scdaCMA {CMA} | R Documentation

## Shrunken Centroids Discriminant Analysis

### Description

The nearest shrunken centroid classification algorithm is detailly described in Tibshirani et al. (2002).

It is widely known under the name PAM (prediction analysis for microarrays), which can also be found in the package `pamr`.

For `S4` method information, see scdaCMA-methods.

### Usage

    scdaCMA(X, y, f, learnind, delta = 0.5, 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.
`delta` | The shrinkage intensity for the class centroids - a hyperparameter that must be tuned. The default `0.5` not necessarily produces good results.
`models` | a logical value indicating whether the model object shall be returned
`...` | Currently unused argument.

### Value

An object of class `cloutput`.

### Note

The results can differ from those obtained by using the package `pamr`.

### Author(s)

Martin Slawski ms@cs.uni-sb.de

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

### References

Tibshirani, R., Hastie, T., Narasimhan, B., and Chu, G., (2003).

Class prediction by nearest shrunken centroids with applications to DNA microarrays.

Statistical Science, 18, 104-117

### See Also

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

### Examples

    ### load Khan data
    data(khan)
    ### extract class labels
    khanY <- khan[,1]
    ### extract gene expression
    khanX <- as.matrix(khan[,-1])
    ### select learningset
    set.seed(111)
    learnind <- sample(length(khanY), size=floor(2/3*length(khanY)))
    ### run Shrunken Centroids classfier, without tuning
    scdaresult <- scdaCMA(X=khanX, y=khanY, learnind=learnind)
    ### show results
    show(scdaresult)
    ftable(scdaresult)
    plot(scdaresult)

[Package CMA version 1.48.0 Index]