rglaft: Estimates of selected univariate covariates(using...

In afthd: Accelerated Failure Time for High Dimensional Data with MCMC

Description

Provides Estimates of selected variable in parametric AFT model with smooth time functions for univariate in high dimensional gene expression data without MCMC.Incorporated variable selection has been done with regularization technique. It also deals covariates with missing values.

@details Survival time T for covariate x, is modelled as AFT model using

S(T|x)=S_0(T\exp(-η(x;β)))

and baseline survival function is modelled as

S_0(T)=\exp(-\exp(η_0(log(T);β_0)))

Where η and η are linear predictor.

Usage

rglaft(m, n, STime, Event, alpha, data)

Arguments

m	Starting column number of covariates of study from high dimensional entered data.
n	Ending column number of covariates of study from high dimensional entered data.
STime	name of survival time in data.
Event	name of event in data. 0 is for censored and 1 for occurrence of event.
alpha	It is chosen value between 0 and 1 to know the regularization method. alpha=1 for Lasso, alpha=0 for Ridge and alpha between 0 and 1 for elastic net regularization.
data	High dimensional gene expression data that contains event status, survival time and and set of covariates.

Value

Matrix that contains survival information of selected covariates(selected from chosen columns using regularization) on AFT model. Uppermost covariates are more significant than lowerone, as covariates are ordered as their increasing order of p value.

Author(s)

Atanu Bhattacharjee, Gajendra Kumar Vishwakarma and Pragya Kumari

See Also

pvaft, rglwbysu, rglwbysm

Examples

##
data(hdata)
set.seed(1000)
rglaft(9,50,STime="os",Event="death",1,hdata)
##

afthd documentation built on Oct. 1, 2021, 5:08 p.m.