prediction: General method for predicting classes of new observations
In CMA: Synthesis of microarray-based classification

Description Usage Arguments Details Value Author(s) References See Also Examples

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.

1	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: A `matrix`. Rows correspond to observations, columns to variables. A `data.frame`, when `f` is not missing (s. below). An object of class `ExpressionSet`.
`X.new`	gene expression data. Can be one of the following: A `matrix`. Rows correspond to observations, columns to variables. A `data.frame`, when `f` is not missing (s. below). An object of class `ExpressionSet`.
`y.tr`	Class labels of training observation. Can be one of the following: A `numeric` vector. A `factor`. A `character` if `X` is an `ExpressionSet` that specifies the phenotype variable. `missing`, if `X` is a `data.frame` and a proper formula `f` is provided. WARNING: The class labels will be re-coded for classifier construction to range from `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: If the gene selection method has been one of `"lasso", "elasticnet", "boosting"`, `nbgene` will be reset to `min(s, nbgene)` where `s` is the number of nonzero coefficients. if the gene selection scheme has been `"one-vs-all", "pairwise"` for the multiclass case, there exist several rankings. The top `nbgene` will be kept of each of them, so the number of effective used genes will sometimes be much larger.
`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, svmCMAclassification

### 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)