Fit a non-population dynamic model using mcmc

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

Wenping Wang

Examples

# \donttest{

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))
#> 
#>              mean           sd       cv%
#> MIT  4.170892e+04 6.341609e+03 15.204444
#> CVI2 5.001683e-01 9.092042e-02 18.177964
#> F    8.446273e-01 5.838073e-02  6.912011
#> err1 1.092145e-02 6.184806e-03 56.629925
#> 
#> # samples: 500 
# }