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()
is a bare-bones
wrapper to the innermost QR-based C code, on which
glm.fit
and lsfit
are also based, for
even more 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, ...)
.lm.fit(x, y, tol = 1e-7)
x |
design matrix of dimension |
y |
vector of observations of length |
w |
vector of weights (length |
offset |
(numeric of length |
method |
currently, only |
tol |
tolerance for the |
singular.ok |
logical. If |
... |
currently disregarded. |
If y
is a matrix, offset
can be a numeric matrix of the
same dimensions, in which case each column is applied to the
corresponding column of y
.
a list
with components (for lm.fit
and lm.wfit
)
coefficients |
|
residuals |
|
fitted.values |
|
effects |
|
weights |
|
rank |
integer, giving the rank |
df.residual |
degrees of freedom of residuals |
qr |
the QR decomposition, see |
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.
lm
which you should use for linear least squares regression,
unless you know better.
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))
if(require("microbenchmark")) {
mb <- microbenchmark(lm(y~X), lm.fit(X,y), .lm.fit(X,y))
print(mb)
boxplot(mb, notch=TRUE)
}