getQLmodel {qle} | R Documentation |
Initial setup of the quasi-likelihood approximation model
getQLmodel(runs, lb, ub, obs, X = NULL, criterion = "qle", ...)
runs |
object of class |
lb |
lower bounds defining the (hyper)box of the parameter domain for QL estimation |
ub |
upper bounds defining the (hyper)box of the parameter domain for QL estimation |
obs |
numeric vector of observed statistics |
X |
matrix of sample locations (model parameters) |
criterion |
name of criterion function to be minimized for QL estimation (see |
... |
arguments passed to |
The function is a wrapper of the functions QLmodel
, fitSIRFk
and thus sets up the quasi-likelihood approximation model all at once given the simulation results
of the initial design runs
. Note that the bound constraints lb,ub
can be different from the ones
used to construct the initial design by simQLdata
. This allows to use, for example, an enlarged
parameter space for the design points and a smaller one for the QL parameter estimation which might prevent the
algorithm from exceeding the parameter space during optimization.
Object of class QLmodel
M. Baaske
data(normal) # simulate model at a minimum of required design points sim <- simQLdata(sim=qsd$simfn,nsim=5,N=8, method="maximinLHS",lb=qsd$lower,ub=qsd$upper) # true and error-free observation obs <- structure(c("T1"=2,"T2"=1), class="simQL") # construct QL approximation model qsd <- getQLmodel(sim,qsd$lower,qsd$upper,obs,var.type="wcholMean")