baseSVM {BioMM} | R Documentation

## Prediction by SVM

### Description

Prediction by support vector machine (SVM) with two different settings: 'classification' and 'regression'.

### Usage

    baseSVM(trainData, testData, predMode = c("classification",
      "probability", "regression"), paramlist = list(tuneP = TRUE, kernel =
      "radial", gamma = 10^(-3:-1), cost = 10^(-2:2)))

### Arguments

`trainData` | The input training dataset. The first column is the label or the output. For binary classes, 0 and 1 are used to indicate the class member.
`testData` | The input test dataset. The first column is the label or the output. For binary classes, 0 and 1 are used to indicate the class member.
`predMode` | The prediction mode. Available options are c('classification', 'probability', 'regression').
`paramlist` | A set of model parameters defined in an R list object. The valid option: list(kernel, gamma, cost, tuneP). 'tuneP': a logical value indicating if hyperparameter tuning should be conducted or not. The default is FALSE. 'kernel': options are c('linear', 'polynomial', 'radial', 'sigmoid'). The defaut is 'radial'. 'gamma': the parameter needed for all kernels except 'linear'. If tuneP is TRUE, more than one value is suggested. 'cost': is the cost of constraints violation. If tuneP is TRUE, more than one value is suggested.

### Details

Hyperparameter tuning is recommended in many biological data mining applications. The best parameters can be determined via an internal cross validation.

### Value

The predicted output for the test data.

### Author(s)

Junfang Chen

### See Also

`svm`

### Examples

    ## Load data
    methylfile <- system.file('extdata', 'methylData.rds', package='BioMM')
    methylData <- readRDS(methylfile)
    dataY <- methylData[,1]
    ## select a subset of genome-wide methylation data at random
    methylSub <- data.frame(label=dataY, methylData[,c(2:2001)])
    trainIndex <- sample(nrow(methylSub), 30)
    trainData = methylSub[trainIndex,]
    testData = methylSub[-trainIndex,]
    library(e1071)
    predY <- baseSVM(trainData, testData,
                     predMode='classification',
                     paramlist=list(tuneP=FALSE, kernel='radial',
                                    gamma=10^(-3:-1), cost=10^(-3:1)))
    testY <- testData[,1]
    accuracy <- classifiACC(dataY=testY, predY=predY)
    print(accuracy)

[Package BioMM version 1.3.0 Index]