Fit a non-population dynamic model using mcmc
Fit a non-population dynamic model using mcmc
dynmodel.mcmc( system, model, evTable, inits, data, fixPars = NULL, nsim = 500, squared = TRUE, seed = NULL )
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 |
A dyn.mcmc object detailing the model fit
Wenping Wang
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))Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.