lilikoi.prognosis: Pathway-based prognosis model

In lilikoi: Metabolomics Personalized Pathway Analysis Tool

Pathway-based prognosis model

Description

Fits a Cox proportional hazards regression model or a Cox neural network model to predict survival results.

Usage

lilikoi.prognosis(
  event,
  time,
  exprdata,
  percent = NULL,
  alpha = 1,
  nfold = 5,
  method = "median",
  cvlambda = "lambda.1se",
  python.path = NULL,
  path = NULL,
  coxnnet = FALSE,
  coxnnet_method = "gradient"
)

Arguments

event	survival event
time	survival time
exprdata	dataset for penalization, with id in the rownames and pathway or metabolites names in the column names.
percent	train-test separation percentage
alpha	denote which penalization method to use.
nfold	fold number for cross validation
method	determine the prognosis index, "quantile", "quantile" or "ratio".
cvlambda	determine the lambda for prediction, "lambda.min" or "lambda.1se".
python.path	saved path for python3
path	saved path for the L2cross_nopercent.py and L2cross.py files in lilikoi
coxnnet	if TRUE, coxnnet will be used.
coxnnet_method	the algorithm for gradient descent. Includes standard gradient descent ("gradient"), Nesterov accelerated gradient "nesterov" and momentum gradient descent ("momentum").

Value

A list of components:

c_index	C-index of the Cox-PH model
difftest	Test results of the survival curve difference test
survp	Kaplan Meier plot

Examples

# inst.path = path.package('lilikoi', quiet = FALSE) # path = "lilikoi/inst/", use R to run
# inst.path = file.path(inst.path, 'inst')
# python.path = "/Library/Frameworks/Python.framework/Versions/3.8/bin/python3"
# Prepare survival event, survival time and exprdata from your dataset.
# lilikoi.prognosis(event, time, exprdata, percent=NULL, alpha=0, nfold=5, method="median",
#   cvlambda=NULL,python.path=NULL, path=inst.path, python.path=python.path,
#   coxnnet=FALSE,coxnnet_method="gradient")

lilikoi documentation built on Oct. 6, 2022, 1:05 a.m.