# lm.fit

##### Fitter Functions for Linear Models

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. `.lm.fit()`

is bare bone
wrapper to the innermost QR-based C code, on which
`glm.fit`

and `lsfit`

are based as well, for
even more experienced users.

- Keywords
- regression, array

##### 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, …)

.lm.fit(x, y, tol = 1e-7)

##### 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 (for `lm.fit`

and `lm.wfit`

)

`p`

vector

`n`

vector or matrix

`n`

vector or matrix

`n`

vector of orthogonal single-df
effects. The first `rank`

of them correspond to non-aliased
coefficients, and are named accordingly.

`n`

vector --- *only* for the `*wfit*`

functions.

integer, giving the rank

degrees of freedom of residuals

the QR decomposition, see `qr`

.

Fits without any columns or non-zero weights do not have the effects and qr components.

.lm.fit() returns a subset of the above, the qr part unwrapped, plus a logical component pivoted indicating if the underlying QR algorithm did pivot.

##### See Also

`lm`

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

##### Examples

`library(stats)`

```
require(utils)
<!-- %% FIXME: Do something more sensible (non-random data) !! -->
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))
if(require("microbenchmark")) {
mb <- microbenchmark(lm(y~X), lm.fit(X,y), .lm.fit(X,y))
print(mb)
boxplot(mb, notch=TRUE)
}
<!-- %% do an example which sets 'tol' and gives a difference! -->
```

*Documentation reproduced from package stats, version 3.4.1, License: Part of R 3.4.1*