| lm.fit | 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 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.
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. |
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)
}
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