QLmodel: Construct the quasi-likelihood approximation model

In qle: Simulation-Based Quasi-Likelihood Estimation

Description

Aggregate and construct the data for quasi-likelihood estimation

Usage

QLmodel(qldata, lb, ub, obs, mods, nfit = 1, cv.fit = TRUE,
  var.type = c("wcholMean", "cholMean", "wlogMean", "logMean", "kriging",
  "const"), criterion = c("qle", "mahal"), verbose = FALSE)

Arguments

qldata	data frame of (initial) simulation results (see setQLdata)
lb	numeric vector of lower bounds defining the (hyper)box
ub	numeric vector of upper bounds defining the (hyper)box
obs	numeric vector of observed statistics
mods	list of (fitted) covariance models (see fitSIRFk)
nfit	number of cycles, nfit=1 (default), after which covariance parameters are re-estimated and otherwise only re-used
cv.fit	logical, TRUE (default), whether to re-fit CV models (re-estimate covariance parameters)
var.type	name of the variance approximation method (see covarTx)
criterion	global criterion function for sampling and minimization, either "qle" or "mahal"
verbose	logical, FALSE (default), whether to give intermediate output

Details

The function aggregates all required information for quasi-likelihood estimation and defines the input object to the function qle, stores the fitted covariance models of the sample means of the statistics and the type of variance matrix approximation. For an advanced setup of the estimation procedure and more involved statistical models this function explicitly offers the data structure to construct individual covariance models for each statistic, see setCovModel. The kriging procedure includes the computation of prediction variances based on kriging.

Value

An object of class QLmodel which stores the data frame of simulation results, bounds on the parameter space, covariance models for kriging, vector of observed statistics as well as options for kriging and covariance parameter estimation.

Author(s)

M. Baaske

See Also

getQLmodel, updateCovModels

Examples

data(normal)

# As an example we re-use the stored normal data and fit 
# a generalized covariance model to the data using simulation
# variances as local variances for REML estimation.
mods <- fitSIRFk(qsd$qldata, verbose=TRUE)

# construct QL approximation model
qsd <- QLmodel(qsd$qldata,qsd$lower,qsd$upper,
			    c("T1"=2,"T2"=1),mods)

qle documentation built on May 2, 2019, 5:26 p.m.