abctools (version 1.0.4)

cov.pi: Coverage property diagnostics

Description

These functions produce diagnostic statistics for an ABC analysis to judge when the tolerance level is small enough to produce roughly no approximation error. This is done by running analyses for many pseudo-observed test data sets and assessing whether the results satisfy the "coverage property" (roughly speaking: credible intervals have the claimed coverage levels).

Usage

cov.pi(param, sumstat, testsets, tol, eps, diagnostics = c(), 
multicore = FALSE, cores, method = "rejection", nacc.min=20, ...)
cov.mc(index, sumstat, testsets, tol, eps, diagnostics = c(), 
multicore = FALSE, cores, method = "rejection", nacc.min=20, ...)
covstats.pi(raw, diagnostics = c("KS", "CGR"), nacc.min = 20)
covstats.mc(raw, index, diagnostics = c("freq", "loglik.binary", 
"loglik.multi"), nacc.min = 20)

Arguments

param

A data frame of parameter values. It must have the same number of rows as sumstat and contain numeric values only.

index

A vector of model indices. Any value which can be converted to factor is ok (e.g. character or numeric entries). It must have the same length as nrow(sumstat).

sumstat

A data frame of summary statistic values whose the ith row has been simulated using param[i,] or index[i].

testsets

A numeric vector giving the rows of sumstat to be used as pseudo-observed data to test the coverage property.

tol

A vector of proportions of ABC acceptances which will be investigated.

eps

A vector of ABC thresholds which will be investigated. These are used when tol is missing. One of eps and tol must be supplied.

diagnostics

A character vector containing diagnostics to be calculated. Allowable values for parameter inference are "KL" (Kullback-Leibler based test) or "CGR" (Cook, Gelman and Rubin test). Allowable values for model choice are "freq" (a separate frequency-based test for each model), "loglik.binary" (a separate log-likelihood based test for each model) or "loglik.multi" (single log-likelihood based test). If diagnostics is empty only raw results will be returned.

multicore

Whether to use the parallel package to perform analyses of test datasets in parallel.

cores

Number of cores to use when multicore==TRUE.

method

Method used for ABC analysis. The default is "rejection". For alternatives see abc (parameter inference) or postpr (model choice).

nacc.min

Minimum number of ABC acceptances required to compute diagnostics. See Values for details of how this is used.

...

Extra arguments to be supplied to the function performing abc analysis i.e. abc (parameter inference) or postpr (model choice).

raw

Raw output component from cov.pi or cov.mc for which diagnostics are to be calculated.

Value

Output of cov.pi or cov.mc is a list of two data frames, raw and diag. The covstats.pi and covstats.mc functions just return the latter data frame.For parameter inference, raw contains estimated cdfs (referred to as p0 estimates in Prangle et al 2013) of the true parameter values for each input configuration (i.e. for every tol/eps value at every test dataset). diag is a data frame of tol/eps value, parameter name, diagnostic name and p-value. Here the p-value relates to the test statistic used as a diagnostic. It is NA if any analyses had fewer than nacc.min acceptances (Diagnostics based on a small number of acceptances can be misleading.)For model choice, raw contains estimated model weights for each input configuration, and diag is a data frame of tol/eps value, model, diagnostic name and p-value (NA under the same conditions as before.)In both cases, raw also reports the number of acceptances. Note that raw contains p0 estimates/weights of NA if regression correction is requested but there are too few acceptances to compute it.

Details

These functions are intended to be applied as follows (i) random models/parameters are generated, data sets simulated for each and summary statistics calculated (ii) these are input to cov.pi (parameter inference) or cov.mc (model choice) which outputs raw results and diagnostics (see below) (iii) the output can be passed to covstats.pi or covstats.mc if further diagnostics are required later (or to find diagnostics for a subset of the pseudo-observed data).

The cov.pi and cov.mc functions operate by performing many ABC analyses. The user specifies which datasets amongst those simulated will used as pseudo-observed "test" data to be analysed. The results of each analysis are compared to the known model/parameters which produced the data to see whether they are consistent in a particular sense (i.e. if the coverage property is satisfied). Various diagnostics are provided to judge this easily, and determine what happens as the ABC threshold is varied. Raw results are also returned which can be investigated in more detail. All ABC analyses use a rejection sampling algorithm implemented by the abc package. The user may specify regression post-processing as part of this analysis.

References

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. Prangle D., Blum M. G. B., Popovic G., Sisson S. A. (2014) Diagnostic tools of approximate Bayesian computation using the coverage property. Australian and New Zealand Journal of Statistics 56, Issue 4, 309--329.

See Also

mc.ci for a diagnostic plot of raw model choice results abc and postpr to perform ABC for a given dataset

Examples

  ##The examples below are chosen to run relatively quickly (<5 mins)
  ##and do not represent recommended tuning choices.
  ## Not run: 
#   data(musigma2)
#   library(ggplot2)
#   ##Parameter inference example
#   parameters <- data.frame(par.sim)
#   sumstats <- data.frame(stat.sim)
#   covdiag <- cov.pi(param=parameters, sumstat=sumstats, testsets=1:100, 
#   tol=seq(0.1,1,by=0.1), diagnostics=c("KS"))
# 
#   #Plot of diagnostic results
#   qplot(x=tol, y=pvalue, facets=.~parameter, data=covdiag$diag) 
#   #Plot of raw results for tol=0.5
#   qplot(x=mu, data=subset(covdiag$raw, tol==0.5)) 
#   #Plot of raw results for tol=0.5
#   qplot(x=sigma2, data=subset(covdiag$raw, tol==0.5)) 
# 
#   #Compute CGR statistic and plot
#   cgrout <- covstats.pi(covdiag$raw, diagnostics="CGR") 
#   qplot(x=tol, y=pvalue, facets=.~parameter, data=cgrout) 
# 
#   ##Model choice example, based on simple simulated data
#   index <- sample(1:2, 1E4, replace=TRUE)
#   sumstat <- ifelse(index==1, rnorm(1E4,0,1), rnorm(1E4,0,rexp(1E4,1)))
#   sumstat <- data.frame(ss=sumstat)
#   covdiag <- cov.mc(index=index, sumstat=sumstat, testsets=1:100, 
#   tol=seq(0.1,1,by=0.1), diagnostics=c("freq"))
#   qplot(x=tol, y=pvalue, data=covdiag$diag)
#   llout <- covstats.mc(covdiag$raw, index=index, 
#   diagnostics="loglik.binary")
#   qplot(x=tol, y=pvalue, data=llout)
#   mc.ci(covdiag$raw, tol=0.5, modname=1, modtrue=index[1:200])
#   ## End(Not run)