pls_ldaCMA {CMA} | R Documentation

## Partial Least Squares combined with Linear Discriminant Analysis

### Description

This method constructs a classifier that extracts Partial Least Squares components that are plugged into Linear Discriminant Analysis. The Partial Least Squares components are computed by the package `plsgenomics`.

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

### Usage

    pls_ldaCMA(X, y, f, learnind, comp = 2, 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 Partial Least Squares components to extract. Default is 2 which can be suboptimal, depending on the particular dataset. Can be optimized using `tune`.
`plot` | If `comp <= 2`, should the classification space of the Partial Least Squares components be plotted ? Default is `FALSE`.
`models` | a logical value indicating whether the model object shall be returned

### Value

An object of class `cloutput`.

### Author(s)

Martin Slawski ms@cs.uni-sb.de

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

### References

Nguyen, D., Rocke, D. M., (2002).

Tumor classifcation by partial least squares using microarray gene expression data.

Bioinformatics 18, 39-50

Boulesteix, A.L., Strimmer, K. (2007).

Partial least squares: a versatile tool for the analysis of high-dimensional genomic data.

Briefings in Bioinformatics 7:32-44.

### 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

    ## Not run:
    ### 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
    plsresult <- pls_ldaCMA(X=khanX, y=khanY, learnind=learnind, comp = 4)
    ### show results
    show(plsresult)
    ftable(plsresult)
    plot(plsresult)
    ## End(Not run)

[Package CMA version 1.48.0 Index]