CMA (version 1.30.0)

plrCMA: L2 penalized logistic regression

Description

High dimensional logistic regression combined with an L2-type (Ridge-)penalty. Multiclass case is also possible. For S4 method information, see plrCMA-methods

Usage

plrCMA(X, y, f, learnind, lambda = 0.01, scale = TRUE, 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.

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.

lambda

Parameter governing the amount of penalization. This hyperparameter should be tuned.

scale

Scale the predictors as specified by X to have unit variance and zero mean.

models

a logical value indicating whether the model object shall be returned

...

Currently unused argument.

Value

cloutput.

References

Zhu, J., Hastie, T. (2004). Classification of gene microarrays by penalized logistic regression.

Biostatistics 5:427-443.

See Also

compBoostCMA, dldaCMA, ElasticNetCMA, fdaCMA, flexdaCMA, gbmCMA, knnCMA, ldaCMA, LassoCMA, nnetCMA, pknnCMA, 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 from first 10 genes
golubX <- as.matrix(golub[,-1])
### select learningset
ratio <- 2/3
set.seed(111)
learnind <- sample(length(golubY), size=floor(ratio*length(golubY)))
### run penalized logistic regression (no tuning)
plrresult <- plrCMA(X=golubX, y=golubY, learnind=learnind)
### show results
show(plrresult)
ftable(plrresult)
plot(plrresult)
### multiclass example:
### load Khan data
data(khan)
### extract class labels
khanY <- khan[,1]
### extract gene expression from first 10 genes
khanX <- as.matrix(khan[,-1])
### select learningset
set.seed(111)
learnind <- sample(length(khanY), size=floor(ratio*length(khanY)))
### run penalized logistic regression (no tuning)
plrresult <- plrCMA(X=khanX, y=khanY, learnind=learnind)
### show results
show(plrresult)
ftable(plrresult)
plot(plrresult)