Fitter functions for gpu enabled linear models

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 = getOption("gputools.precision", "single") == "single",
  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: TRUE computes in single precision on the GPU, FALSE in double. The default follows options(gputools.precision): single unless that is "double". It also sets the default tolerance (see gpuLm.defaultTol).

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

if (FALSE) { # gpuAvailable()
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))
}