summary.abc: Summaries of posterior samples generated by ABC algortithms

In abc: Tools for Approximate Bayesian Computation (ABC)

Summaries of posterior samples generated by ABC algortithms

Description

Calculates simple summaries of posterior samples: the minimum and maximum, the weighted mean, median, mode, and credible intervals.

Usage

## S3 method for class 'abc'
summary(object, unadj = FALSE, intvl = .95, print = TRUE,
digits = max(3, getOption("digits")-3), ...)

Arguments

object	an object of class "abc".
unadj	logical, if TRUE it forces to plot the unadjusted values when method is "loclinear" or "neuralnet".
intvl	size of the symmetric credible interval.
print	logical, if TRUE prints messages. Mainly for internal use.
digits	the digits to be rounded to. Can be a vector of the same length as the number of parameters, when each parameter is rounded to its corresponding digits.
...	other arguments passed to density.

Details

If method is "rejection" in the original call to abc, posterior means, medians, modes and percentiles defined by intvl, 95 by default (credible intervals) are calculated. If a regression correction was used (i.e. method is "loclinear" or "neuralnet" in the original call to abc) the weighted posterior means, medians, modes and percentiles are calculated.

To calculate the mode, parameters are passed on from density.default. Note that the posterior mode can be rather different depending on the parameters to estimate the density.

Value

The returned value is an object of class "table". The rows are,

Min.	minimun
Lower perc.	lower percentile
Median	or weighted median
Mean	or weighted mean
Mode	or weighted mode
Upper perc.	upper percentile
Max.	maximum

See Also

abc, hist.abc, plot.abc

Examples

## see ?abc for examples

abc documentation built on May 20, 2022, 1:11 a.m.