Hi,
I've been running out of memory while using the lm.fit function - have
solved the problem and thought there might be interest in incorporating some
of the changes. Looked at the source and changed the following lines
storage.mode(x) <- "double"
storage.mode(y) <- "double"
z <- .Fortran("dqrls", qr = x, n = n, p = p, y = y, ny = ny,
tol = as.double(tol), coefficients = mat.or.vec(p, ny),
residuals = y, effects = y, rank = integer(1), pivot = 1:p,
qraux = double(p), work = double(2 * p), PACKAGE = "base")
to
if (storage.mode(x) != "double")
storage.mode(x) <- "double"
if (storage.mode(y) != "double")
storage.mode(y) <- "double"
z <- .Fortran("dqrls", qr = x, n = n, p = p, y = y, ny = ny,
tol = as.double(tol), coefficients = mat.or.vec(p, ny),
residuals = y, effects = y, rank = integer(1), pivot = 1:p,
qraux = double(p), work = double(2 * p), PACKAGE = "base",
DUP=F)
and I'm now able to call the function with data that is more than 4x larger
than before. Have read the manuals and am aware of the dangers of DUP=F,
but thought that if there was ever a fortran function that has been well
debugged this is a likely candidate. Will leave that up to the judgment of
those of you who actually know how it works.
The tests for storage.mode are probably less controversial, and actually
make a big difference in peak memory usage when the matrices are large.
Regards,
Laurens Leerink
