stpm (version 1.3.2)

simdata_cont: Multi-dimensional simulation function for continuous-time SPM.

Description

Multi-dimensional simulation function for continuous-time SPM.

Usage

simdata_cont(N = 10, a = -0.05, f1 = 80, Q = 2e-08, f = 80, b = 5, mu0 = 1e-05, theta = 0.08, ystart = 80, tstart = 30, tend = 105, dt = 1, sd0 = 1, nobs = NULL, gomp = FALSE)

Arguments

N

Number of individuals.

a

A k by k matrix, represents the adaptive capacity of the organism

f1

A trajectory that corresponds to the long-term average value of the stochastic process Y(t), which describes a trajectory of individual covariate (physiological variable) influenced by different factors represented by a random Wiener process W(t). This is a vector with length of k.

Q

A matrix k by k, which is a non-negative-definite symmetric matrix, represents a sensitivity of risk function to deviation from the norm.

f

A vector with length of k, represents the normal (or optimal) state of physiological variable.

b

A diffusion coefficient, k by k matrix, characterizes a strength of the random disturbances from Wiener process W(t).

mu0

A baseline mortality.

theta

A displacement coefficient.

ystart

A vector with length equal of k, 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 covariate value.

nobs

A number of observations (lines) for individual observations.

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), Q = Q*exp(theta*t).

Value

A table with simulated data.

References

Yashin, A.I. et al (2007). Stochastic model for analysis of longitudinal data on aging and mortality. Mathematical Biosciences, 208(2), 538-551..

Examples

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