fdaCMA {CMA} | R Documentation |
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.
fdaCMA(X, y, f, learnind, comp = 1, plot = FALSE)
X |
Gene expression data. Can be one of the following:
|
y |
Class labels. Can be one of the following:
|
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 . |
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 martin.slawski@campus.lmu.de
Anne-Laure Boulesteix http://www.slcmsr.net/boulesteix
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)