ldaCMA {CMA} | R Documentation |
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.
ldaCMA(X, y, f, learnind, ...)
X |
Gene expression data. Can be one of the following:
|
y |
Class labels. Can be one of the following:
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. |
... |
Further arguments to be passed to lda from the
package MASS |
An object of class cloutput
.
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.
Martin Slawski martin.slawski@campus.lmu.de
Anne-Laure Boulesteix http://www.slcmsr.net/boulesteix
McLachlan, G.J. (1992).
Discriminant Analysis and Statistical Pattern Recognition.
Wiley, New York
compBoostCMA
, dldaCMA
, ElasticNetCMA
,
fdaCMA
, flexdaCMA
, gbmCMA
,
knnCMA
, 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 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)