mlhighKap: mlhighKap

In highMLR: Feature Selection for High Dimensional Survival Data

mlhighKap

Description

This function extracts desired number of features based on minimum log-Loss function using Kaplan Meier model as learner method on a high dimensional survival data.

Usage

mlhighKap(cols, idSurv, idEvent, per = 20, fold = 3, data)

Arguments

cols	A numeric vector of column numbers indicating the features for which the log Loss functions are to be computed
idSurv	The name of the survival time variable
idEvent	The name of the survival event variable
per	Percentage of features to be selected, default value 20
fold	An integer denoting number of folds in cross validation, default value 3
data	A data frame that contains the survival and covariate information for the subjects

Details

Performs feature selection using Kaplan Meier method

Using the Kaplan Meier method on the given survival data, this function selects the most significant feature based on a performance measure. The performance measure is considered as logarithmic loss function. It is defined as,

L(f,t)=-log(f(t))

. The features with minimum log-loss function are extracted.

Value

A dataframe containing desired number of features based on minimum log Loss function

Author(s)

Atanu Bhattacharjee, Gajendra K. Vishwakarma & Souvik Banerjee

References

Sonabend, R., Kiraly, F. J., Bender, A., Bernd Bischl B. and Lang M. mlr3proba: An R Package for Machine Learning in Survival Analysis, 2021, Bioinformatics

See Also

mlhighCox

Examples

## Not run: 
data(hnscc)
mlhighKap(cols=c(6:15), idSurv="OS", idEvent="Death", per=20, fold = 3, data=hnscc)

## End(Not run)

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