ElasticNetCMA: Classfication and variable selection by the ElasticNet
In chbernau/CMA: Synthesis of microarray-based classification

Description Usage Arguments Value Note Author(s) References See Also Examples

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.
The method can be used for variable selection alone, s. GeneSelection.
For S4 method information, see ElasticNetCMA-methods.

1	ElasticNetCMA(X, y, f, learnind, norm.fraction = 0.1, alpha=0.5, models=FALSE, ...)

`X`	Gene expression data. Can be one of the following: A `matrix`. Rows correspond to observations, columns to variables. A `data.frame`, when `f` is not missing (s. below). An object of class `ExpressionSet`. note: by default, the predictors are scaled to have unit variance and zero mean. Can be changed by passing `standardize = FALSE` via the `...` argument.
`y`	Class labels. Can be one of the following: A `numeric` vector. A `factor`. A `character` if `X` is an `ExpressionSet` that specifies the phenotype variable. `missing`, if `X` is a `data.frame` and a proper formula `f` is provided. WARNING: The class labels will be re-coded to range from `0` to `K-1`, where `K` is the total number of different classes in the learning set.
`f`	A two-sided formula, if `X` is a `data.frame`. The left part correspond to class labels, the right to variables.
`learnind`	An index vector specifying the observations that belong to the learning set. May be `missing`; in that case, the learning set consists of all observations and predictions are made on the learning set.
`norm.fraction`	L1 Shrinkage intensity, expressed as the fraction of the coefficient L1 norm compared to the maximum possible L1 norm (corresponds to `fraction = 1`). Lower values correspond to higher shrinkage. Note that the default (0.1) need not produce good results, i.e. tuning of this parameter is recommended.
`alpha`	The elasticnet mixing parameter, with 0<alpha<= 1. The penalty is defined as (1-alpha)/2\|\|beta\|\|_2^2+alpha\|\|beta\|\|_1. `alpha=1` is the lasso penalty; Currently 'alpha<0.01' not reliable, unless you supply your own `lambda` sequence
`models`	a logical value indicating whether the model object shall be returned
`...`	Further arguments passed to the function `glmpath` from the package of the same name.

An object of class clvarseloutput.

For a strongly related method, s. LassoCMA.
Up to now, this method can only be applied to binary classification.

Martin Slawski ms@cs.uni-sb.de

Anne-Laure Boulesteix boulesteix@ibe.med.uni-muenchen.de

Christoph Bernau bernau@ibe.med.uni-muenchen.de

Zhou, H., Hastie, T. (2004).
Regularization and variable selection via the elastic net. Journal of the Royal Statistical Society B, 67(2),301-320

Young-Park, M., Hastie, T. (2007)
L1-regularization path algorithm for generalized linear models.
Journal of the Royal Statistical Society B, 69(4), 659-677

compBoostCMA, dldaCMA, fdaCMA, flexdaCMA, gbmCMA, knnCMA, ldaCMA, LassoCMA, nnetCMA, pknnCMA, plrCMA, pls_ldaCMA, pls_lrCMA, pls_rfCMA, pnnCMA, qdaCMA, rfCMA, scdaCMA, shrinkldaCMA, svmCMA

### load Golub AML/ALL data
data(golub)
### extract class labels
golubY <- golub[,1]
### extract gene expression
golubX <- as.matrix(golub[,-1])
### select learningset
ratio <- 2/3
set.seed(111)
learnind <- sample(length(golubY), size=floor(ratio*length(golubY)))
### run ElasticNet - penalized logistic regression (no tuning)
result <- ElasticNetCMA(X=golubX, y=golubY, learnind=learnind, norm.fraction = 0.2, alpha=0.5)
show(result)
ftable(result)
plot(result)