[R] faraway tutorial: cryptic command to newbie
chong at stat.purdue.edu
Mon Feb 24 19:53:03 CET 2003
Not only it's unfair criticism, it's probably also imprecise
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
> 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,
> >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
> >and is close to the worse possible way. (If you must use normal
> >equations, do at least centre the columns, and preferably do some
> >> 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 -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.
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