| jmcsBig {jmBIG} | R Documentation |
Joint model for BIG data using FastJM
Description
function for joint model in BIG DATA using FastJM
Usage
jmcsBig(dtlong, dtsurv, longm, survm, samplesize = 50, rd, id)
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 |
model for longitudinal response |
survm |
survival model |
samplesize |
sample size to divide the Big data |
rd |
random effect part |
id |
name of id column in longitudinal dataset |
Value
returns a list containing various output which are useful for prediction.
Author(s)
Atanu Bhattacharjee, Bhrigu Kumar Rajbongshi and Gajendra Kumar Vishwakarma
References
Li, Shanpeng, et al. "Efficient Algorithms and Implementation of a Semiparametric Joint Model for Longitudinal and Competing Risk Data: With Applications to Massive Biobank Data." Computational and Mathematical Methods in Medicine 2022 (2022).
See Also
jmbayesBig,jmstanBig,joinRMLBig
Examples
##
library(survival)
library(dplyr)
fit2<-jmcsBig(dtlong=data.frame(long2),dtsurv = data.frame(surv2),
longm=y~ x7+visit,survm=Surv(time,status)~x1+visit,rd= ~ visit|id,samplesize=200,id='id')
print(fit2)
##