fdaCMA: Fisher's Linear Discriminant Analysis
In chbernau/CMA: Synthesis of microarray-based classification

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

Fisher's Linear Discriminant Analysis constructs a subspace of 'optimal projections' in which classification is performed. The directions of optimal projections are computed by the function cancor from the package stats. For an exhaustive treatment, see e.g. Ripley (1996).

For S4 method information, see fdaCMA-methods.

1	fdaCMA(X, y, f, learnind, comp = 1, plot = FALSE,models=FALSE)

`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 discriminant coordinates (projections) to compute. Default is one, must be smaller than or equal to `K-1`, where `K` is the number of classes.
`plot`	Should the projections onto the space spanned by the optimal projection directions be plotted ? Default is `FALSE`.
`models`	a logical value indicating whether the model object shall be returned

An object of class cloutput.

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

Martin Slawski ms@cs.uni-sb.de

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

Ripley, B.D. (1996)

Pattern Recognition and Neural Networks.

Cambridge University Press

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

### 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 FDA
fdaresult <- fdaCMA(X=golubX, y=golubY, learnind=learnind, comp = 1, plot = TRUE)
### show results
show(fdaresult)
ftable(fdaresult)
plot(fdaresult)
### 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 FDA
fdaresult <- fdaCMA(X=khanX, y=khanY, learnind=learnind, comp = 2, plot = TRUE)
### show results
show(fdaresult)
ftable(fdaresult)
plot(fdaresult)