| lm.fit {pbdDMAT} | R Documentation |
Fits a real linear model via QR with a "limited pivoting strategy", as in R's DQRDC2 (fortran).
## S4 method for signature 'ddmatrix,ddmatrix' lm.fit(x, y, tol = 1e-07, singular.ok = TRUE)
x, y |
numeric distributed matrices |
tol |
tolerance for numerical rank estimation in QR decomposition. |
singular.ok |
logical. If |
Solves the linear least squares problem, which is to find an x
(possibly non-uniquely) such that || Ax - b ||^2 is minimized, where
A is a given n-by-p model matrix, b is a "right hand side"
n-by-1 vector (multiple right hand sides can be solved at once, but the
solutions are independent, i.e. not simultaneous), and "||" is the l2 norm.
Uses level 3 PBLAS and ScaLAPACK routines (modified PDGELS) to get a linear least squares solution, using the 'limited pivoting strategy' from R's DQRDC2 (unsed in DQRLS) routine as a way of dealing with (possibly) rank deficient model matrices.
A model matrix with many dependent columns will likely experience poor performance, especially at scale, due to all the data swapping that must occur to handle rank deficiency.
Returns a list of values similar to R's lm.fit(). Namely, the
list contains:
coefficients | (distributed matrix) solution to the linear least squares problem |
residuals | (distributed matrix) difference in the numerical fit and the observed |
effects | (distributed matrix) t(Q) %*% y |
rank | (global numeric) numerical column rank |
fitted.values | (distributed matrix) Numerical fit A %*% x |
assign | NULL if lm.fit() is called directly |
qr | list, same as return from qr() |
df.residual | (global numeric) degrees of freedom of residuals |
## Not run: # Save code in a file "demo.r" and run with 2 processors by # > mpiexec -np 2 Rscript demo.r library(pbdDMAT, quiet = TRUE) init.grid() # don't do this in production code x <- matrix(rnorm(9), 3) y <- matrix(rnorm(3)) dx <- as.ddmatrix(x) dy <- as.ddmatrix(y) fit <- lm.fit(x=dx, y=dy) print(fit) finalize() ## End(Not run)