# print.dglars: Printing a dgLARS Object In dglars: Differential Geometric Least Angle Regression

## Description

Print information about the sequence of models estimated by dgLARS method.

## Usage

 ```1 2``` ```## S3 method for class 'dglars' print(x, digits = max(3, getOption("digits") - 3), ...) ```

## Arguments

 `x` fitted `dglars` object `digits` significant digits in printout `...` additional print arguments

## Details

The call that produced the object `x` is printed, followed by a five-column `data.frame` with columns “`Sequence`”, “`g`”, “`Dev`”, “`%Dev`” and “`n. non zero`”. The column named “`Sequence`” gives information on how is changed the active set along the path. The column “`g`” shows the sequence of g values used to compute the solution curve, while the columns “`Dev`” and “`%Dev`” show the corresponding deviance and the fraction of explained deviance, respectively. Finally the “`n. non zero`” column shows the number of nonzero coefficients. The last part gives information about the algorithm and the method used to compute the solution curve. The code about the convergence of the used algorithm is also showed.

## Value

The `data.frame` above is silently returned.

## Author(s)

Luigi Augugliaro
Maintainer: Luigi Augugliaro [email protected]

`dglars` function.
 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24``` ```############################# # y ~ Binomial set.seed(123) n <- 100 p <- 100 X <- matrix(rnorm(n * p), n, p) eta <- 1 + 2 * X[,1] mu <- binomial()\$linkinv(eta) y <- rbinom(n, 1, mu) fit <- dglars(y ~ X, family = binomial) fit # adaptive dglars method b_wght <- coef(fit)\$beta[, 20] fit <- dglars(y ~ X, family = binomial, b_wght = b_wght) fit # the first three coefficients are not penalized fit <- dglars(y ~ X, family = binomial, unpenalized = 1:3) fit # 'probit' link function fit <- dglars(y ~ X, family = binomial("probit")) fit ```