dynmodel.mcmc: Fit a non-population dynamic model using mcmc

In nlmixrdevelopment/nlmixr: Nonlinear Mixed Effects Models in Population PK/PD

Fit a non-population dynamic model using mcmc

Description

Fit a non-population dynamic model using mcmc

Usage

dynmodel.mcmc(
  system,
  model,
  evTable,
  inits,
  data,
  fixPars = NULL,
  nsim = 500,
  squared = TRUE,
  seed = NULL
)

Arguments

system	an RxODE object
model	a list of statistical measurement models
evTable	an Event Table object
inits	initial values of system parameters
data	input data
fixPars	fixed system parameters
nsim	number of mcmc iterations
squared	if parameters be squared during estimation
seed	random number seed

Value

A dyn.mcmc object detailing the model fit

Author(s)

Wenping Wang

Examples

ode <- "
   dose=200;
   pi = 3.1415926535897931;

   if (t<=0) {
      fI = 0;
   } else {
      fI = F*dose*sqrt(MIT/(2.0*pi*CVI2*t^3))*exp(-(t-MIT)^2/(2.0*CVI2*MIT*t));
   }

   C2 = centr/V2;
   C3 = peri/V3;
   d/dt(centr) = fI - CL*C2 - Q*C2 + Q*C3;
   d/dt(peri)  =              Q*C2 - Q*C3;
"
sys1 <- RxODE(model = ode)


## ------------------------------------------------------------------------
dat <- invgaussian
mod <- cp ~ C2 + prop(.1)
inits <- c(MIT = 190, CVI2 = .65, F = .92)
fixPars <- c(CL = .0793, V2 = .64, Q = .292, V3 = 9.63)
ev <- eventTable()
ev$add.sampling(c(0, dat$time))
(fit <- dynmodel.mcmc(sys1, mod, ev, inits, dat, fixPars))

nlmixrdevelopment/nlmixr documentation built on Aug. 22, 2023, 2:16 p.m.