dldaCMA {CMA} | R Documentation |
Performs a diagonal discriminant analysis under the assumption of a multivariate normal distribution in each classes (with equal, diagonally structured) covariance matrices. The method is also known under the name 'naive Bayes' classifier.
For S4
method information, see dldaCMA-methods.
dldaCMA(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. |
... |
Currently unused argument. |
An object of class cloutput
.
As opposed to linear or quadratic discriminant analysis, variable selection is not strictly necessary.
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
, 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 golubX <- as.matrix(golub[,-1]) ### select learningset ratio <- 2/3 set.seed(111) learnind <- sample(length(golubY), size=floor(ratio*length(golubY))) ### run DLDA dldaresult <- dldaCMA(X=golubX, y=golubY, learnind=learnind) ### show results show(dldaresult) ftable(dldaresult) plot(dldaresult) ### multiclass example: ### load Khan data data(khan) ### extract class labels khanY <- khan[,1] ### extract gene expression khanX <- as.matrix(khan[,-1]) ### select learningset set.seed(111) learnind <- sample(length(khanY), size=floor(ratio*length(khanY))) ### run LDA ldaresult <- dldaCMA(X=khanX, y=khanY, learnind=learnind) ### show results show(dldaresult) ftable(dldaresult) plot(dldaresult)