Marginal Summary Statistics for Multiple MCMC Chains

Calculates summary statistics of the output of the MCMC algorithm for multiple parameters. Results are given in calendar years (BC/AD).

Usage

# S4 method for class 'MCMC'
summary(object, level = 0.95, calendar = get_calendar())

# S4 method for class 'PhasesMCMC'
summary(object, level = 0.95, calendar = get_calendar())

Arguments

object

An MCMC or a PhasesMCMC object.

level

A length-one numeric vector giving the confidence level.

calendar

A aion::TimeScale object specifying the target calendar (see aion::calendar()).

Value

A data.frame where the rows correspond to the chains of interest and columns to the following statistics:

mean

The mean of the MCMC chain.

sd

The standard deviation of the MCMC chain.

min

Minimum value of the MCMC chain.

q1

First quantile of the MCMC chain.

median

Median of the MCMC chain.

q3

Third quantile of the MCMC chain.

max

Maximum value of the MCMC chain.

lower

Lower boundary of the credible interval of the MCMC chain at level.

upper

Upper boundary of the credible interval of the MCMC chain at level.

See also

Other statistics: interval_credible(), interval_hdr(), sensitivity()

Author

A. Philippe, M.-A. Vibet, T. S. Dye, N. Frerebeau

Examples

## Coerce to MCMC
eve <- as_events(mcmc_events, calendar = CE(), iteration = 1)

## Summary
summary(eve, calendar = CE())
#>      mad  mean  sd   min    q1 median    q3  max start   end
#> E1  -900  -638 272 -1349  -889   -658  -386   -5 -1046  -202
#> E2 -1766 -1785 100 -2000 -1857  -1785 -1719 -971 -1981 -1611
#> E3  -702  -656  92 -1229  -717   -672  -611  -67  -803  -450
#> E4 -1241 -1236  87 -1864 -1289  -1235 -1181 -719 -1401 -1064
summary(eve, calendar = BP())
#>     mad mean   sd  min   q1 median   q3  max start  end
#> E1 2850 2588 1680 3299 2839   2608 2338 1957  2998 2154
#> E2 3716 3737 1852 3950 3807   3735 3671 2923  3933 3561
#> E3 2652 2608 1860 3181 2667   2624 2561 2017  2753 2400
#> E4 3191 3186 1863 3814 3239   3187 3131 2669  3351 3016

## Plot events
plot(eve, calendar = CE(), interval = "credible", level = 0.68)

plot(eve, calendar = BP(), interval = "hdr", level = 0.68)

plot(eve[, 1], interval = "hdr")


## Compute phases
pha <- phases(eve, groups = list(B = c(2, 4), A = c(1, 3)))

## Summary
summary(pha, calendar = CE())
#> $B
#>            mad  mean  sd   min    q1 median    q3   max start   end
#> start    -1766 -1785 100 -2000 -1857  -1785 -1719 -1223 -1981 -1611
#> end      -1240 -1235  87 -1833 -1289  -1235 -1181  -719 -1404 -1067
#> duration   561   551 132     5   464    552   639  1157   297   806
#> 
#> $A
#>           mad mean  sd   min   q1 median   q3  max start  end
#> start    -708 -773 148 -1349 -890   -749 -671 -207 -1059 -501
#> end      -690 -521 169 -1050 -670   -537 -384   -5  -776 -214
#> duration  278  253 138     1  151    249  345  880     1  487
#> 
summary(pha, calendar = BP())
#> $B
#>           mad mean   sd  min   q1 median   q3  max start  end
#> start    3718 3737 1852 3950 3807   3735 3671 3173  3933 3561
#> end      3192 3187 1863 3783 3239   3187 3131 2669  3354 3019
#> duration 1391 1401 1820 1945 1488   1398 1311  793  1653 1146
#> 
#> $A
#>           mad mean   sd  min   q1 median   q3  max start  end
#> start    2660 2723 1802 3299 2840   2701 2623 2157  3011 2453
#> end      2640 2473 1783 3000 2622   2487 2334 1957  2726 2166
#> duration 1674 1699 1814 1949 1801   1703 1607 1072  1949 1465
#> 

## Plot phases
plot(pha, calendar = BP())

plot(pha, succession = "hiatus")

plot(pha, succession = "transition")