shrinkldaCMA {CMA} | R Documentation

## Shrinkage linear discriminant analysis

### Description

Linear Discriminant Analysis combined with the James-Stein-Shrinkage approach of Schaefer and Strimmer (2005) for the covariance matrix.

Currently still an experimental version.

For `S4` method information, see shrinkldaCMA-methods

### Usage

    shrinkldaCMA(X, y, f, learnind, 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.
`models` | a logical value indicating whether the model object shall be returned
`...` | Further arguments to be passed to `cov.shrink` from the package `corpcor`

### Value

An object of class `cloutput`.

### Note

This is still an experimental version.

Covariance shrinkage is performed by calling functions from the package `corpcor`.

Variable selection is not necessary.

### Author(s)

Martin Slawski ms@cs.uni-sb.de

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

### References

Schaefer, J., Strimmer, K. (2005).

A shrinkage approach to large-scale covariance estimation and implications for functional genomics.

Statististical Applications in Genetics and Molecular Biology, 4:32.

### See Also

`compBoostCMA`, `dldaCMA`, `ElasticNetCMA`, `fdaCMA`, `flexdaCMA`, `gbmCMA`, `knnCMA`, `ldaCMA`, `LassoCMA`, `nnetCMA`, `pknnCMA`, `plrCMA`, `pls_ldaCMA`, `pls_lrCMA`, `pls_rfCMA`, `pnnCMA`, `qdaCMA`, `rfCMA`, `scdaCMA`, `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  shrinkage-LDA
    result <- shrinkldaCMA(X=golubX, y=golubY, learnind=learnind)
    ### show results
    show(result)
    ftable(result)
    plot(result)

[Package CMA version 1.46.0 Index]