mc.ci {abctools} | R Documentation |

## Diagnostic plots for model choice coverage output

### Description

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.

### Usage

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

### Arguments

`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 |

### Details

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.

### Author(s)

Dennis Prangle

### 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

`cov.mc`

to produce the input for this function

### Examples

```
##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)
```

*abctools*version 1.1.7 Index]