This will give the coefficients of each regression for which there are
no missing values in the dependent variable and NAs for the rest:
> # test data
> set.seed(123)
> y <- cbind(y1 = 1:4, y2 = c(NA, 2:4))
> x <- 1:4 + rnorm(4)
>
> qr.coef(qr(cbind(1, x)), y)
y1 y2
0.8607244 NA
x 0.6049789 NA
On Fri, Jun 11, 2010 at 8:49 AM, ivo welch <ivo...@gmail.com> wrote:
> this is not an important question, but I wonder why lm returns an
> error, and whether this can be shut off. it would seem to me that
> returning NA's would make more sense in some cases---after all, the
> problem is clearly that coefficients cannot be computed.
>
> I know that I can trap the lm.fit() error---although I have always
> found this to be quite inconvenient---and this is easy if I have only
> one regression in my lm() statement.
>
> but, let's presume I have a matrix with a few thousand dependent y
> variables (and the same independent X variables). Let's presume one
> of the y variables contains only NA's. I believe I now cannot use
> lm(y ~ X), because one of the regressions will throw the lm.fit
> exception. (all the other y vectors should have worked.)
>
> or is there a way to get lm() to work in such situations?
>
> /iaw
>
> ----
> Ivo Welch (ivo.we...@brown.edu, ivo.we...@gmail.com)
>
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>
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