gpuLm.fit: Fitter functions for gpu enabled linear models

Description Usage Arguments Value See Also Examples

View source: R/gpuLm.R

Description

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

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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

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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))

gputools documentation built on May 30, 2017, 1:52 a.m.

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