ElasticNetCMA {CMA} | R Documentation |
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
.
ElasticNetCMA(X, y, f, learnind, norm.fraction = 0.1, alpha=0.5, models=FALSE, ...)
X |
Gene expression data. Can be one of the following:
|
y |
Class labels. Can be one of the following:
WARNING: The class labels will be re-coded to
range from |
f |
A two-sided formula, if |
learnind |
An index vector specifying the observations that
belong to the learning set. May be |
norm.fraction |
L1 Shrinkage intensity, expressed as the fraction
of the coefficient L1 norm compared to the
maximum possible L1 norm (corresponds to |
alpha |
The elasticnet mixing parameter, with 0<alpha<= 1. The penalty is defined as (1-alpha)/2||beta||_2^2+alpha||beta||_1.
|
models |
a logical value indicating whether the model object shall be returned |
... |
Further arguments passed to the function |
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)