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:
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. |
genesel |
Optional (but usually recommended) object of class
genesel containing variable importance
information for the argument learningsets .
In this case the object contains a single variable selection. Appropriate
genesel -objects can be obtained using the function genesel
without learningset and setting X=X.tr and y=y.tr (i.e. corresponding to the training data of this function).
|
nbgene |
Number of best genes to be kept for classification, based
on either genesel or the call to GeneSelection
using genesellist . In the case that both are missing,
this argument is not necessary.
note:
|
classifier |
Name of function ending with CMA indicating
the classifier to be used. |
tuneres |
Analogous to the argument genesel - object of
class tuningresult containing information
about the best hyperparameter choice for the argument
learningsets . Appropriate tuning-objects can be obtained using the function
tune without learningsets and setting parameters X=X.tr , y=y.tr and genesel=genesel
(i.e. using the same training data and gene selection as in this function) |
models |
a logical value indicating whether the model object shall be returned |
... |
Further arguments passed to the function classifier . |
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)