ga_mtlr_fun: mtlr model using GA as feature selection

In SydneyBioX/SurvBenchmark_package: survival approaches benchmarking

mtlr model using GA as feature selection

Description

mtlr model using GA as feature selection

Usage

ga_mtlr_fun(r, data, cvK, numm, topnumm, generation_num, timess)

Arguments

r	a numeric value, a seed to run this method
data	a dataframe, the data used to performance this survival model
cvK	a numeric value, cross-validation fold
numm	a numeric value, the number of variables,i.e.for example, number of proteins in the data
topnumm	a numeric value, the number of variables selected to be passed into the model, for example, the number of DE genes
generation_num	a numeric value, the generation number used in the GA algorithm, details see package "GenAlgo"
timess	a numeric vector of length 15, contains time points to get the time-dependent AUC values

Value

a data.frame with allevaluation measurements in all columns and rows are each fold results from cross-validation

Examples

data("exampledt", package = "SurvBenchmark")
fitform_ogl=survival::Surv(time,status)~.
formula1=fitform_ogl
formula2=fitform_ogl
formula3=survival::Surv(time,status)~1
formula4=survival::Surv(time,status)~1
form1=as.formula(~.)
timess=seq(as.numeric(summary(cancerdt2_1$time)[2]),as.numeric(summary(cancerdt2_1$time)[5]),(as.numeric(summary(cancerdt2_1$time)[5])-as.numeric(summary(cancerdt2_1$time)[2]))/14)
want=ga_mtlr_fun(1,cancerdt2_1,5, 16047,5,20,timess);

SydneyBioX/SurvBenchmark_package documentation built on June 4, 2022, 12:01 p.m.