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

The function constructs a list of covariance models of statistics in order to estimate the prediction error variances by a cross-validation approach at unsampled points.

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`qsd` |
object of class |

`reduce` |
if |

`type` |
type of prediction variances, " |

`control` |
control arguments for REML estimation, passed to |

`cl` |
cluster object, |

`verbose` |
if |

Using the cross-validation approach (see vignette) for estimating the prediction variances
might require a refit of covariance parameters of each statistic based on the remaining sample points.
The covariance models are refitted if '`fit`

' equals `TRUE`

and otherwise simply updated without fitting which
saves some computational resources. The number of points left-out, if applicable, is dynamically adjusted depending on the number
of sample points in order to prevent the main estimation algorithm to fit as many models as there are points already evaluated.

The number *n_c* of covariance models still to fit, that is, the number of partitioning sets of sample points, is limited by
*n_c≤q n*, with maximum *k* sampling points deleted from the full sample set with overall *n* sample points such that
*n=n_c k* (see also the vignette for further details).

A list of certain length depending on the current sample size (number of evaluated points).
Each list element corresponds to a (possibly reduced) number of sample points with at most *k* points
(see details) left-out for fitting the corresponding covariance models.

M. Baaske

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