lilikoi.machine_learning: A machine learning Function

In lilikoi: Metabolomics Personalized Pathway Analysis Tool

A machine learning Function

Description

This function for classification using 8 different machine learning algorithms and it plots the ROC curves and the AUC, SEN, and specificty

Usage

lilikoi.machine_learning(
  MLmatrix = PDSmatrix,
  measurementLabels = Label,
  significantPathways = selected_Pathways_Weka,
  trainportion = 0.8,
  cvnum = 10,
  dlround = 50,
  nrun = 10,
  Rpart = TRUE,
  LDA = TRUE,
  SVM = TRUE,
  RF = TRUE,
  GBM = TRUE,
  PAM = TRUE,
  LOG = TRUE,
  DL = TRUE
)

Arguments

MLmatrix	selected pathway deregulation score or metabolites expression matrix
measurementLabels	measurement label for samples
significantPathways	selected pathway names
trainportion	train percentage of the total sample size
cvnum	number of folds
dlround	epoch number for the deep learning method
nrun	denotes the total number of runs of each method to get their averaged performance metrics
Rpart	TRUE if run Rpart method
LDA	TRUE if run LDA method
SVM	TRUE if run SVM method
RF	TRUE if run random forest method
GBM	TRUE if run GBM method
PAM	TRUE if run PAM method
LOG	TRUE if run LOG method
DL	TRUE if run deep learning method

Value

Evaluation results and plots of all 8 machine learning algorithms, along with variable importance plots.

Examples

dt = lilikoi.Loaddata(file=system.file("extdata","plasma_breast_cancer.csv", package = "lilikoi"))
Metadata <- dt$Metadata
# lilikoi.machine_learning(MLmatrix = Metadata, measurementLabels = Metadata$Label,
# significantPathways = 0,
# trainportion = 0.8, cvnum = 10, dlround=50,Rpart=TRUE,
# LDA=FALSE,SVM=FALSE,RF=FALSE,GBM=FALSE,PAM=FALSE,LOG=FALSE,DL=FALSE)

lilikoi documentation built on March 18, 2022, 6:06 p.m.