Function to Classify Microarray Data using Penalized Discriminant Methods
Description
This function is used to classify microarray data. Since the underlying model fit is based on penalized discriminant methods, there is no need for a prefiltering step to reduce the number of genes.
Usage
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Arguments
formula 
A symbolic description of the model to be fit. Details given below. 
method 
One of "pls", "pcr", "ridge", corresponding to partial least squares, principal components regression and ridge regression. 
keep.fitted 
Boolean. Should the fitted values be kept? Default is TRUE, as this is necessary for the plotting and predict functions. 
... 
Additional parameters to pass to 
Details
The formula interface is identical to all other formula calls in R, namely Y ~ X, where Y is a numeric vector of class assignments and X is a matrix or data.frame containing the gene expression values. Note that unlike most microarray analyses, in this instance the columns of X are genes and rows are samples, so most calls will require something similar to Y ~ t(X).
Value
an object of class "fda"
. Use predict
to extract
discriminant variables, posterior probabilities or predicted class
memberships. Other extractor functions are coef
,
and plot
.
The object has the following components:
percent.explained 
the percent betweengroup variance explained by each dimension (relative to the total explained.) 
values 
optimal scaling regresssion sumofsquares for each
dimension (see reference). The usual discriminant analysis
eigenvalues are given by 
means 
class means in the discriminant space. These are also
scaled versions of the final theta's or class scores, and can be
used in a subsequent call to 
theta.mod 
(internal) a class scoring matrix which allows

dimension 
dimension of discriminant space. 
prior 
class proportions for the training data. 
fit 
fit object returned by 
call 
the call that created this object (allowing it to be

confusion 
A 'confusion' matrix that shows how well the classifier works using the training data. 
Author(s)
James W. MacDonald and Debashis Ghosh, based on fda
in
the mda
package of Trevor Hastie and Robert Tibshirani, which
was ported to R by Kurt Hornik, Brian D. Ripley, and Friedrich Leisch.
References
http://www.sph.umich.edu/~ghoshd/COMPBIO/POPTSCORE
"Flexible Disriminant Analysis by Optimal Scoring" by Hastie, Tibshirani and Buja, 1994, JASA, 12551270.
"Penalized Discriminant Analysis" by Hastie, Buja and Tibshirani, Annals of Statistics, 1995 (in press).
Examples
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