在FactoMiner R中提取主要成分 [英] Extracting Principal Components in FactoMiner R

问题描述

我正在尝试使用FactoMiner中的PCA提取协方差矩阵的主成分.但是，由于某种原因，我只在var-> coord变量中看到n-1个分量

I am trying to extract the principal components for a covariance matrix using PCA in FactoMiner. However, for some reason , I only see n-1 components in the var-->coord variable

library(FactoMineR)
x = matrix(rnorm(10000),nrow = 100,ncol = 100)
y = PCA(x,ncp = 100,graph = FALSE)
dim(y$var$coord)

这将产生100 99的输出.我是这个软件包的新手，希望能获得更多的见解

This leads to an output of 100 99. I am new to this package and hope to get more insights

推荐答案

在对p个变量和n个观察值进行的主成分分析中，最大维数为min(p; n-1).您有一个100x100的矩阵，所以它将是min(100; 99)=1.尝试使用100x101矩阵，您将能够提取100个尺寸:

The maximum number of dimensions in a principal component analysis performed on p variables and n observations is min(p;n-1). You have a matrix of 100x100, so that would be min(100;99) = 1. Try this with a 100x101 matrix and you will be able to extract 100 dimensions:

x = matrix(rnorm(10100),nrow = 101,ncol = 100)
y = PCA(x,ncp = 100,graph = FALSE)
dim(y$var$coord)
[1] 100 100

也就是说，PCA的全部目的是将数据缩小到几个维度，因此尝试全部使用它们会破坏其目的.

That said, the whole point of PCA is to reduce your data to a few dimensions, so trying to use them all defeats its purpose.

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