pls_lrCMA {CMA} | R Documentation

## Partial Least Squares followed by logistic regression

### Description

This method constructs a classifier that extracts Partial Least Squares components that form the the covariates in a binary logistic regression model. The Partial Least Squares components are computed by the package `plsgenomics`.

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

### Usage

    pls_lrCMA(X, y, f, learnind, comp = 2, lambda = 1e-4, plot = 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`.
`lambda` | Parameter controlling the amount of L2 penalization for logistic regression, usually taken to be a small value in order to stabilize estimation in the case of separable data.
`plot` | If `comp <= 2`, should the classification space of the Partial Least Squares components be plotted ? Default is `FALSE`.

### Value

An object of class `cloutput`.

### Note

Up to now, only the two-class case is supported.

### Author(s)

Martin Slawski martin.slawski@campus.lmu.de

Anne-Laure Boulesteix http://www.slcmsr.net/boulesteix

### References

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_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 PLS, combined with logistic regression
    result <- pls_lrCMA(X=golubX, y=golubY, learnind=learnind)
    ### show results
    show(result)
    ftable(result)
    plot(result)

[Package CMA version 1.2.0 Index]