mc.ci {abctools} | R Documentation |

Plots credible interval estimates for raw model choice output from
`cov.mc`

. This is used to investigate whether the coverage
property holds and validate whether diagnostic statistics are acting
as intended.

mc.ci(raw, tol, eps, modname, modtrue, nbins=5, bintype=c("interval", "quantile"), bw=FALSE, ...)

`raw` |
The |

`tol` |
The value of |

`eps` |
The value of |

`modname` |
The name of the model to test. |

`modtrue` |
Vector containing the true models generating the pseudo-observed
test data. i.e. |

`nbins` |
Number of bins to display. |

`bintype` |
How to choose the bins (see Details). |

`bw` |
Whether to produce a black and white image. Default is FALSE. Colour is used to make different bins stand out. |

`...` |
Additional plotting arguments - anything that can be used by |

This function provides a plot which can be used as an informal test of
the model choice coverage hypothesis for a particular value of
`eps`

or `tol`

and choice of model. The plot is more
flexible than the diagnostics, but not suitable as the basis of a
formal test.

For each pseudo-observed data set, the ABC probability that the model
is `modname`

is taken from `raw`

, and the true model is
taken from `modtrue`

. The probabilities are binned into
`nbins`

intervals, either of equal length or based on `nbins+1`

equally spaced empirical quantiles. The function estimates the
observed probability of `modname`

within each bin using Bayesian
inference for a binomial proportion under a uniform prior. The plot
shows the mean and 95% credible interval plotted against predicted
probabilities. Informally, the coverage property should be rejected
if predicted values are too unlikely given the observed values.

Dennis Prangle

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.

`cov.mc`

to produce the input for this function

##The examples below are chosen to run relatively quickly (<5 mins) ##and do not represent recommended tuning choices. ## Not run: 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")) mc.ci(covdiag$raw, tol=0.5, modname=1, modtrue=index[1:100]) ## End(Not run)

[Package *abctools* version 1.1.3 Index]