LassoCMA: L1 penalized logistic regression

In chbernau/CMA: Synthesis of microarray-based classification

Description

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.

Usage

LassoCMA(X, y, f, learnind, norm.fraction = 0.1,models=FALSE,...)

Arguments

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.
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.

Value

An object of class clvarseloutput.

Note

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

Author(s)

Martin Slawski [e-mail]
Anne-Laure Boulesteix [e-mail]
Christoph Bernau [e-mail]

References

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

See Also

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

Examples

### 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)

chbernau/CMA documentation built on May 17, 2019, 12:04 p.m.