baseRandForest {BioMM} | R Documentation

## Prediction by random forest

### Description

Prediction by random forest with different settings: 'probability', 'classification' and 'regression'.

### Usage

    baseRandForest(trainData, testData, predMode = c("classification",
      "probability", "regression"), paramlist = list(ntree = 2000, nthreads =
      20))

### 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('probability', 'classification', 'regression').
`paramlist` | A set of model parameters defined in an R list object. The valid option: list(ntree, nthreads). 'ntree' is the number of trees used. The defaul is 2000. 'nthreads' is the number of threads used for computation. The default is 20.

### Value

The predicted output for the test data.

### Author(s)

Junfang Chen

### Examples

    ## Load data
    methylfile <- system.file('extdata', 'methylData.rds', package='BioMM')
    methylData <- readRDS(methylfile)
    dataY <- methylData[,1]
    ## test 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(ranger)
    predY <- baseRandForest(trainData, testData,
                            predMode='classification',
                            paramlist=list(ntree=300, nthreads=20))
    testY <- testData[,1]
    accuracy <- classifiACC(dataY=testY, predY=predY)
    print(accuracy)

[Package BioMM version 1.4.0 Index]