CMA (version 1.30.0)

pls_lrCMA: 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,models=FALSE)

Arguments

X

Gene expression data. Can be one of the following:

• Amatrix. Rows correspond to observations, columns to variables.
• Adata.frame, whenfisnotmissing (s. below).
• An object of classExpressionSet.

y

Class labels. Can be one of the following:

• Anumericvector.
• Afactor.
• AcharacterifXis anExpressionSetthat specifies the phenotype variable.
• missing, ifXis adata.frameand a proper formulafis 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<="" 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

• An object of class cloutput.

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)