predByCV: Cross validation prediction by supervised machine learning...

In transbioZI/BioMM: BioMM: Biological-informed Multi-stage Machine learning framework for phenotype prediction using omics data

Cross validation prediction by supervised machine learning and feature selection

Description

Prediction by supervised machine learning models using cross validation along with feature selection methods.

Usage

predByCV(
  data,
  repeats,
  nfolds,
  FSmethod,
  cutP,
  fdr,
  FScore = MulticoreParam(),
  classifier,
  predMode,
  paramlist,
  innerCore = MulticoreParam()
)

Arguments

data	The input dataset. The first column is the label or the output. For binary classes, 0 and 1 are used to indicate the class member.
repeats	The number of repeats used for cross validation. Repeated cross validation is performed if N >=2.
nfolds	The number of folds is defined for cross validation.
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, 0.01, etc). The default is 0.05. If FSmethod = "posTopCor", cutP is defined as the number of most correlated features with 'fdr' = NULL.
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 if parallel computing needed.
classifier	Machine learning classifiers.
predMode	The prediction mode. Available options are c('probability', 'classification', 'regression').
paramlist	A set of model parameters defined in an R list object.
innerCore	The number of cores used for computation.

Value

The predicted cross validation output.

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)])  
library(ranger) 
library(BiocParallel)
param1 <- MulticoreParam(workers = 1)
param2 <- MulticoreParam(workers = 20)
predY <- predByCV(methylSub, repeats=1, nfolds=10,   
                  FSmethod=NULL, cutP=0.1, 
                  fdr=NULL, FScore=param1, 
                  classifier='randForest',
                  predMode='classification', 
                  paramlist=list(ntree=300, nthreads=1),
                  innerCore=param2)
dataY <- methylData[,1]
accuracy <- classifiACC(dataY=dataY, predY=predY)
print(accuracy)  

transbioZI/BioMM documentation built on Jan. 12, 2023, 2:18 p.m.