CMA (version 1.30.0)

pls_ldaCMA: 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<="" code="">, 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

cloutput.

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

### 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)