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 (CV) approach at unsampled points.

1 2 |

`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 CV-based approach (see vignette) for estimating the prediction variances
might require a refit of covariance parameters of each statistic based on leaving out a certain number of sample points.
The covariance models can be refitted if '`fit`

' equals `TRUE`

and otherwise are simply updated without fitting which
saves some computational resources. The number of points left out 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.

For CV 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 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 (reduced) number of sample points with at most *k* points
(see details) left out for fitting the covariance models.

M. Baaske

1 2 3 4 5 6 |

Embedding an R snippet on your website

Add the following code to your website.

For more information on customizing the embed code, read Embedding Snippets.