R: Compute weights for use with adaptive lasso.

adaptive.weights {MESS} | R Documentation

## Compute weights for use with adaptive lasso.

### Description

Fast computation of weights needed for adaptive lasso based on Gaussian family data.

### Usage

    adaptive.weights(x, y, nu = 1, weight.method = c("multivariate", "univariate"))

### Arguments

`x` | input matrix, of dimension nobs x nvars; each row is an observation vector.
`y` | response variable.
`nu` | non-negative tuning parameter
`weight.method` | Should the weights be computed for multivariate regression model (only possible when the number of observations is larger than the number of parameters) or by individual marginal/"univariate" regression coefficients.

### Details

The weights returned are 1/abs(beta_hat)^nu where the beta-parameters are estimated from the corresponding linear model (either multivariate or univariate).

### Value

Returns a list with two elements:

`weights` | the computed weights
`nu` | the value of nu used for the computations

### Author(s)

Claus Ekstrom claus@rprimer.dk

### References

Xou, H (2006). The Adaptive Lasso and Its Oracle Properties. JASA, Vol. 101.

### See Also

`glmnet`

### Examples

    set.seed(1)
    x <- matrix(rnorm(50000), nrow=50)
    y <- rnorm(50, mean=x[,1])
    weights <- adaptive.weights(x, y)

    if (requireNamespace("glmnet", quietly = TRUE)) {
        res <- glmnet::glmnet(x, y, penalty.factor=weights$weights)
        head(res)
    }

[Package MESS version 0.5.9 Index]