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

The function estimates the (hyper)parameters of the covariance models by
the *Restricted Maximum Likelihood* (REML) method.

1 |

`models` |
object either of class |

`Xs` |
matrix of sample points, the design |

`data` |
data frame of simulated sample means of statistics
first column corrsponds to the first model in the list ' |

`controls` |
list of control parameters, see |

`cl` |
cluster object, |

`verbose` |
logical, |

The function fits a list of covariance models using the REML method. In order to avoid singularities
of the so-called trend matrices make sure to use at least the minimum required number of sample points stored in
'`Xs`

' which depends on the defined trend order, see `setCovModel`

.

An object of class `reml`

which consists of a list of named lists
('`model`

', '`convergence`

') each storing a fitted covariance model itself
together with the optimization results from `nloptr`

as an attribute
named '`optres`

'. The default method for estimating the covariance parameters is
`mlsl`

which uses random starting points and thus could produce different results if
it is run multiple times. If the results strongly vary, then the corresponding REML function might have many
local minima which precludes the use of this default algorithm and another one, e.g. '`NLOPT_GN_DIRECT`

'
(see `nloptr.print.options`

), might lead to better results.

M. Baaske

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