# grouplasso-methods: Fit a linear regression model the group lasso penalty In netReg: Network-Regularized Regression Models

## Description

Fit a linear regression model the group LASSO penalty.

## Usage

 ``` 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 30 31 32 33 34``` ```group.lasso( X, Y, grps = NULL, lambda = 1, thresh = 1e-05, maxit = 1e+05, learning.rate = 0.01, family = gaussian ) ## S4 method for signature 'matrix,numeric' group.lasso( X, Y, grps = NULL, lambda = 1, thresh = 1e-05, maxit = 1e+05, learning.rate = 0.01, family = gaussian ) ## S4 method for signature 'matrix,matrix' group.lasso( X, Y, grps = NULL, lambda = 1, thresh = 1e-05, maxit = 1e+05, learning.rate = 0.01, family = gaussian ) ```

## Arguments

 `X` input matrix, of dimension (`n` x `p`) where `n` is the number of observations and `p` is the number of covariables. Each row is an observation vector. `Y` output matrix, of dimension (`n` x `q`) where `n` is the number of observations and `q` is the number of response variables. Each row is an observation vector. `grps` vector of integers or `NA_integer_` of length `p` that encodes the grouping of variables, e.g., `c(1, 1, 2, 2, NA)` `lambda` `numerical` shrinkage parameter `thresh` `numerical` threshold for optimizer `maxit` maximum number of iterations for optimizer (`integer`) `learning.rate` step size for Adam optimizer (`numerical`) `family` family of response, e.g., gaussian or binomial

## Value

An object of class `edgenet`

 `beta ` the estimated (`p` x `q`)-dimensional coefficient matrix B.hat `alpha ` the estimated (`q` x `1`)-dimensional vector of intercepts `parameters ` regularization parameters `lambda ` regularization parameter lambda) `family ` a description of the error distribution and link function to be used. Can be a `netReg::family` function or a character string naming a family function, e.g. `gaussian` or "gaussian". `call ` the call that produced the object

## References

Yuan, Ming and Lin, Yi (2006), Model selection and estimation in regression with grouped variables.
Journal of the Royal Statistical Society: Series B

Meier, Lukas and Van De Geer, Sara and Bühlmann, Peter (2008), The group lasso for logistic regression.
Journal of the Royal Statistical Society: Series B

## Examples

 ```1 2 3 4 5 6 7``` ```X <- matrix(rnorm(100 * 10), 100, 5) b <- rnorm(5) grps <- c(NA_integer_, 1L, 1L, 2L, 2L) # estimate the parameters of a Gaussian model Y <- X %*% b + rnorm(100) fit <- group.lasso(X = X, Y = Y, grps = grps, family = gaussian, maxit = 10) ```

netReg documentation built on Nov. 8, 2020, 5:17 p.m.