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 coeffcients, 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`.

`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))

```

[Package stats version 2.5.0 Index]