The function cycles through all possible subsets of summary statistics and computes a criterion from the posterior sample. The subset which achieves the minimum is chosen as the most informative subset.

1 2 3 4 |

`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 |

`crit` |
a function to minimize to measure information from a posterior sample, e.g. |

`sumsubs` |
an optional index into the summary statistics to limit summary selection to a specific subset of summaries. |

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

`do.only` |
an optional index into the summary statistics combination table. Can be used to limit entropy calculations to certain summary statistics subsets only. |

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

`do.crit` |
a boolean value indicating whether the measure on the posterior sample should be returned. |

`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. |

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

The function uses a criterion (e.g.sample entropy) as a proxy for information in a posterior sample. The criterion for each possible subset of statistics is computed, and the best subset is judged as the one which minimises this vector of values.

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. |

These functions are computationally intensive due to the cyclic ABC inference procedure.

Matt Nunes

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.

`nn.ent`

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 | ```
# 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)
obsparam<-matrix(coalobs[1,1])
# example of entropy minimization algorithm:
tmp <-mincrit(obsstats, param, simstats, tol=.01, method="rejection",
do.crit=TRUE)
tmp$critvals
``` |

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