| baseModel {BioMM} | R Documentation |
Prediction using different supervised machine learning models.
baseModel(trainData, testData, classifier = c("randForest", "SVM",
"glmnet"), predMode = c("classification", "probability", "regression"),
paramlist)
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. |
The predicted output for the test data.
Junfang Chen
## 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)