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

The function implements functions which implement summary statistics selection methods.

1 2 |

`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). |

`ssmethod` |
a function to perform summary statistics selection. Current methods are |

`verbose` |
a boolean value indicating whether informative statements should be printed to screen. |

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

`...` |
any other optional arguments to the summary selection procedure. |

The function is essentially a wrapper for more specific summary selection methods, and is designed to be flexible for future additions and minimization criteria. See the help files for each summary selection method for more details.

A list with the following components:

`best` |
the best subset(s) of statistics. |

`critvals` |
the calculated criterion values (if |

`err` |
simulation error (if |

`order` |
the subsets considered during the algorithm (same as the input |

`post.sample` |
an array of dimension |

`sumsubs` |
an index into the subsets considered during the algorithm. |

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.

Fearnhead, P. and Prangle, D. (2012) Constructing summary statistics for approximate Bayesian computation: semi-automatic approximate Bayesian computation. *J. R. Stat. Soc. B* **74**, Part 3, 1–28.

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

Nunes, M. A. and Balding, D. J. (2010) On Optimal Selection of Summary
Statistics for Approximate Bayesian Computation.
*Stat. Appl. Gen. Mol. Biol.* **9**, Iss. 1, Art. 34.

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.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 | ```
# 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)
tmp<-selectsumm(obsstats, param, simstats, ssmethod =AS.select, tol =.1,
method = "rejection", allow.none = FALSE, inturn = TRUE, hcorr = TRUE)
tmp$best
``` |

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