dldaCMA {CMA} | R Documentation

## Diagonal Discriminant Analysis

### Description

Performs a diagonal discriminant analysis under the assumption of a multivariate normal distribution in each classes (with equal, diagonally structured) covariance matrices. The method is also known under the name 'naive Bayes' classifier.

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

### Usage

    dldaCMA(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
`...` | Currently unused argument.

### Value

An object of class `cloutput`.

### Note

As opposed to linear or quadratic discriminant analysis, variable selection is not strictly necessary.

### Author(s)

Martin Slawski ms@cs.uni-sb.de

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

### References

McLachlan, G.J. (1992).

Discriminant Analysis and Statistical Pattern Recognition.

Wiley, New York

### See Also

`compBoostCMA`, `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
    golubX <- as.matrix(golub[,-1])
    ### select learningset
    ratio <- 2/3
    set.seed(111)
    learnind <- sample(length(golubY), size=floor(ratio*length(golubY)))
    ### run DLDA
    dldaresult <- dldaCMA(X=golubX, y=golubY, learnind=learnind)
    ### show results
    show(dldaresult)
    ftable(dldaresult)
    plot(dldaresult)
    ### multiclass example:
    ### 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(ratio*length(khanY)))
    ### run LDA
    ldaresult <- dldaCMA(X=khanX, y=khanY, learnind=learnind)
    ### show results
    show(dldaresult)
    ftable(dldaresult)
    plot(dldaresult)

[Package CMA version 1.48.0 Index]