Description Usage Arguments Details Value Author(s) See Also

Wrapper around `trust`

allowing for multiple fits
from randomly chosen initial values.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 |

`objfun` |
Objective function, see |

`center` |
Parameter values around which the initial values for each fit
are randomly sampled. The initial values handed to trust are the sum
of center and the output of samplefun, center +
samplefun. See |

`studyname` |
The names of the study or fit. This name is used to determine filenames for interim and final results. See Details. |

`rinit` |
Starting trust region radius, see |

`rmax` |
Maximum allowed trust region radius, see |

`fits` |
Number of fits (jobs). |

`cores` |
Number of cores for job parallelization. |

`samplefun` |
Function to sample random initial values. It is assumed,
that samplefun has a named parameter "n" which defines how many
random numbers are to be returned, such as for |

`resultPath` |
character indicating the folder where the results should be stored. Defaults to ".". |

`stats` |
If true, the same summary statistic as written to the logfile is printed to command line on mstrust completion. |

`output` |
logical. If true, writes output to the disc. |

`...` |
Additional parameters handed to trust(), samplefun(), or the objective function by matching parameter names. All unmatched parameters are handed to the objective function objfun(). The log file starts with a table telling which parameter was assigend to which function. |

By running multiple fits starting at randomly chosen inital
parameters, the chisquare landscape can be explored using a deterministic
optimizer. Here, `trust`

is used for optimization. The standard
procedure to obtain random initial values is to sample random variables
from a uniform distribution (`rnorm`

) and adding these to
center. It is, however, possible, to employ any other sampling
strategy by handing the respective function to mstrust(),
samplefun.

In case a special sampling is required, a customized sampling function can be used. If, e.g., inital values leading to a non-physical systems are to be discarded upfront, the objective function can be addapted accordingly.

All started fits either lead to an error or complete converged or unconverged. A statistics about the return status of fits can be shown by setting stats to TRUE.

Fit final and intermediat results are stored under studyname. For
each run of mstrust for the same study name, a folder under
studyname of the form "trial-x-date" is created. "x" is the number
of the trial, date is the current time stamp. In this folder, the
intermediate results are stored. These intermediate results can be loaded
by `load.parlist`

. These are removed on successfull completion
of mstrust. In this case, the final list of fit parameters
(parameterList.Rda) and the fit log (mstrust.log) are found instead.

A parlist holding errored and converged fits.

Wolfgang Mader, Wolfgang.Mader@fdm.uni-freiburg.de

1. `trust`

, for the used optimizer,
2. `rnorm`

, `runif`

for two common sampling functions,
3. `msParframe`

for passing a reproducible set of random initial
guesses to mstrust,
4. `as.parframe`

for formatting the output to a handy table

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