Fit a non-population dynamic model using mcmc

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

Arguments

system

an RxODE object

model

a list of statistical meaurement models

evTable

an Event Table object

inits

initial values of system parameters

data

input data

fixPars

fixed system paraameters

nsim

number of mcmc iteractions

squared

if parameters be squared during estimation

seed

random number seed

Examples

# NOT RUN {
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 <- read.table("invgaussian.txt", header=TRUE)
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))

# }