fraidpm: Frailty with drichlet process mixture

In SurviMChd: High Dimensional Survival Data Analysis with Markov Chain Monte Carlo

Description

Frailty analysis on high dimensional data by Drichlet process mixture.

Usage

fraidpm(m, n, Ins, Del, Time, T.min, chn, iter, adapt, data)

Arguments

m	Starting column number form where study variables to be selected.
n	Ending column number till where study variables will get selected.
Ins	Variable name of Institute information.
Del	Variable name containing the event information.
Time	Variable name containing the time information.
T.min	Variable name containing the time of event information.
chn	Number of MCMC chains.
iter	Define number of iterations as number.
adapt	Define number of adaptations as number.
data	High dimensional data, event information given as (delta=0 if alive, delta=1 if died). If patient is censored then t.min=duration of survival. If patient is died then t.min=0. If patient is died then t=duration of survival. If patient is alive then t=NA.

Details

By given m and n, a total of 3 variables can be selected.

Value

fraidpmout omeg[i] are frailty effects.

Author(s)

Atanu Bhattacharjee and Akash Pawar

References

Bhattacharjee, A. (2020). Bayesian Approaches in Oncology Using R and OpenBUGS. CRC Press.

Congdon, P. (2014). Applied bayesian modelling (Vol. 595). John Wiley & Sons.

See Also

fraidm frairand

Examples

## Not run: 
##
data(frailty)
fraidpm(m=5,n=7,Ins="institute",Del="del",Time="timevar",T.min="time.min",chn=2,iter=6,
adapt=100,data=frailty)
##

## End(Not run)

SurviMChd documentation built on May 23, 2021, 5:07 p.m.