fdaCMA {CMA} | R Documentation

## Fisher's Linear Discriminant Analysis

### Description

Fisher's Linear Discriminant Analysis constructs a subspace of 'optimal projections' in which classification is performed. The directions of optimal projections are computed by the function `cancor` from the package `stats`. For an exhaustive treatment, see e.g. Ripley (1996).

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

### Usage

    fdaCMA(X, y, f, learnind, comp = 1, plot = FALSE,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.
`comp` | Number of discriminant coordinates (projections) to compute. Default is one, must be smaller than or equal to `K-1`, where `K` is the number of classes.
`plot` | Should the projections onto the space spanned by the optimal projection directions be plotted ? Default is `FALSE`.
`models` | a logical value indicating whether the model object shall be returned

### Value

An object of class `cloutput`.

### Note

Excessive variable selection has usually to performed before `fdaCMA` can be applied in the `p > n` setting. Not reducing the number of variables can result in an error message.

### Author(s)

Martin Slawski ms@cs.uni-sb.de

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

### References

Ripley, B.D. (1996)

Pattern Recognition and Neural Networks.

Cambridge University Press

### See Also

`compBoostCMA`, `dldaCMA`, `ElasticNetCMA`, `fdaCMA`, `flexdaCMA`, `gbmCMA`, `knnCMA`, `ldaCMA`, `LassoCMA`, `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 from first 10 genes
    golubX <- as.matrix(golub[,2:11])
    ### select learningset
    ratio <- 2/3
    set.seed(111)
    learnind <- sample(length(golubY), size=floor(ratio*length(golubY)))
    ### run FDA
    fdaresult <- fdaCMA(X=golubX, y=golubY, learnind=learnind, comp = 1, plot = TRUE)
    ### show results
    show(fdaresult)
    ftable(fdaresult)
    plot(fdaresult)
    ### multiclass example:
    ### load Khan data
    data(khan)
    ### extract class labels
    khanY <- khan[,1]
    ### extract gene expression from first 10 genes
    khanX <- as.matrix(khan[,2:11])
    ### select learningset
    set.seed(111)
    learnind <- sample(length(khanY), size=floor(ratio*length(khanY)))
    ### run FDA
    fdaresult <- fdaCMA(X=khanX, y=khanY, learnind=learnind, comp = 2, plot = TRUE)
    ### show results
    show(fdaresult)
    ftable(fdaresult)
    plot(fdaresult)

[Package CMA version 1.46.0 Index]