
协方差矩阵或协列表返回cov.wt（和cov.mve或cov.mcd包MASS）。如果提供，这是用来而非x的协方差矩阵。
参数：...
arguments passed to or from other methods. If x is a formula one might specify cor or scores.
参数传递或其他方法。 x如果是一个公式可能会指定cor或scores。
参数：object
Object of class inheriting from "princomp"
继承类的对象从"princomp"
参数：newdata
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.
一个可选的数据框或矩阵中寻找与预测的变数。如果省略，则使用分数。如果原来的适合用一个公式或一个数据框或一个矩阵的列名，newdata必须包含列具有相同的名称。否则，它必须包含相同的列数，要以相同的顺序使用。
Details
详情----------Details----------
princomp is a generic function with "formula" and "default" methods.
princomp是"formula"和"default"方法的通用功能。
The calculation is done using eigen on the correlation or covariance matrix, as determined by cor. This is done for compatibility with the S-PLUS result. A preferred method of calculation is to use svd on x, as is done in prcomp.
使用eigencor相关或协方差矩阵，确定计算完成。这样做的结果，S-PLUS的兼容性。一个计算的首选方法是使用svd上x，prcomp。
Note that the default calculation uses divisor N for the covariance matrix.
请注意，默认的计算使用的协方差矩阵除数N。
The print method for these objects prints the results in a nice format and the plot method produces a scree plot (screeplot). There is also a biplot method.
print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（screeplot）。还有一个biplot方法。
If x is a formula then the standard NA-handling is applied to the scores (if requested): see napredict.
如果x是一个公式，那么标准的NA处理申请的分数（如果要求）：看到napredict。
princomp only handles so-called R-mode PCA, that is feature extraction of variables. If a data matrix is supplied (possibly via a formula) it is required that there are at least as many units as variables. For Q-mode PCA use prcomp.
princomp只处理所谓的R-PCA的模式，是变量的特征提取。如果提供的数据矩阵（可能通过一个公式），它有许多单位如变量至少需要。对于Q型PCA的使用prcomp。
值----------Value----------
princomp returns a list with class "princomp" containing the following components:
princomp类"princomp"包含以下组件返回一个列表：
参数：sdev
the standard deviations of the principal components.
的主要组成部分的标准偏差。
参数：loadings
the matrix of variable loadings (i.e., a matrix whose columns contain the eigenvectors). This is of class "loadings": see loadings for its print method.
变载荷矩阵（即矩阵的列中包含的特征向量）。这类"loadings"：看到loadingsprint方法。
参数：center
the means that were subtracted.
被减去的手段。
参数：scale
the scalings applied to each variable.
结垢适用于每个变量。
参数：n.obs
the number of observations.
的若干意见。
参数：scores
if scores = TRUE, the scores of the supplied data on the principal components. These are non-null only if x was supplied, and if covmat was also supplied if it was a covariance list. For the formula method, napredict() is applied to handle the treatment of values omitted by the na.action.
如果scores = TRUE，所提供的数据的主要组成部分的分数。这些都是非空的，只有x提供，如果covmat也提供的，如果它是一个协列表。公式法，napredict()适用于处理治疗na.action遗漏值。
参数：call
the matched call.
匹配的呼叫。
参数：na.action
If relevant.
如果相关。
注意----------Note----------
The signs of the columns of the loadings and scores are arbitrary, and so may differ between different programs for PCA, and even between different builds of R.
负荷和分数列的标志是任意的，所以PCA不同的方案之间可能会有所不同，甚至不同版本之间的河
参考文献----------References----------
Multivariate Analysis, London: Academic Press.
Modern Applied Statistics with S, Springer-Verlag.
参见----------See Also----------
summary.princomp, screeplot, biplot.princomp, prcomp, cor, cov, eigen.
summary.princomp，screeplot，biplot.princomp，prcomp，cor，cov，eigen。
举例----------Examples----------
require(graphics)
## The variances of the variables in the[＃在变量的方差]
## USArrests data vary by orders of magnitude, so scaling is appropriate[＃USArrests数据不同量级，所以适当的比例是]
(pc.cr <- princomp(USArrests)) # inappropriate[不当]
princomp(USArrests, cor = TRUE) # =^= prcomp(USArrests, scale=TRUE)[= ^ = prcomp（USArrests，规模= TRUE）]
## Similar, but different:[＃类似，但不同的：]
## The standard deviations differ by a factor of sqrt(49/50)[＃标准偏差不同因素的SQRT（49/50）]
summary(pc.cr <- princomp(USArrests, cor = TRUE))
loadings(pc.cr) ## note that blank entries are small but not zero[＃注意，虽小，但不为零的空白条目]
plot(pc.cr) # shows a screeplot.[显示一个screeplot。]
biplot(pc.cr)
## Formula interface[＃公式接口]
princomp(~ ., data = USArrests, cor = TRUE)
## NA-handling[＃不适用处理]
USArrests[1, 2] <- NA
pc.cr <- princomp(~ Murder + Assault + UrbanPop,
data = USArrests, na.action=na.exclude, cor = TRUE)
pc.cr$scores[1:5, ]
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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。
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发表于 2012-2-16 18:25:41
