hidimLasso: hidimLasso LASSO for high dimensional data

In SurvHiDim: High Dimensional Survival Data Analysis

Description

Least Absolute Shrinkage and Selection Operator (LASSO) for High Dimensional Survival data.

Usage

hidimLasso(m, n, OS, Death, data)

Arguments

m	Starting column number from where variables of high dimensional data will get selected.
n	Ending column number till where variables of high dimensional data will get selected.
OS	Column name of survival duration event, a string value. i.e. "os"
Death	Column name of survival event, a string value. i.e "death"
data	High dimensional data having survival duration, event and various covariates observations

Details

'HiDimLasso' allows a user to apply LASSO function on the High Dimensional data and reduce the study variables to handful number of co-variate which are observed impacting the survival outcomes.

Column of Overall Survival must be named as 'OS' and the column defining the event must be named as 'Event'.

By default it stores the outcome data in user's current directory.

Value

A list of variables selected by LASSO as predictor variables.

Author(s)

Atanu Bhattacharjee and Akash Pawar

References

Bhattacharjee, A. (2020). Bayesian Approaches in Oncology Using R and OpenBUGS. CRC Press.

Congdon, P. (2014). Applied bayesian modelling (Vol. 595). John Wiley & Sons.

Banerjee, S., Vishwakarma, G. K., & Bhattacharjee, A. (2019). Classification Algorithm for High Dimensional Protein Markers in Time-course Data. arXiv preprint arXiv:1907.12853.

See Also

hidimSurvlas hidimSurvbonlas

Examples

##
data(hnscc)
hidimLasso(7,105,OS="os",Death="death",hnscc)
##

SurvHiDim documentation built on June 26, 2021, 5:06 p.m.