rglwbysu: Bayesian univariate analysis of AFT model for selected...

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

Description

Provides posterior Estimates of selected variable(by regularization technique) in AFT model for univariate in high dimensional gene expression data with MCMC. Incorporated variable selection has been done with regularization technique. It also deals covariates with missing values.

Usage

rglwbysu(m, n, STime, Event, nc, ni, 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.
nc	number of chain used in model.
ni	number of iteration used in model.
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.

Details

Here weibull distribution has been used for AFT model with MCMC. This function deals covariates (in data) with missing values. Missing value in any column (covariate) is replaced by mean of that particular covariate.

Value

posterior estimates (Coef, SD, Credible Interval) of regression coefficient for all selected covariate (using regularization) in model and deviance.

Author(s)

Atanu Bhattacharjee, Gajendra Kumar Vishwakarma and Pragya Kumari

References

Prabhash et al(2016) <doi:10.21307/stattrans-2016-046>

See Also

wbysuni,wbysmv, rglwbysm

Examples

##
data(hdata)
set.seed(1000)
rglwbysu(9,45,STime="os",Event="death",2,10,1,hdata)
##

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