mlhighKap | R Documentation |
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.
mlhighKap(cols, idSurv, idEvent, per = 20, fold = 3, data)
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 |
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.
A dataframe containing desired number of features based on minimum log Loss function
Atanu Bhattacharjee, Gajendra K. Vishwakarma & Souvik Banerjee
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
mlhighCox
## Not run: data(hnscc) mlhighKap(cols=c(6:15), idSurv="OS", idEvent="Death", per=20, fold = 3, data=hnscc) ## End(Not run)
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.