| pls_ldaCMA {CMA} | R Documentation |
This method constructs a classifier that extracts
Partial Least Squares components that are plugged into
Linear Discriminant Analysis.
The Partial Least Squares components are computed by the package
plsgenomics.
For S4 method information, see pls_ldaCMA-methods.
pls_ldaCMA(X, y, f, learnind, comp = 2, plot = FALSE,models=FALSE)
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. |
comp |
Number of Partial Least Squares components to extract.
Default is 2 which can be suboptimal, depending on the
particular dataset. Can be optimized using tune. |
plot |
If comp <= 2, should the classification space of the
Partial Least Squares components be plotted ? Default is FALSE. |
models |
a logical value indicating whether the model object shall be returned |
An object of class cloutput.
Martin Slawski ms@cs.uni-sb.de
Anne-Laure Boulesteix boulesteix@ibe.med.uni-muenchen.de
Nguyen, D., Rocke, D. M., (2002).
Tumor classifcation by partial least squares using microarray gene expression data.
Bioinformatics 18, 39-50
Boulesteix, A.L., Strimmer, K. (2007).
Partial least squares: a versatile tool for the analysis of high-dimensional genomic data.
Briefings in Bioinformatics 7:32-44.
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 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(2/3*length(khanY))) ### run Shrunken Centroids classfier, without tuning plsresult <- pls_ldaCMA(X=khanX, y=khanY, learnind=learnind, comp = 4) ### show results show(plsresult) ftable(plsresult) plot(plsresult)