abctools (version 0.2-2)

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=FALSE, 
limit=ncol(sumstats), allow.none=TRUE, 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. (to appear). 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