coef.dglars: Extract the dgLARS Coefficient Path In dglars: Differential Geometric Least Angle Regression

Description

`coef.dglars` is used to extract the coefficient path computed by dgLARS method.

Usage

 ```1 2 3``` ```## S3 method for class 'dglars' coef(object, type = c("pearson", "deviance", "mle"), ...) ```

Arguments

 `object` fitted `dglars` object. `type` a description of the estimator used for the dispersion parameter. `...` this is the argument by means of to pass the argument `g` to `predict.dglars` and `phihat`.

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; by using the argument “`...`”, the user can obtain the prediction of the estimates for any value of the tuning parameter (see the example below).

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 [email protected]

`predict.dglars` and `phihat`.
 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29``` ```########################### # 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") ```