# hgam {hgam}

Fitting high-dimensional generalized additive models

### Description

`hgam`

is used to fit high-dimensional generalized additive models.

### Usage

hgam(formula, data = NULL, weights, model = LinReg(), nknots = 20, lambda1 = 2, lambda2 = 3, ...)

### Arguments

- formula
- an object of class
`formula`

(or one that can be coerced to that class): a symbolic description of the model to be fitted. - data
- a data frame.
- weights
- vector of weights.
- model
- an object of class
`grpl.model`

implementing the negative log-likelihood, gradient, hessian etc. See the documentation of`grpl.model`

for more details. - nknots
- number of knots.
- lambda1
- grouplasso penalty term.
- lambda2
- smoothing penalty term.
- ...
- ignored.

### Values

`hgam`

returns an object of class `hgam`

:

- y
- response
- x
- covariables
- Btilde
- model matrix
- coef
- coefficients
- Btildenew
- function to set up the model matrix for (new) data

### See Also

`grplasso`

### Examples

test.d <- dgp(1000) test.m <- hgam(y ~ ., data = test.d)

Documentation reproduced from package hgam, version 0.1-2. License: GPL-2