lgnbymv {afthd} R Documentation

Bayesian multivariate analysis of AFT model with log normal distribution.

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'\beta +\sigma\epsilon_i ;~\epsilon_i \sim F_\epsilon (.)~which~is~iid

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

 T \sim LN(x'\beta,1/\tau)

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>

##