predByFS {BioMM} | R Documentation

## Prediction by supervised machine learning along with feature selection

### Description

Prediction by supervised machine learning along with feature selection.

### Usage

    predByFS(trainData, testData, FSmethod, cutP, fdr,
      FScore = MulticoreParam(), classifier, predMode, 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.
`FSmethod` | Feature selection methods. Available options are c(NULL, 'positive', 'wilcox.test', 'cor.test', 'chisq.test', 'posWilcox', or 'top10pCor').
`cutP` | The cutoff used for p value thresholding. Commonly used cutoffs are c(0.5, 0.1, 0.05, etc.). The default is 0.05.
`fdr` | Multiple testing correction method. Available options are c(NULL, 'fdr', 'BH', 'holm', etc.). See also `p.adjust`. The default is NULL.
`FScore` | The number of cores used for feature selection.
`classifier` | Machine learning classifiers. Available options are c('randForest', 'SVM', 'glmnet').
`predMode` | The prediction mode. Available options are c('probability', 'classification', 'regression').
`paramlist` | A set of model parameters defined in an R list object.

### Details

If no feature selected or just one selected feature, then top 10

### Value

The predicted output for the test data.

### Author(s)

Junfang Chen

### See Also

`getDataByFilter`

### 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(2:501)])
    trainIndex <- sample(nrow(methylSub), 30)
    trainData = methylSub[trainIndex,]
    testData = methylSub[-trainIndex,]
    library(ranger)
    library(BiocParallel)
    param <- MulticoreParam(workers = 10)
    predY <- predByFS(trainData, testData,
                      FSmethod='cor.test', cutP=0.1,
                      fdr=NULL, FScore=param,
                      classifier='randForest',
                      predMode='classification',
                      paramlist=list(ntree=300, nthreads=20))
    testY <- testData[,1]
    accuracy <- classifiACC(dataY=testY, predY=predY)
    print(accuracy)

[Package BioMM version 1.3.0 Index]