lilikoi (version 2.1.1)

lilikoi.machine_learning: 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
)

Value

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

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

Examples

# \donttest{
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)
# }