lm.fit {stats}R Documentation

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 (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, ...)


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.


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.

See Also

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


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]