ldaCMA: Linear Discriminant Analysis

Description Usage Arguments Value Note Author(s) References See Also Examples

Description

Performs a linear discriminant analysis under the assumption of a multivariate normal distribution in each classes (with equal, but generally structured) covariance matrices. The function lda from the package MASS is called for computation.

For S4 method information, see ldaCMA-methods.

Usage

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ldaCMA(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 lda from the package MASS

Value

An object of class cloutput.

Note

Excessive variable selection has usually to performed before ldaCMA can be applied in the p > n setting. Not reducing the number of variables can result in an error message.

Author(s)

Martin Slawski ms@cs.uni-sb.de

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

References

McLachlan, G.J. (1992).

Discriminant Analysis and Statistical Pattern Recognition.

Wiley, New York

See Also

compBoostCMA, dldaCMA, ElasticNetCMA, fdaCMA, flexdaCMA, gbmCMA, knnCMA, LassoCMA, nnetCMA, pknnCMA, plrCMA, pls_ldaCMA, pls_lrCMA, pls_rfCMA, pnnCMA, qdaCMA, rfCMA, scdaCMA, shrinkldaCMA, svmCMA

Examples

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## Not run: 
### load Golub AML/ALL data
data(golub)
### extract class labels
golubY <- golub[,1]
### extract gene expression from first 10 genes
golubX <- as.matrix(golub[,2:11])
### select learningset
ratio <- 2/3
set.seed(111)
learnind <- sample(length(golubY), size=floor(ratio*length(golubY)))
### run LDA
ldaresult <- ldaCMA(X=golubX, y=golubY, learnind=learnind)
### show results
show(ldaresult)
ftable(ldaresult)
plot(ldaresult)
### multiclass example:
### load Khan data
data(khan)
### extract class labels
khanY <- khan[,1]
### extract gene expression from first 10 genes
khanX <- as.matrix(khan[,2:11])
### select learningset
set.seed(111)
learnind <- sample(length(khanY), size=floor(ratio*length(khanY)))
### run LDA
ldaresult <- ldaCMA(X=khanX, y=khanY, learnind=learnind)
### show results
show(ldaresult)
ftable(ldaresult)
plot(ldaresult)

## End(Not run)

chbernau/CMA documentation built on May 17, 2019, 12:04 p.m.