data.table vs plyr regression output

Question

The data.table package is very helpful in terms of speed. But I am having trouble actually using the output from a linear regression. Is there an easy way to get the data.table output to be as pretty/useful as that from the plyr package? Below is an example. Thank you!

library('data.table');
library('plyr');

REG <- data.table(ID=c(rep('Frank',5),rep('Tony',5),rep('Ed',5)), y=rnorm(15), x=rnorm(15), z=rnorm(15));
REG;

ddply(REG, .(ID), function(x) coef(lm(y ~ x + z, data=x)));

REG[, coef(lm(y ~ x + z)), by=ID];

The data.table coefficient estimates are output in a single column whereas the plyr/ddply coefficient estimates are output in multiple and nicely labeled columns.

I know I can run the regression three times with data.table but that seems really inefficient. I could be wrong, though.

REG[, Intercept=coef(lm(y ~ x + z))[1],
      x        =coef(lm(y ~ x + z))[2],
      z        =coef(lm(y ~ x + z))[3], by=ID];

Answer 1

Try this:

> REG[, as.list(coef(lm(y ~ x + z))), by=ID];
        ID (Intercept)           x         z
[1,] Frank  -0.2928611  0.07215896  1.835106
[2,]  Tony   0.9120795 -1.11153056  2.041260
[3,]    Ed   1.0498359  5.77131778 -1.253741

I have the nagging feeling that this question was asked less than a week ago, but I don't think I arrived at this approach when I tried it and I don't remember than any answer was this compact.

Oh, there it is .. on r-help. Matthew can comment on the rightfulness of this if he wants. I guess the message is that functions returning lists will not have dimensions dropped. The interesting thing was the using list(coef(lm(...)) did not succeed in the manner we hoped.