lasso.rand {gespeR} | R Documentation |
Based on Meinshausen and Buehlmann (2009)
lasso.rand(x, y, weakness = 1, subsample = 1:nrow(x), dfmax = (ncol(x) + 1), lambda = NULL, standardize = FALSE, intercept = FALSE, ...)
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 |
A glmnet
object
Fabian Schmich
y <- rnorm(50) x <- matrix(runif(50 * 20), ncol = 20) lasso.rand(x = x, y = y)