simJM: Simulate joint model data

In mayrandy/JMboost: Boosting Joint Models for Longitudinal and Time-to-Event Outcomes

Description

Simulate data frames for joint modelling of longitudinal and time-to-event outcome.

Usage

simJM(n = 100, n_i = 5, alpha = 0.5,
      beta, betals = 0, betatimeind = 0, lambda, 
      noninf = 0, noninfls = 0)

Arguments

n	number of individuals.
n_i	number of observations per individual (before censoring).
alpha	association parameter.
beta	coefficient beta vector for longitudinal outcome.
betals	coefficient beta vector for shared predictor.
betatimeind	index of time-variable in shared predictor.
lambda	constant bazeline hazard.
noninf	noninf: number of non informative covariates for the longitudinal sub-predictor.
noninfls	noninfls: number of non informative covariates for the shared sub-predictor

Details

Simulates a data frame for joint models, considering a longitudinal and a shared predictor. For more details, see the Appendix of Waldmann et al. (2016).

Value

A data frame.

References

Waldmann, E., Taylor-Robinson, D., Klein, N., Kneib, T., Pressler, T., Schmid, M., & Mayr, A. (2016). Boosting Joint Models for Longitudinal and Time-to-Event Data. arXiv preprint arXiv:1609.02686.

Examples

set.seed(123)
dat <- simJM(n = 400, n_i = 3, alpha = .5,
            beta = c(1,2,3), betals = c(2,3,1),
            betatimeind = 3, lambda = 0.6)

j1 <- JMboost(y = dat$y, Xl = dat$Xl, Xls = dat$Xls,
              last = dat$last, delta = dat$delta,
              id = dat$id, time = dat$time, lambda = 1, alpha = 0.1,
              mstop_l = 100, mstop_ls = 100, step.length = 0.1,
              betatimeind = 3)

mayrandy/JMboost documentation built on May 21, 2019, 2:23 p.m.