simdata_time_dep: Simulation function for continuous trait with time-dependant...

In stpm: Stochastic Process Model for Analysis of Longitudinal and Time-to-Event Outcomes

Simulation function for continuous trait with time-dependant coefficients.

Description

Simulation function for continuous trait with time-dependant coefficients.

Usage

simdata_time_dep(
  N = 10,
  f = list(at = "-0.05", f1t = "80", Qt = "2e-8", ft = "80", bt = "5", mu0t = "1e-3"),
  step = 1,
  tstart = 30,
  tend = 105,
  ystart = 80,
  sd0 = 1,
  nobs = NULL,
  format = "short"
)

Arguments

N	Number of individuals.
f	a list of formulas that define age (time) - dependency. Default: list(at="a", f1t="f1", Qt="Q*exp(theta*t)", ft="f", bt="b", mu0t="mu0*exp(theta*t)")
step	An interval between two observations, a random uniformally-distributed value is then added to this step.
tstart	Starting time (age). Can be a number (30 by default) or a vector of two numbers: c(a, b) - in this case, starting value of time is simulated via uniform(a,b) distribution.
tend	A number, defines final time (105 by default).
ystart	A starting value of covariates.
sd0	A standard deviation for modelling the next covariate value, sd0 = 1 by default.
nobs	A number of observations (lines) for individual observations.
format	Data format: "short" (default), "long".

Value

A table with simulated data.

References

Yashin, A. et al (2007), Health decline, aging and mortality: how are they related? Biogerontology, 8(3), 291-302.<DOI:10.1007/s10522-006-9073-3>.

Examples

library(stpm)
dat <- simdata_time_dep(N=100)
head(dat)

stpm documentation built on Sept. 5, 2022, 5:06 p.m.