shrinkldaCMA {CMA} | R Documentation |
Linear Discriminant Analysis combined with the James-Stein-Shrinkage approach of Schaefer and Strimmer (2005) for the covariance matrix.
Currently still an experimental version.
For S4
method information, see shrinkldaCMA-methods
shrinkldaCMA(X, y, f, learnind, models=FALSE, ...)
X |
Gene expression data. Can be one of the following:
|
y |
Class labels. Can be one of the following:
WARNING: The class labels will be re-coded to
range from |
f |
A two-sided formula, if |
learnind |
An index vector specifying the observations that
belong to the learning set. May be |
models |
a logical value indicating whether the model object shall be returned |
... |
Further arguments to be passed to |
An object of class cloutput
.
This is still an experimental version.
Covariance shrinkage is performed by calling functions
from the package corpcor
.
Variable selection is not necessary.
Martin Slawski ms@cs.uni-sb.de
Anne-Laure Boulesteix boulesteix@ibe.med.uni-muenchen.de
Schaefer, J., Strimmer, K. (2005).
A shrinkage approach to large-scale covariance estimation and implications for functional genomics.
Statististical Applications in Genetics and Molecular Biology, 4:32.
compBoostCMA
, dldaCMA
, ElasticNetCMA
,
fdaCMA
, flexdaCMA
, gbmCMA
,
knnCMA
, ldaCMA
, LassoCMA
,
nnetCMA
, pknnCMA
, plrCMA
,
pls_ldaCMA
, pls_lrCMA
, pls_rfCMA
,
pnnCMA
, qdaCMA
, rfCMA
,
scdaCMA
, 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 shrinkage-LDA result <- shrinkldaCMA(X=golubX, y=golubY, learnind=learnind) ### show results show(result) ftable(result) plot(result)