jmbayesBig: Joint model for BIG data using JMbayes2

In jmBIG: Joint Longitudinal and Survival Model for Big Data

Joint model for BIG data using JMbayes2

Description

function for joint model in BIG DATA using JMbayes2

Usage

jmbayesBig(
  dtlong,
  dtsurv,
  longm,
  survm,
  samplesize = 50,
  rd,
  timeVar,
  nchain = 1,
  id,
  niter = 2000,
  nburnin = 1000
)

Arguments

dtlong	longitudinal dataset, which contains id,visit time,longitudinal measurements along with various covariates
dtsurv	survival dataset corresponding to the longitudinal dataset, with survival status and survival time
longm	fixed effect model for longitudinal response
survm	survival model
samplesize	sample size to divide the Big data
rd	random effect model part
timeVar	time variable in longitudinal model, included in the longitudinal data
nchain	number of chain for MCMC
id	name of id column in longitudinal dataset
niter	number of iteration for MCMC chain
nburnin	number of burnin sample for MCMC chain

Value

returns a list containing various output which are useful for prediction.

Author(s)

Atanu Bhattacharjee, Bhrigu Kumar Rajbongshi and Gajendra Kumar Vishwakarma

References

Rizopoulos, D., G. Papageorgiou, and P. Miranda Afonso. "JMbayes2: extended joint models for longitudinal and time-to-event data." R package version 0.2-4 (2022).

See Also

jmcsBig,jmstanBig,joinRMLBig

Examples

 
##
library(survival)
library(nlme)
library(dplyr)
fit5<-jmbayesBig(dtlong=long2,dtsurv = surv2,longm=y~ x7+visit,survm=Surv(time,status)~x1+visit,
rd= ~ visit|id,timeVar='visit',nchain=1,samplesize=200,id='id')
ydt<-long2%>%filter(id%in%c(900))
cdt<-surv2[,'id']%>%filter(id%in%c(900))
newdata<-full_join(ydt,cdt,by='id')
P2<-predJMbayes(model<-fit5,ids<-c(900),newdata=newdata,process = 'event')
plot(P2$p1[[1]])
##

jmBIG documentation built on May 29, 2024, 6:04 a.m.