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)
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 . |
An object of class cloutput
.
Martin Slawski martin.slawski@campus.lmu.de
Anne-Laure Boulesteix http://www.slcmsr.net/boulesteix
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)