mlclassCox: 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 CoxPH model.

Description

Applications of machine learning in survival analysis by prognostic classification of genes by CoxPH model.

Usage

mlclassCox(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 Times of repeated observations.
s_ID	"Column/Variable name" consisting unique identification for each subject.
per	Percentage value for ordering, default=20.
fold	Number of folds for re-sampling, default=3.
data	High dimensional data containing survival observations with multiple 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: 
data(srdata)
mlclassCox(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.