plrCMA {CMA} | R Documentation

## 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 `tune`d.
`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

An object of class `cloutput`.

### Author(s)

Special thanks go to

Ji Zhu (University of Ann Arbor, Michigan)

Trevor Hastie (Stanford University)

who provided the basic code that was then adapted by

Martin Slawski ms@cs.uni-sb.de

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

### 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)

[Package CMA version 1.44.0 Index]