The C code called by this function was written by
Mark Seligman at Rapid Biologics, http://rapidbiologics.com
The function gpuLm calls this function
to fit linear models. So gpuLm.fit should not need to be used
directly.
Usage
gpuLm.fit(x, y, w = NULL, offset = NULL, method = "qr",
useSingle, tol = gpuLm.defaultTol(useSingle), singular.ok = TRUE, ...)
Arguments
x
design matrix of dimension n * p.
y
vector of observations of length n, or a matrix with
n rows.
w
vector of weights (length n) to be used in the fitting
process for the wfit functions. Weighted least squares is
used with weights w, i.e., sum(w * e^2) is minimized.
offset
numeric of length n). This can be used to
specify an a priori known component to be included in the
linear predictor during fitting.
method
currently, only method="qr" is supported.
useSingle
logical. If TRUE, the gpu will use single precision
arithmetic. In the future, if FALSE the gpu may use double precision
arithmetic, but this is not implemented yet.
tol
tolerance for the qr decomposition. Default
is 1e-7.
singular.ok
logical. If FALSE, a singular model is an
error.
...
currently disregarded.
Value
a list with components
coefficients
p vector
residuals
n vector or matrix
fitted.values
n vector or matrix
effects
(not null fits)n vector of orthogonal single-df
effects. The first rank of them correspond to non-aliased
coefficients, and are named accordingly.
weights
n vector — only for the *wfit*
functions.
rank
integer, giving the rank
df.residual
degrees of freedom of residuals
qr
(not null fits) the QR decomposition, see qr.
See Also
gpuLm which should usually be used for linear least squares
regression
Examples
require(utils)
set.seed(129)
n <- 7 ; p <- 2
X <- matrix(rnorm(n * p), n,p) # no intercept!
y <- rnorm(n)
w <- rnorm(n)^2
str(lmw <- gpuLm.fit(x=X, y=y, w=w))