Description Usage Arguments Details
View source: R/mxPenaltyFunctions.R
Elastic net regularization
1 2 3 4 5 6 7 8 9 10 11 12 | mxRegularizeElasticNet(
what,
name,
alpha = 0,
alpha.step = 0.1,
alpha.max = 1,
lambda = 0,
lambda.step = 0.1,
lambda.max = 0.4,
alpha.min = NA,
lambda.min = NA
)
|
what |
A list of parameters to regularize |
name |
Name of the regularizer object |
alpha |
strength of the mixing parameter to be applied at start (default 0.5). Note that 0 indicates a ridge regression with penalty lambda / 2 , and 1 indicates a LASSO regression with penalty lambda. |
alpha.step |
alpha step during penalty search (default 0.1) |
alpha.max |
when to end the alpha search (default 1) |
lambda |
strength of the penalty to be applied at starting values (default 0) |
lambda.step |
step function for lambda step (default .01) |
lambda.max |
end of lambda range (default .4) |
alpha.min |
beginning of the alpha range (default 0) |
lambda.min |
beginning of the lambda range (default lambda) |
Applies elastic net regularization. Elastic net is a weighted combination of ridge and LASSO penalties.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.