bigGlm {glmnet} | R Documentation |
glmnet
Fit a generalized linear model as in glmnet
but unpenalized. This
allows all the features of glmnet
such as sparse x, bounds on
coefficients, offsets, and so on.
bigGlm(x, ..., path = FALSE)
x |
input matrix |
... |
Most other arguments to glmnet that make sense |
path |
Since |
This is essentially the same as fitting a "glmnet" model with a single value
lambda=0
, but it avoids some edge cases. CAVEAT: If the user tries a
problem with N smaller than or close to p for some models, it is likely to
fail (and maybe not gracefully!) If so, use the path=TRUE
argument.
It returns an object of class "bigGlm" that inherits from class
"glmnet". That means it can be predicted from, coefficients extracted via
coef
. It has its own print method.
Trevor Hastie
Maintainer: Trevor Hastie
hastie@stanford.edu
print
, predict
, and coef
methods.
# Gaussian
x = matrix(rnorm(100 * 20), 100, 20)
y = rnorm(100)
fit1 = bigGlm(x, y)
print(fit1)
fit2=bigGlm(x,y>0,family="binomial")
print(fit2)
fit2p=bigGlm(x,y>0,family="binomial",path=TRUE)
print(fit2p)