lasso.rand {gespeR} | R Documentation

## Randomized Lasso

### Description

Based on Meinshausen and Buehlmann (2009)

### Usage

    lasso.rand(x, y, weakness = 1, subsample = 1:nrow(x), dfmax = (ncol(x) +
      1), lambda = NULL, standardize = FALSE, intercept = FALSE, ...)

### Arguments

`x` | The design matrix
`y` | The response vector
`weakness` | The weakness parameter
`subsample` | The data subsample (default: none)
`dfmax` | The maxiumum number of degrees of freedom
`lambda` | The regularisation parameter
`standardize` | Indicator, wheter to standardize the design matrix
`intercept` | Indicator, whether to fit an intercept
`...` | Additional arguments to `glmnet`

### Value

A `glmnet` object

### Author(s)

Fabian Schmich

### Examples

    y <- rnorm(50)
     x <- matrix(runif(50 * 20), ncol = 20)
     lasso.rand(x = x, y = y)

[Package gespeR version 1.22.0 Index]