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

View source: R/dynmodel.R

dynmodel.mcmcR Documentation

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))


nlmixr documentation built on March 27, 2022, 5:05 p.m.

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