SurviMChd (version 0.1.2)

fraidm: Frailty with Discrete Mixture Model

Description

Discrete mixture model with MCMC

Usage

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

Value

fraidmout - b[1] is the posterior estimate of the regression coefficient for first covariate.

b[2] is the posterior estimate of the regression coefficient for second covariate.

b[3] is the posterior estimate of the regression coefficient for third covariate.

omega[1] and omega[2] are frailty effects.

c[1] and c[2] are regression intercept and coefficients of covariates over mean effect.

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.

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.

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

fraidpm frairand

Examples

# \donttest{
##
data(frailty)
fraidm(m=5,n=7,Ins="institute",Del="del",Time="timevar",T.min="time.min",chn=2,iter=6,data=frailty)
##
# }