mlclassKap: Applications of machine learning in survival analysis by...

In highMLR: Feature Selection for High Dimensional Survival Data

Applications of machine learning in survival analysis by prognostic classification of genes by Kaplan-Meier estimator.

Description

Applications of machine learning in survival analysis by prognostic classification of genes by Kaplan-Meier estimator.

Usage

mlclassKap(m, n, idSurv, idEvent, Time, s_ID, per = 20, fold = 3, data)

Arguments

m	Starting column number from where high dimensional variates to be selected.
n	Ending column number till where high dimensional variates to be selected.
idSurv	"Column/Variable name" consisting duration of survival.
idEvent	"Column/Variable name" consisting survival event.
Time	"Column/Variable name" consisting timepoints of repeated observations.
s_ID	"Column/Variable name" consisting unique identification for each subject.
per	Percentage value for ordering, default=20.
fold	Number of fold for resampling, default=3.
data	High dimensional data containing survival observations and high dimensional covariates.

Value

A list of genes as per their classifications

GeneClassification

List of genes classified using Cox proportional hazard model

GeneClassification$Positive_Gene

Sublist of genes classified as positive genes

GeneClassification$Negative_Gene

Sublist of genes classified as negative genes

GeneClassification$Volatile_Gene

Sublist of genes classified as volatile genes

Result

A dataframe consisting threshold values with corresponding coefficients and p-values.

Examples

 ## Not run: 
##
mlclassKap(m=50,n=59,idSurv="OS",idEvent="event",Time="Visit",s_ID="ID",per=20,fold=3,data=srdata)
##

## End(Not run)

highMLR documentation built on July 18, 2022, 9:06 a.m.