| lm.fit {stats} | R Documentation |
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. |
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. |
The functions lm.{w}fit.null are called by lm.fit or
lm.wfit respectively, when x has zero columns.
a list with components
coefficients |
p vector |
residuals |
n vector |
fitted.values |
n vector |
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.
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))