AS.select {abctools} | R Documentation |

This function uses approximate sufficiency to assess subsets of summary statistics for ABC inference.

AS.select(obs, param, sumstats, obspar=NULL, abcmethod=abc, grid=10, inturn=TRUE, limit=ncol(sumstats), allow.none=FALSE, do.err=FALSE, final.dens=FALSE, errfn=rsse, trace=TRUE, ...)

`obs` |
(matrix of) observed summary statistics. |

`param` |
matrix of simulated model parameter values. |

`sumstats` |
matrix of simulated summary statistics. |

`obspar` |
optional observed parameters (for use to assess simulation performance). |

`abcmethod` |
a function to perform ABC inference, e.g. the |

`grid` |
the number of bins into which to divide the posterior sample for the approximate sufficiency calculation. |

`inturn` |
a boolean value indicating whether "bad" statistics should be dropped and tested sequentially ( |

`limit` |
an optional integer indicating whether to limit summary selection to subsets of a maximum size. |

`allow.none` |
a boolean values indicating whether an empty subset of statistics is considered in the selection procedure. |

`do.err` |
a boolean value indicating whether the simulation error should be returned. Note: if |

`final.dens` |
a boolean value indicating whether the posterior sample should be returned. |

`errfn` |
an error function to assess ABC inference performance. |

`trace` |
whether to show progress messages. |

`...` |
any other optional arguments to the ABC inference procedure (e.g. arguments to the |

The summary selection procedure works by sequentially testing randomly chosen statistics for inclusion, using the ratio of ABC posterior samples to determine whether a statistic is added. Since adding a statistic may result in a suboptimal subset of summaries, the included statistics are then individually dropped and retested, to determine whether a smaller subset of statistics is equally / more informative than the accepted set of statistics.

A list with the following components:

`best` |
the final subset of included statistics. |

`err` |
simulation error (if |

`post.sample` |
an array of dimension |

The approximate sufficiency techniques described here are only suitable for single parameters only.

Matt Nunes

Blum, M. G. B, Nunes, M. A., Prangle, D. and Sisson, S. A. (2013) A
comparative review of dimension reduction methods in approximate
Bayesian computation. *Stat. Sci.* **28**, Issue 2, 189–208.

Joyce, P. and P. Marjoram (2008) Approximately sufficient statistics and
Bayesian computation. *Stat. Appl. Gen. Mol. Biol.* **7**
Article 26.

Nunes, M. A. and Prangle, D. (2016) abctools: an R package for tuning
approximate Bayesian computation analyses. *The R Journal*
**7**, Issue 2, 189–205.

# load example data: data(coal) data(coalobs) param<-coal[,2] simstats<-coal[,4:6] # use matrix below just in case to preserve dimensions. obsstats<-matrix(coalobs[1,4:6],nrow=1) # example of AS.select: ## Not run: tmp <-AS.select(obsstats, param, simstats, tol=.1, method="neuralnet", nument=5, allow.none=FALSE, inturn=TRUE) tmp$best ## End(Not run)

[Package *abctools* version 1.1.3 Index]