classification {mAPKL} | R Documentation

## Classify samples according to the SVM algorithm

### Description

This function performs classification through the Support Vector Machines (SVM) algorithm. The algorithm applies on the "exemplars" dataset. It produces a classification result either on the training set or on a validation set. This function estimates how well the selected "genes" from mAP-KL method discriminate between two phenotypes.The default SVM settings are: "linear" kernel and 5-folds cross-validation. Regarding the parameters for the "linear" kernel ,cost parameter, and for the "radial" kernel, cost and gamma parameters, are estimated automatically through the tune.svm function as described in e1071 r-package.

### Usage

    classification(trExemplObj,classLabels,valExemplObj=NULL,kf=5,kernel="linear")

### Arguments

`trExemplObj` | The exemplars train eSet object.
`classLabels` | The varLabels name in the eSet object where the class labels are stored e.g "type".
`valExemplObj` | The exemplars validation eSet object (if not NULL).
`kf` | The k-folds value of the cross-validation parameter.The default value is 5-folds. By setting "Loo" or "LOO" a Leave-One-Out Cross Validation is performed
`kernel` | The type of kernel used for the classification analysis.The default kernel is "linear"

### Value

`classL` | The labels of the train set
`valClassL` | The labels of the validation set if not NULL
`predLbls` | The predicted labels according to the classification analysis

### Author(s)

Argiris Sakellariou

### Examples

    library(mAPKLData)
    data(mAPKLData)
    breast <- sampling(Data=mAPKLData, valPercent=40, classLabels="type", seed=135)
    normTrainData <- preprocess(breast$trainData)
    normTestData <- preprocess(breast$testData)

    exprs(breast$trainData)<-normTrainData$clL2.normdata
    exprs(breast$testData)<-normTestData$clL2.normdata

    out.clL2 <- mAPKL(trObj=breast$trainData, classLabels="type",
    valObj=breast$testData,dataType=7)

    clasPred <- classification(trExemplObj=out.clL2@exemplTrain, classLabels="type",
    valExemplObj=out.clL2@exemplTest)

[Package mAPKL version 1.10.0 Index]