Plot
Usage
multiplot(...)
# S4 method for MCMC
autoplot(
object,
...,
select = NULL,
groups = NULL,
density = TRUE,
interval = NULL,
level = 0.95,
decreasing = TRUE
)
# S4 method for MCMC,missing
plot(
x,
select = NULL,
groups = NULL,
density = TRUE,
interval = NULL,
level = 0.95,
decreasing = TRUE,
...
)
# S4 method for PhasesMCMC
autoplot(
object,
...,
select = NULL,
level = 0.95,
range = NULL,
decreasing = TRUE,
facet = TRUE
)
# S4 method for PhasesMCMC,missing
plot(
x,
select = NULL,
level = 0.95,
range = NULL,
decreasing = TRUE,
facet = TRUE,
...
)
Arguments
- ...
Extra parameters to be passed to
stats::density()
.- object, x
An
MCMC
or aPhasesMCMC
object.- select
An
integer
vector specifying the index of the MCMC samples to be drawn.- groups
A
character
vector used for mapping colours.- density
A
logical
scalar: should estimated density be plotted?- interval
A
character
string specifying the confidence interval to be drawn. It must be one of "credible
" (credible interval) or "hpdi
" (highest posterior density interval). Any unambiguous substring can be given. IfNULL
(the default) no interval is computed.- level
A length-one
numeric
vector giving the confidence level.- decreasing
A
logical
scalar: should the sort order be decreasing?- range
A
character
string specifying the additional time range to be displayed. It must be one of "hiatus
" or "transition
". IfNULL
(the default), no additional time ranges are displayed.- facet
A
logical
scalar: should a matrix of panels defined by phase be drawn?
Value
autoplot()
returns aggplot
object.plot()
is called it for its side-effects: it results in a graphic being displayed (invisibly returnsx
).
Methods (by class)
plot(x = MCMC, y = missing)
: Plots of credible intervals or HPD regions of a series of events.plot(x = PhasesMCMC, y = missing)
: Plots the characteristics of a group of events (phase).
Examples
## Coerce to MCMC
eve <- as_events(events, calendar = "CE", iteration = 1)
## BP
eve_BP <- CE_to_BP(eve)
summary(eve_BP)
#> mad mean sd min q1 median q3 max lower upper
#> E1 2849 2587 271 1954 2335 2607 2838 3298 2995 2151
#> E2 3715 3734 99 2920 3668 3734 3806 3949 3930 3560
#> E3 2651 2605 91 2016 2560 2621 2666 3178 2752 2399
#> E4 3190 3185 86 2668 3130 3184 3238 3813 3350 3013
## CE
eve_CE <- BP_to_CE(eve_BP)
summary(eve_CE)
#> mad mean sd min q1 median q3 max lower upper
#> E1 -900 -638 271 -1349 -889 -658 -386 -5 -1046 -202
#> E2 -1766 -1785 99 -2000 -1857 -1785 -1719 -971 -1981 -1611
#> E3 -702 -656 91 -1229 -717 -672 -611 -67 -803 -450
#> E4 -1241 -1236 86 -1864 -1289 -1235 -1181 -719 -1401 -1064
## Plot events
plot(eve_CE, interval = "credible", level = 0.68)
#> Picking joint bandwidth of 15.1
plot(eve_BP, interval = "hpdi", level = 0.68)
#> Picking joint bandwidth of 15.1
## Compute phases
pha <- phase(eve, groups = list(B = c(2, 4), A = c(1, 3)))
summary(pha)
#> $B
#> mad mean sd min q1 median q3 max lower upper
#> start -1766 -1785 99 -2000 -1857 -1785 -1719 -1223 -1981 -1611
#> end -1240 -1235 86 -1833 -1289 -1235 -1181 -719 -1404 -1067
#> duration 560 550 131 4 463 551 638 1156 296 805
#>
#> $A
#> mad mean sd min q1 median q3 max lower upper
#> start -708 -773 147 -1349 -890 -749 -671 -207 -1059 -501
#> end -690 -521 168 -1050 -670 -537 -384 -5 -776 -214
#> duration 277 252 137 0 150 248 344 879 0 486
#>
## Plot phases
plot(pha)
plot(pha, range = "hiatus")
plot(pha, range = "transition")