getQLmodel: Setup the quasi-likelihood estimation model

In mbaaske/qle: Simulation-Based Quasi-Likelihood Estimation

Description

Setup the quasi-likelihood model data

Usage

getQLmodel(runs, lb, ub, obs, X = NULL, useVar = TRUE, criterion = "qle",
  ...)

Arguments

runs	object of class simQL as simulation results from simQLdata
lb	lower bounds defining the (hyper)box
ub	upper bounds defining the (hyper)box
obs	numeric vector of observed statistics
X	matrix of sample locations (model parameters)
useVar	logical, TRUE (default), whether to use prediction variances
criterion	the criterion function to be minimized for parameter estimation (see qle)
...	arguments passed to fitSIRFk, setQLdata, setCovModel and QLmodel for fitting kriging covariance models

Details

The function is a wrapper to setQLdata and fitSIRFk in order to setup the data required for estimating the model parameters.

Value

Object of class QLmodel

Author(s)

M. Baaske

See Also

simQLdata, QLmodel, fitSIRFk

Examples

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")

mbaaske/qle documentation built on May 27, 2019, midnight