lilikoi.prognosis {lilikoi}

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")