R: Fitter Functions for Linear Models

lm.fit {stats}

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,
       singular.ok = TRUE, ...)

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

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

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