ElasticNetCMA-methods: Classfication and variable selection by the ElasticNet
Description
Zou and Hastie (2004) proposed a combined L1/L2 penalty
for regularization and variable selection.
The Elastic Net penalty encourages a grouping
effect, where strongly correlated predictors tend to be in or out of the model together.
The computation is done with the function glmpath from the package
of the same name.
Methods
- X = "matrix", y = "numeric", f = "missing"
- signature 1
- X = "matrix", y = "factor", f = "missing"
- signature 2
- X = "data.frame", y = "missing", f = "formula"
- signature 3
- X = "ExpressionSet", y = "character", f = "missing"
- signature 4
For references, further argument and output information, consult
ElasticNetCMA