lgnbymv: Bayesian multivariate analysis of AFT model with log normal...

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

Description

Provides posterior estimates of AFT model with log normal distribution using Bayesian for multivariate (maximum 5 at a time) in high dimensional gene expression data. It also deals covariates with missing values.

Usage

lgnbymv(m, n, STime, Event, nc, ni, 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 MCMC chain.
ni	number of MCMC iteration to update the outcome.
data	High dimensional gene expression data that contains event status, survival time and and set of covariates.

Details

This function deals covariates (in data) with missing values. Missing value in any column (covariate) is replaced by mean of that particular covariate. AFT model is log-linear regression model for survival time T_{1}, T_{2},..,T_{n}. i.e.,

log(T_i)= x_i'β +σε_i ;~ε_i \sim F_ε (.)~which~is~iid

Where F_ε is known cdf which is defined on real line. When baseline distribution is normal then T follows log normal distribution.

T \sim LN(x'β,1/τ)

Value

Data frame is containing mean, sd, n.eff, Rhat and credible intervals for beta's, sigma, tau and deviance of the model for the chosen covariates. beta[1] is for intercept and others are for covariates (which is/are chosen as columns in data). sigma is the scale parameter of the distribution.

Author(s)

Atanu Bhattacharjee, Gajendra Kumar Vishwakarma and Pragya Kumari

References

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

See Also

lgnbyuni, wbysmv, lgstbymv

Examples

##
data(hdata)
lgnbymv(10,12,STime="os",Event="death",2,100,hdata)
##

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