SurviMChd (version 0.1.2)

fraidpm: Frailty with drichlet process mixture

Description

Frailty analysis on high dimensional data by Drichlet process mixture.

Usage

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

Value

fraidpmout omeg[i] are frailty effects.

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.

Author

Atanu Bhattacharjee and Akash Pawar

Details

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

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

# \donttest{
##
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)
##
# }