On Thu, 20 May 2010, ivo welch wrote:
hi thomas---
thanks for the answer. the problem with the "+factor(fmid)" is not just
that it provides uninteresting coefficients and that it eats more memory,
but that it is also MUCH slower when there are (hundred of) thousands of
fixed effects.
There is also the plm() function in the "plm" package which provides fixed
effects models (among many other models for panel data).
However, if I recall correctly, it internally employs the full regressor
matrix, not the demeaned one. More details are explained in the vignette,
though:
vignette("plm", package = "plm")
hth,
Z
Does Bill Venables describe how to do extend the lm() function? I googled
"course notes on advanced programming Venables", but did not find it. Do
you have a better link? (hopefully, this is a short explanation---I know
the algorithm. I want to learn how to coax it into an lm statement.)
regards,
/iaw
----
Ivo Welch (ivo.we...@brown.edu, ivo.we...@gmail.com)
I would just have used lm(y~x+z+factor(firmid)). Admittedly, you get a
whole bunch of uninteresting coefficients in the output, but it's not that
hard to subset them out.
There are two implementation of this in Bill Venables' course notes on
advanced programming. I think they are also in 'S Programming', but I can't
find my copy right now. These were motivated by computational problems: the
full design matrix for the linear model was too large for memory at the time
(last century).
As a final note, I would strongly discourage
r= lm( y ~ x + z, fixed.effects=firmid )
as a specification, and would argue for
r= lm( y ~ x + z, fixed.effects=~firmid )
I think the ability to have some subset of the arguments in a modelling
call silently treated as formulas was a bad decision, although it must have
looked user-friendly at the time.
-thomas
Thomas Lumley Assoc. Professor, Biostatistics
tlum...@u.washington.edu University of Washington, Seattle
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