wbysuni: Posterior univariate estimates of AFT model with weibull...

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

Description

Provides posterior estimates of AFT model with weibull distribution using MCMC for univariate in high dimensional gene expression data. It also deals covariates with missing values.

Usage

wbysuni(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 status in data. 0 is for censored and 1 for occurrence of event.
nc	number of markov chain.
ni	number of iteration for MCMC.
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. Here, when baseline distribution is extreme value then T follows weibull distribution. To make interpretation of regression coefficients simpler, using extreme value distribution with median 0. So using weibull distribution that leads to AFT model when

T \sim Weib(√{τ},log(2)\exp(-x'β √{τ}))

Value

Data frame is containing posterior estimates (Coef, SD, Credible Interval, Rhat, n.eff) of regression coefficient for covariates and deviance. Result shows together for all covariates chosen from column m to n.

Author(s)

Atanu Bhattacharjee, Gajendra Kumar Vishwakarma and Pragya Kumari

References

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

See Also

pvaft, wbysmv, rglwbysu, wbyscrku

Examples

##
data(hdata)
wbysuni(9,13,STime="os",Event="death",1,10,hdata)
##

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