CMA (version 1.30.0)

shrinkldaCMA: Shrinkage linear discriminant analysis

Description

Linear Discriminant Analysis combined with the James-Stein-Shrinkage approach of Schaefer and Strimmer (2005) for the covariance matrix.

Currently still an experimental version.

For S4 method information, see shrinkldaCMA-methods

Usage

shrinkldaCMA(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

...

Further arguments to be passed to cov.shrink from the package corpcor

Value

cloutput.

References

Schaefer, J., Strimmer, K. (2005).

A shrinkage approach to large-scale covariance estimation and implications for functional genomics.

Statististical Applications in Genetics and Molecular Biology, 4:32.

See Also

compBoostCMA, dldaCMA, ElasticNetCMA, fdaCMA, flexdaCMA, gbmCMA, knnCMA, ldaCMA, LassoCMA, nnetCMA, pknnCMA, plrCMA, pls_ldaCMA, pls_lrCMA, pls_rfCMA, pnnCMA, qdaCMA, rfCMA, scdaCMA, 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  shrinkage-LDA
result <- shrinkldaCMA(X=golubX, y=golubY, learnind=learnind)
### show results
show(result)
ftable(result)
plot(result)