baseModel {BioMM} | R Documentation

## Base supervised machine learning models for prediction

### Description

Prediction using different supervised machine learning models.

### Usage

    baseModel(trainData, testData, classifier = c("randForest", "SVM",
      "glmnet"), predMode = c("classification", "probability", "regression"),
      paramlist)

### 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.
`classifier` | Machine learning classifiers. Available options are c('randForest', 'SVM', 'glmnet').
`predMode` | The prediction mode. Available options are c('classification', 'probability', 'regression'). 'probability' is currently only for 'randForest'.
`paramlist` | A set of model parameters defined in an R list object. See more details for each individual model.

### 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]
    ## 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(ranger)
    set.seed(123)
    predY <- baseModel(trainData, testData,
                       classifier='randForest',
                       predMode='classification',
                       paramlist=list(ntree=300, nthreads=20))
    print(table(predY))
    testY <- testData[,1]
    accuracy <- classifiACC(dataY=testY, predY=predY)
    print(accuracy)

[Package BioMM version 1.3.0 Index]