Dear All,
 
based on the following set of codes:
 
require(deSolve)
require(FME)
pars <- list(k = 0.08,v=15)
intimes <- c(0,0.5,12)
input   <- c(800,0,0)
forc <- approxfun(intimes, input, method="constant", rule=2)
derivs <- function(t, state, pars) {
     inp <- forc(t)
     dy1 <- - pars$k * state[1] + inp/pars$v
     return(list(c(dy1)))}
model <- function(pars, times=seq(0, 13, by = 0.01)) {
  state <- c(y = 0)
  return(ode(y = state, times = times, func = derivs, parms = pars,
    method="lsoda"))
}
observed <- matrix (nc=2,byrow=2,data=c(0,0,0.5,19.7,11.5,11.3))
colnames(observed) <- c("time", "y")
tout <- unique(sort(c(intimes, observed[,"time"])))
obj <- function(x, parset = names(x)) {
 pars[parset] <- x
 out <- model(pars, tout)
 return(modCost(obs = observed, model = out))
}
prior <- function(p)
  return( sum(((p-c(0.08,15))/c(0.04,6))^2 ))
final <- modMCMC(p = c(k=0.08,v=15), f = obj, prior=prior,lower=c(0.0001,0.1),
  jump = NULL, niter = 100,updatecov=10, wvar0 = 1,var0=NULL,burninlength = 10)
 
 
the FME package documentation notes: 
 
bestpar : the parameter set that gave the highest probability.
bestfunp : the function value corresponding to bestpar
 
my question/s are what is the selection of the "highest probability" is based on and how is bestfunp calculated? I am asked to explain these two parameters of modMCMC results in statistical terms and could really use the help,
 
thank you,
 
Andras 
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