Description Usage Arguments Details Value Author(s) See Also Examples

Aggregate and construct the data for quasi-likelihood estimation

1 2 3 |

`qldata` |
data frame of (initial) simulation results (see |

`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 |

`nfit` |
number of cycles, |

`cv.fit` |
logical, |

`var.type` |
name of the variance approximation method (see |

`useVar` |
logical, |

`criterion` |
global criterion function for sampling and minimization, either " |

`verbose` |
logical, |

The function aggregates all required information for quasi-likelihood estimation, 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 as defined by `setCovModel`

. The user has the choice whether or not to
make use of of kriging prediction variances by '`useVar`

' to account for the simulation error when constructing
the approximation of the variance matrix and the quasi-score function. If `TRUE`

, then a kriging procedure calculating
prediction variances is automatically used. Otherwise the so-called *dual* approach is employed which has some computational
advantage if prediction variances are not required.

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 fitting.

M. Baaske

1 2 3 4 5 6 7 8 9 10 | ```
data(normal)
# We simply re-use the stored normal data and fit again:
# fit generalized covariance model to the data using
# simulation variances as local nugget variances
mods <- fitSIRFk(qsd$qldata, verbose=TRUE)
# construct QL approximation model
qsd <- QLmodel(qsd$qldata,qsd$lower,qsd$upper,
c("T1"=2,"T2"=1),mods)
``` |

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