These functions take an `SLik`

object (as produced by `MSL`

) and samples its parameter space in (hopefully) clever ways, not yet well documented. `rparam`

calls `sample_volume`

to define points targeting the likelihood maximum and the bounds of confidence intervals, with `n`

for these different targets dependent on the mean square error of prediction of likelihood at the maximum and at CI bounds.

1 2 3 4 5 6 7 | ```
rparam(object, n= 1, useEI = list(max=TRUE,profileCI=TRUE,rawCI=FALSE),
useCI = TRUE, verbose = interactive(), tryn=30*n,
level = 0.95, CIweight=Infusion.getOption("CIweight"))
sample_volume(object, n = 6, useEI, vertices=NULL,
dlr = NULL, verbose = interactive(),
fixed = NULL, tryn= 30*n)
``` |

`object` |
an |

`n` |
The number of parameter points to be produced |

`useEI` |
List of booleans, each determining whether to use an “expected improvement” (EI) criterion (e.g. Bingham et al., 2014) to select candidate parameter points to better ascertain a particular focal point. The elements |

`vertices` |
Points are sampled within a convex hull defined by |

`useCI` |
Whether to define points targeting the bounds of confidence intervals for the parameters. An expected improvement criterion is also used here. |

`level` |
If |

`dlr` |
A (log)likelihood ratio threshold used to select points in the upper region of the likelihood surface. Default value is
given by |

`verbose` |
Whether to display some information about selection of points, or not |

`fixed` |
A list or named vector, of which each element is of the form |

`tryn` |
See |

`CIweight` |
For development purposes, not documented. |

a data frame of parameter points. Only parameters variable in the `SLik`

object are considered.

D. Bingham, P. Ranjan, and W.J. Welch (2014) Design of Computer Experiments for Optimization, Estimation of Function Contours, and Related Objectives, pp. 109-124 in Statistics in Action: A Canadian Outlook (J.F. Lawless, ed.). Chapman and Hall/CRC.

1 2 3 4 5 | ```
if (Infusion.getOption("example_maxtime")>10) {
data(densv)
summliksurf <- infer_surface(densv) ## infer a log-likelihood surface
sample_volume(summliksurf)
}
``` |

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