# summary.abc: Summaries of posterior samples generated by ABC algortithms In abc: Tools for Approximate Bayesian Computation (ABC)

## Description

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

## Usage

 ```1 2 3``` ```## 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

`abc`, `hist.abc`, `plot.abc`

## Examples

 `1` ```## see ?abc for examples ```

### Example output

```Loading required package: abc.data

Attaching package: 'SparseM'

The following object is masked from 'package:base':

backsolve