coef.dglars {dglars} | R Documentation |
Extract the dgLARS Coefficient Path
Description
coef.dglars
is used to extract the coefficient path computed by dgLARS method.
Usage
## S3 method for class 'dglars'
coef(object, type = c("pearson", "deviance", "mle", "grcv"),
g = NULL, ...)
Arguments
object |
fitted |
type |
a description of the estimator used for the dispersion parameter. |
g |
vector of values of the tuning parameter. |
... |
further arguments passed to the function |
Details
coef.dglars
is a wrapper function calling “predict.dglars
” and “phihat
”. By default, this function returns the sequence of the penalized coefficients and the sequence of the penalized estimate of the dispersion parameter \phi
. The user can specify the argumnets of the function grcv
by the argument ...
).
Value
coef.dglars
returns a named list with component:
beta |
the sequence of the penalized estimates of the regression coefficients; |
phi |
the penalized estimates of the dispersion parameter; |
g |
the vector of the values of the tuning parameter. |
Author(s)
Luigi Augugliaro
Maintainer: Luigi Augugliaro luigi.augugliaro@unipa.it
See Also
predict.dglars
, phihat
and grcv
.
Examples
###########################
# Logistic regression model
set.seed(123)
n <- 100
p <- 10
X <- matrix(rnorm(n * p), n, p)
b <- 1:2
eta <- b[1] + X[, 1] * b[2]
mu <- binomial()$linkinv(eta)
y <- rbinom(n, 1, mu)
fit <- dglars(y ~ X, family = binomial)
coef(fit)
coef(fit, g = seq(4, 0.5, length = 10))
###########################
# Gamma family
n <- 100
p <- 10
X <- matrix(abs(rnorm(n * p)), n, p)
b <- 1:2
eta <- b[1] + X[, 1] * b[2]
mu <- drop(Gamma()$linkinv(eta))
shape <- 0.5
phi <- 1 / shape
y <- rgamma(n, scale = mu / shape, shape = shape)
fit <- dglars(y ~ X, Gamma("log"))
coef(fit, type = "pearson")
coef(fit, type = "deviance")
coef(fit, type = "mle")