prediction {CMA} | R Documentation

## General method for predicting classes of new observations

### Description

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`.

### Usage

    prediction(X.tr,y.tr,X.new,f,classifier,genesel,models=F,nbgene,tuneres,...)

### Arguments

`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`.

### Details

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.

### Value

A object of class `predoutput-class`; Predicted classes can be seen by `show(predoutput)`

### Author(s)

Christoph Bernau bernau@ibe.med.uni-muenchen.de

Anne-Laure Boulesteix boulesteix@ibe.med.uni-muenchen.de

### References

Slawski, M. Daumer, M. Boulesteix, A.-L. (2008) CMA - A comprehensive Bioconductor package for supervised classification with high dimensional data. BMC Bioinformatics 9: 439

### See Also

`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`

### Examples

    ### 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)

[Package CMA version 1.48.0 Index]