refine | R Documentation |

This is a generic function with currently methods for `SLik`

, `SLik_j`

and `SLikp`

objects (as produced by `MSL`

). Depending on the value of its `newsimuls`

argument, and on whether the function used to generate empirical distributions can be called by R, it (1) defines new parameters points and/or (2) infers their summary likelihood or tail probabilities for each parameter point independently, adds the inferred values results as input for refined inference of likelihood or P-value response surface, and provides new point estimates and confidence intervals.

## S3 method for class 'SLik' refine(object, method=NULL, ...) ## Default S3 method: refine(object, surfaceData, Simulate = attr(surfaceData,"Simulate"), maxit = 1, n = NULL, useEI = list(max=TRUE,profileCI=TRUE,rawCI=FALSE), newsimuls = NULL, trypoints=NULL, useCI = TRUE, level = 0.95, verbose = list(most=interactive(),final=NULL,movie=FALSE,proj=FALSE), precision = Infusion.getOption("precision"), nb_cores = NULL, packages=attr(object$logLs,"packages"), env=attr(object$logLs,"env"), method, eval_RMSEs=TRUE, update_projectors = FALSE, cluster_args=list(), cl_seed=.update_seed(object), ...)

`object` |
an |

`surfaceData` |
A data.frame with attributes, usually taken from the |

`Simulate` |
Character string: name of the function used to simulate samples. The only meaningful non-default value is |

`maxit` |
Maximum number of iterative refinements (see also |

`n` |
A number of parameter points (excluding replicates and confidence interval points), whose likelihood should be computed
(see |

`useEI` |
Cf this argument in |

`newsimuls` |
For the For other methods, a |

`trypoints` |
A data frame of parameters on which the simulation function |

`useCI` |
whether to include parameter points near the inferred confidence interval points in the set of points which likelihood should be computed |

`level` |
Intended coverage of confidence intervals |

`verbose` |
A list as shown by the default, or simply a vector of booleans. |

`precision` |
Requested local precision of surface estimation, in terms of prediction standard errors (RMSEs) of both the maximum summary log-likelihood and the likelihood ratio at any CI bound available. Iterations will stop when either |

`nb_cores` |
Shortcut for |

`cluster_args` |
A list of arguments for |

`packages` |
NULL or a list with possible elements |

`env` |
An environment, passed as the |

`method` |
(A vector of) suggested method(s) for estimation of smoothing parameters (see |

`eval_RMSEs` |
passed to |

`update_projectors` |
Boolean; whether to update the projectors at each iteration. |

`cl_seed` |
NULL or integer, passed to |

`...` |
further arguments passed to or from other methods. |

New parameter points are sampled as follows: the algorithm aims to sample uniformly the space of parameters contained in the confidence regions defined by the `level`

argument, and to surround it by a region sampled proportionally to likelihood. In each iteration the algorithm aims to add as many points (say *n*) as computed in the first iteration, so that after *k* iterations of `refine`

, there are *n * (k+1)* points in the simulation table. However, when not enough points satisfy certain criteria, only *n/5* points may be added in an iteration, this being compensated in further iterations. For example, if *n=600*, the table may include only 720 points after the first refine, but 1800 after the second.

independent control of parallel computation for sample simulation and RMSE computations is possible:

`control_args=list(RMSE=list(spec=<number of 'children'>))`

can be used to force parallel computation of RMSEs;

`control_args=list(spec=<.>, <other makeCluster arguments>))`

would instead apply the same arguments to both reference table and RMSE computation, overcoming the default effect of `nb_cores`

; finally

`control_args=list(reftable=list(<makeCluster arguments>),RMSEs=list(<makeCluster arguments>))`

allows full independent control of parallelisation for the two computations.

`refine`

returns an updated `SLik`

or `SLik_j`

object.

See workflow examples in (by order of decreasing relevance) `example_reftable`

, `example_raw_proj`

and `example_raw`

.

## see Note for links to examples.

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.