abctools (version 1.0.2)

AS.select: Summary statistics selection using approximate sufficiency.

Description

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

Usage

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,...)

Value

A list with the following components:bestthe final subset of included statistics.errsimulation error (if obspar is supplied and do.err=TRUE).post.samplean array of dimension nacc x npar x ndatasets giving the posterior sample for each observed dataset. Not returned if final.dens=FALSE.

Details

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.

References

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.

See Also

AS.test

Examples

# 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:

tmp <-AS.select(obsstats, param, simstats, tol=.1, method="neuralnet",
nument=5, allow.none=FALSE, inturn=TRUE)

tmp$best