## The Posts

The purpose of this post is to provide guidance to a reader who has just discovered that I have a large pile of posts about principal components / factor analysis. This pile of posts might seem a very jungle, without any map.

Here, have a map.

As I finalize this post, it will be number 52 in PCA / FA. Here’s a list of the 52 posts, including the dates spanned by any group, and the number of posts in that group. (When the picture was taken, I didn’t know when this would be published. In fact, post 51 was scheduled but not yet published. Even more, post 51 did not even exist when the first picture was created.)

transition/attitude matrices is a post that is sometimes relevant when we discuss “new data” in PCA, but it is not in the PCA / FA category.

“tricky prepro” is short for “tricky preprocessing”, and discusses the combination of constant row sums and covariance or correlation matrix.
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## PCA / FA. Example 4 ! Davis, and almost everyone else

I would like to revisit the work we did in Davis (example 4). For one thing, I did a lot of calculations with that example, and despite the compare-and-contrast posts towards the end, I fear it may be difficult to sort out what I finally came to.

In addition, my notation has settled down a bit since then, and I would like to recast the work using my current notation.

The original (“raw”) data for example 4 was (p. 502, and columns are variables):

$X_r = \left(\begin{array}{lll} 4 & 27 & 18 \\ 12 & 25 & 12 \\ 10 & 23 & 16 \\ 14 & 21 & 14\end{array}\right)$
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## PCA/FA Answers to some Basilevsky questions

Let us look at three of the questions I asked early in February, and answer two of them.

## First, what do we know? What have we done?

We assume that we have data X, with the variables in columns, as usual. In fact, we assume that the data is at least centered, and possibly standardized.

We compute the covariance matrix

$c = \frac{X^T X}{N-1}\$,

then its eigendecomposition

$c = v\ \Lambda^2\ v^T\$,

where $\Lambda^2$ is the diaginal matrix of eigenvalues. We define the $\sqrt{\text{eigenvalue}}$-weighted matrix

$A = v\ \Lambda\$.

Finally, we use A as a transition matrix to define new data Z:

$X^T = A\ Z^T\$.

We discovered two things. One, the matrix A is the cross covariance between Z and X:

$A = \frac{X^T Z}{N-1}\$.

I find this interesting, and I suspect that it would jump off the page at me out of either Harman or Jolliffe; that is, I suspect it is written there but it didn’t register.

Two, we discovered that we could find a matrix Ar which is the cross covariance between Zc and Xs. Read the rest of this entry »

## Introduction

I am looking into Basilevsky because he did something I didn’t understand: he normalized the rows of the Ac matrix (which I denoted Ar). We discussed that, and we illustrated the computations, in the previous two posts. But we did those computations without having any data. I want to take a closer look, with data.

In contrast to As and Ac, which are eigenvector matrices, his Ar matrix is not. Nevertheless, as I said, his Ar is not without some redeeming value. In fact, all three of the A matrices have the same redeeming value.

I will show, first by direct computation and then by proof, that each of these A matrices is the cross covariance between X data and Z data.
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## Introduction

The previous post discussed something interesting that Basilevsky did. (Search the bibliography, he’s there.) I can’t say I like it — because it leads to a basis which is non-orthogonal, and whose vectors are not eigenvectors of either the correlation or covariance matrices.

But I had to understand it.

I don’t know how widespread it is nowadays, but even if Basilevsky is the only author who does it, it’s another example of the lack of standardization (no pun intend, I swear) in PCA / FA. This branch of applied statistics is like the mythical Wild West: everybody’s got a gun and there are bullets flying all over the place. Law and order have not arrived yet.

OTOH, it’s nice to find something different in just about every book I open.
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## Introduction and review

Basilevsky presents an extremely interesting idea. For all I know, it’s become common in the last 10-20 years, but I hadn’t seen it in any of the other books we’ve looked at.

I’ll tell you up front that he’s going to normalize the rows of an A matrix, specifically the A matrix computed from the eigendecomposition of the covariance matrix.

I’ll also tell you up front that I don’t see any good reason for doing it, but I’m not averse to finding such a reason someday.
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## PCA / FA example 9: centered and raw, 3 models

What follows is simple computation, solely to show us exactly what happens. It continues the work of the previous post, which did my default calculations for standardized data. Here I do the same calculations for centered and raw data.

## Centered

The raw data is still

$\text{raw} = \left(\begin{array}{lll} 2.09653 & -0.793484 & -7.33899 \\ -1.75252 & 13.0576 & 0.103549 \\ 3.63702 & 29.0064 & 8.52945 \\ 0.0338101 & 46.912 & 19.8517 \\ 5.91502 & 70.9696 & 36.0372\end{array}\right)$

I will center the data and call it Xc. Get the column means…

$\{1.98597,\ 31.8304,\ 11.4366\}$

and subtract them from each column to get Xc:
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