| 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")