LassoCMA {CMA} | R Documentation |
The Lasso (Tibshirani, 1996) is one of the most popular
tools for simultaneous shrinkage and variable selection. Recently,
Friedman, Hastie and Tibshirani (2008) have developped and algorithm to
compute the entire solution path of the Lasso for an arbitrary
generalized linear model, implemented in the package glmnet
.
The method can be used for variable selection alone, s. GeneSelection
.
For S4
method information, see LassoCMA-methods
.
LassoCMA(X, y, f, learnind, norm.fraction = 0.1,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 |
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. ElasticNetCMA
.
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
Tibshirani, R. (1996)
Regression shrinkage and selection via the lasso.
Journal of the Royal Statistical Society B, 58(1), 267-288
Friedman, J., Hastie, T. and Tibshirani, R. (2008) Regularization
Paths for Generalized Linear Models via Coordinate Descent
http://www-stat.stanford.edu/~hastie/Papers/glmnet.pdf
compBoostCMA
, dldaCMA
, ElasticNetCMA
,
fdaCMA
, flexdaCMA
, gbmCMA
,
knnCMA
, ldaCMA
,
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 L1 penalized logistic regression (no tuning) lassoresult <- LassoCMA(X=golubX, y=golubY, learnind=learnind, norm.fraction = 0.2) show(lassoresult) ftable(lassoresult) plot(lassoresult)