[R] applying lm on an array of observations with common design matrix

[Prof Brian Ripley]

On Sat, 17 Feb 2007, Ranjan Maitra wrote:

> Dear list,
>
> I have a 4-dimensional array Y of dimension 330 x 67 x 35 x 51. I have a 
> design matrix X of dimension 330 x 4. I want to fit a linear regression 
> of each
>
> lm( Y[, i, j, k] ~ X). for each i, j, k.
>
> Can I do it in one shot without a loop?

Yes.

YY <- YY
dim(YY) <- c(330, 67*35*51)
fit <- lm(YY ~ X)

> Actually, I am also interested in getting the p-values of some of the 
> coefficients -- lets say the coefficient corresponding to the second 
> column of the design matrix. Can the same be done using array-based 
> operations?

Use lapply(summary(fit), function(x) coef(x)[3,4])  (since there is a 
intercept, you want the third coefficient).

Note that this will give a vector, so set its dimension to c(67,35,51) to 
relate to the original array.

I have not BTW looked into the memory requirements here, and you might 
want to do this on slices of the array for that reason.

-- 
Brian D. Ripley,                  ripley at stats.ox.ac.uk
Professor of Applied Statistics,  http://www.stats.ox.ac.uk/~ripley/
University of Oxford,             Tel:  +44 1865 272861 (self)
1 South Parks Road,                     +44 1865 272866 (PA)
Oxford OX1 3TG, UK                Fax:  +44 1865 272595