lm.fit {base}

R Documentation

Fitter Functions for Linear Models

Description

These are the basic computing engines called by lm used to fit linear models. These should usually not be used directly unless by experienced users.

Usage

lm.fit (x, y,    offset = NULL, method = "qr", tol = 1e-7, ...)
lm.wfit(x, y, w, offset = NULL, method = "qr", tol = 1e-7, ...)
lm.fit.null (x, y,    method = "qr", tol = 1e-7, ...)
lm.wfit.null(x, y, w, method = "qr", tol = 1e-7, ...)

Arguments

x	design matrix of dimension n * p.
y	vector of observations of length n.
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.
tol	tolerance for the qr decomposition. Default is 1e-7.
...	currently disregarded.

Details

The functions lm.{w}fit.null are called by lm.fit or lm.wfit respectively, when x has zero columns.

Value

a list with components

coefficients	p vector
residuals	n vector
fitted.values	n vector
effects	n vector; ......
weights	n vector — only for the *wfit* functions.
rank	integer, giving the rank
df.residual	degrees of freedom of residuals
qr	the QR decomposition, see qr.

See Also

lm which you should use for linear least squares regression, unless you know better.

Examples

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 <- lm.wfit(x=X, y=y, w=w))

str(lm. <- lm.fit (x=X, y=y))

str(lm0 <-  lm.fit.null (x=X, y=y))
str(lmw0 <- lm.wfit.null(x=X, y=y,w=w))

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