fraidpm | R Documentation |
Frailty analysis on high dimensional data by Drichlet process mixture.
fraidpm(m, n, Ins, Del, Time, T.min, chn, iter, adapt, data)
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. |
By given m and n, a total of 3 variables can be selected.
fraidpmout omeg[i] are frailty effects.
Atanu Bhattacharjee and Akash Pawar
Bhattacharjee, A. (2020). Bayesian Approaches in Oncology Using R and OpenBUGS. CRC Press.
Congdon, P. (2014). Applied bayesian modelling (Vol. 595). John Wiley & Sons.
fraidm frairand
##
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)
##
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