sim_pobs: Multi-dimension simulation function for data with partially...

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

Multi-dimension simulation function for data with partially observed covariates (multidimensional GenSPM) with arbitrary intervals

Description

Multi-dimension simulation function for data with partially observed covariates (multidimensional GenSPM) with arbitrary intervals

Usage

sim_pobs(
  N = 10,
  aH = -0.05,
  aL = -0.01,
  f1H = 60,
  f1L = 80,
  QH = 2e-08,
  QL = 2.5e-08,
  fH = 60,
  fL = 80,
  bH = 4,
  bL = 5,
  mu0H = 8e-06,
  mu0L = 1e-05,
  thetaH = 0.08,
  thetaL = 0.1,
  p = 0.25,
  ystart = 80,
  tstart = 30,
  tend = 105,
  dt = 1,
  sd0 = 1,
  mode = "observed",
  gomp = FALSE,
  nobs = NULL
)

Arguments

N	Number of individuals.
aH	A k by k matrix, which characterize the rate of the adaptive response when Z = 1.
aL	A k by k matrix, which characterize the rate of the adaptive response when Z = 0.
f1H	A particular state, which if a deviation from the normal (or optimal) when Z = 1. This is a vector with length of k.
f1L	A particular state, which if a deviation from the normal (or optimal) when Z = 0. This is a vector with length of k.
QH	A matrix k by k, which is a non-negative-definite symmetric matrix when Z = 1.
QL	A matrix k by k, which is a non-negative-definite symmetric matrix when Z = 0.
fH	A vector-function (with length k) of the normal (or optimal) state when Z = 1.
fL	A vector-function (with length k) of the normal (or optimal) state when Z = 0.
bH	A diffusion coefficient, k by k matrix when Z = 1.
bL	A diffusion coefficient, k by k matrix when Z = 0.
mu0H	mortality at start period of time when Z = 1.
mu0L	mortality at start period of time when Z = 0.
thetaH	A displacement coefficient of the Gompertz function when Z = 1.
thetaL	A displacement coefficient of the Gompertz function when Z = 0.
p	A proportion of carriers in a sumulated population (default p = 0.25).
ystart	A vector with length equal to number of dimensions used, defines starting values of covariates.
tstart	A number that defines starting time (30 by default).
tend	A number, defines final time (105 by default).
dt	A discrete step size between two observations. A random uniform value is then added to this step size.
sd0	A standard deviation for modelling the next physiological variable (covariate) value.
mode	Can have the following values: "observed" (default), "unobserved". This represents a type of group to simulate: a group with observed variable Z, or group with unbobserved variable Z.
gomp	A flag (FALSE by default). When it is set, then time-dependent exponential form of mu0 and Q are used: mu0 = mu0*exp(theta*t).
nobs	A number of observations (lines) for individual observations.

Value

A table with simulated data.

References

Arbeev, K.G. et al (2009). Genetic model for longitudinal studies of aging, health, and longevity

Yashin, A.I. et al (2007). Stochastic model for analysis of longitudinal data on aging and mortality. Mathematical Biosciences, 208(2), 538-551.<DOI:10.1016/j.mbs.2006.11.006>.

Examples

library(stpm)
dat <- sim_pobs(N=50)
head(dat)

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