coef.dglars {dglars} | R Documentation |
coef.dglars
is used to extract the coefficient path computed by dgLARS method.
## S3 method for class 'dglars' coef(object, type = c("pearson", "deviance", "mle", "grcv"), g = NULL, ...)
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 |
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 ...
).
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. |
Luigi Augugliaro
Maintainer: Luigi Augugliaro luigi.augugliaro@unipa.it
predict.dglars
, phihat
and grcv
.
########################### # 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")