prediction {CMA} | R Documentation |
This method constructs the given classifier using the specified training data, gene selection and tuning results.. Subsequently, class labels are predicted for new observations.
For S4 method information, s. classification-methods
.
prediction(X.tr,y.tr,X.new,f,classifier,genesel,models=F,nbgene,tuneres,...)
X.tr |
Training gene expression data. Can be one of the following:
|
X.new |
gene expression data. Can be one of the following:
|
y.tr |
Class labels of training observation. Can be one of the following:
WARNING: The class labels will be re-coded for classifier construction to
range from |
f |
A two-sided formula, if |
genesel |
Optional (but usually recommended) object of class
|
nbgene |
Number of best genes to be kept for classification, based
on either
|
classifier |
Name of function ending with |
tuneres |
Analogous to the argument |
models |
a logical value indicating whether the model object shall be returned |
... |
Further arguments passed to the function |
This function builds the specified classifier and predicts the class labels of new observations. Hence, its usage differs from those of most other prediction functions in R.
A object of class predoutput-class
; Predicted classes can be seen by show(predoutput)
Christoph Bernau bernau@ibe.med.uni-muenchen.de
Anne-Laure Boulesteix boulesteix@ibe.med.uni-muenchen.de
Slawski, M. Daumer, M. Boulesteix, A.-L. (2008) CMA - A comprehensive Bioconductor package for supervised classification with high dimensional data. BMC Bioinformatics 9: 439
GeneSelection
, tune
, evaluation
,
compBoostCMA
, dldaCMA
, ElasticNetCMA
,
fdaCMA
, flexdaCMA
, gbmCMA
,
knnCMA
, ldaCMA
, LassoCMA
,
nnetCMA
, pknnCMA
, plrCMA
,
pls_ldaCMA
, pls_lrCMA
, pls_rfCMA
,
pnnCMA
, qdaCMA
, rfCMA
,
scdaCMA
, shrinkldaCMA
, svmCMA
classification
### a simple k-nearest neighbour example ### datasets ## Not run: plot(x) data(golub) golubY <- golub[,1] golubX <- as.matrix(golub[,-1]) ###Splitting data into training and test set X.tr<-golubX[1:30] X.new<-golubX[31:39] y.tr<-golubY[1:30] ### 1. GeneSelection selttest <- GeneSelection(X=X.tr, y=y.tr, method = "t.test") ### 2. tuning tunek <- tune(X.tr, y.tr, genesel = selttest, nbgene = 20, classifier = knnCMA) ### 3. classification pred <- prediction(X.tr=X.tr,y.tr=y.tr,X.new=X.new, genesel = selttest, tuneres = tunek, nbgene = 20, classifier = knnCMA) ### show and analyze results: show(pred) ## End(Not run)