faraway tutorial: cryptic command to newbie

-----Original Message-----
From: Chong Gu [mailto:chong at stat.purdue.edu]
Sent: Monday, February 24, 2003 1:51 PM
To: Julian Faraway
Cc: r-help at stat.math.ethz.ch
Subject: Re: [R] faraway tutorial: cryptic command to newbie



Not only it's unfair criticism, it's probably also imprecise
information.

For a detailed discussion of the precisions of regression estimates
through QR-decomposition and normal equations, one may consult Golub
and Van Loan's book on Matrix Computation (1989, Section 5.3.9 on page
230).  QR takes twice as much computation, requires more memory, but
does NOT necessarily provide better precision.

The above said, I am not questioning the adequacy of the QR approach
to regression calculation as implemented in R.

That's an unfair criticism. That discussion was never intended as 
a recommendation for how to compute a regression. Of course, SVD or
QR decompositions are the preferred method but many newbies 

don't want to 

digest all that right from the start. These are just 

obscure details to 

the beginner.

One of the strengths of R in teaching is that students can directly 
implement the formulae from the theory. This reinforces the 

connection 

between theory and practice. Implementing the normal 

equations directly

is a quick early illustration of this connection. 

Explaining the precise 

details of how to fit a regression model is something that can be 
deferred.

Julian Faraway

I am just about working through Faraways excellent 

tutorial "practical

regression and ANOVA using R"

I assume this is a reference to the PDF version available 

via CRAN. I am

afraid that is *not* a good discussion of how to do regression, 

especially

not using R.  That page is seriously misleading: good ways 

to compute

regressions are QR decompositions with pivoting (which R 

uses) or an SVD.

Solving the normal equations is well known to square the condition 

number,

and is close to the worse possible way.  (If you must use normal
equations, do at least centre the columns, and preferably do some 
scaling.)

on page 24 he makes the x matrix:
x <- cbind(1,gala[,-c(1,2)])

how can I understand this gala[,-c(1,2)])... I couldn't find an
explanation of such "c-like" abbreviations anywhere.

Well, it is in all good books (as they say) including `An 

Introduction to 

R'. (It's even on page 210 of that book!)

-c(1,2) is (try it)

-c(1,2)

[1] -1 -2

so this drops columns 1 and 2.  It then adds in front a 

column made up of 

ones, which is usually a sign of someone not really 

understanding how

R's linear models work.