Description Usage Arguments Value Author(s) Examples
Prediction by random forest with different settings: 'probability', 'classification' and 'regression'.
1 2 3 4 5 6 | baseRandForest(
trainData,
testData,
predMode = c("classification", "probability", "regression"),
paramlist = list(ntree = 2000, nthreads = 20)
)
|
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. |
The predicted output for the test data.
Junfang Chen
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 |
## 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), 12)
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)
|
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.