rglaft {afthd}

Estimates of selected univariate covariates(using regularization) in AFT model without 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(-\eta(x;\beta)))

and baseline survival function is modelled as

S_0(T)=\exp(-\exp(\eta_0(log(T);\beta_0)))

Where \eta and \eta 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)
##