Find the influence of dates in a pair of Bchronology runs across the core
Source:R/coreInfluence.BchronologyRun.R
coreInfluence.RdThis function takes as input two Bchronology runs and compares the uncertainty intervals. It does this by
computing the mean uncertainty across the core (type = 'mean') at a specified percentile level (e.g. 95%) and
subsequently reporting the reduction/increase in uncertainty between the two runs. Both cores must
have the same set of depths/positions at regular intervals.
coreInfluence(
bchrRun1,
bchrRun2,
percentile = 0.95,
type = c("plot", "summary", "max"),
ageTolerance = 500,
...
)Arguments
- bchrRun1
The output of a run of the
Bchronologyfunction- bchrRun2
The output of another run of the
Bchronologyfunction, possibly with different dates. Note this must have the same value ofpredictPositionsasbchrRun1- percentile
The value of the percentile to compare the uncertainties. Default is 95%
- type
if
plotwill return a plot of the difference in uncertainties at the specified percentile level. Ifsummarywill return text output of the reduction in uncertainty at each position. Ifmaxwill return the position of the maximum decrease in uncertainty and a list of all the positions where the reduction in uncertainty exceeds the value ofageTolerance- ageTolerance
A value in years for which to report the positions at which the reduction in uncertainty exceeds this value.
- ...
Additional arguments to plot
Value
Depending on type will outputs some text and plots providing the influence values for the cores in question.
Details
For example, if the ageTolerance value is 500 years, then coreInfluence will return all of the positions at
which the uncertainty reduction is bigger than 500.
See also
Bchronology, choosePositions, dateInfluence for finding the influence of removing a single date from a core
Examples
# \donttest{
data(Glendalough)
# Start with a run that remove two dates
GlenOut1 <- Bchronology(
ages = Glendalough$ages[-c(3:4)],
ageSds = Glendalough$ageSds[-c(3:4)],
calCurves = Glendalough$calCurves[-c(3:4)],
positions = Glendalough$position[-c(3:4)],
positionThicknesses = Glendalough$thickness[-c(3:4)],
ids = Glendalough$id[-c(3:4)],
predictPositions = seq(0, 1500, by = 10)
)
#> Running Bchronology...
#>
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#> Run completed!
GlenOut2 <- Bchronology(
ages = Glendalough$ages,
ageSds = Glendalough$ageSds,
calCurves = Glendalough$calCurves,
positions = Glendalough$position,
positionThicknesses = Glendalough$thickness,
ids = Glendalough$id,
predictPositions = seq(0, 1500, by = 10)
)
#> Running Bchronology...
#>
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#> Run completed!
# Now compare their influence
coreInfluence(GlenOut1,
GlenOut2,
type = c("max", "plot"),
xlab = "Age (cal years BP)",
ylab = "Depth (cm)",
main = "Chronology difference at 95% for
Glendalough removing two dates",
las = 1
)
#> Position of maximum age uncertainty change is: 1170
#> with age uncertainty reduction of 5211.45 years
#>
#> Positions with age uncertainty change above 500 years are:
#> 440 450 460 470 480 490 500 510 520 530 540 550 560 570 580 590 600 610 620 630 640 650 660 670 680 690 700 710 720 730 740 750 760 770 780 790 800 810 820 830 840 850 860 870 880 890 900 910 920 930 940 950 960 970 980 990 1000 1010 1020 1030 1040 1050 1060 1070 1080 1090 1100 1110 1120 1130 1140 1150 1160 1170 1180 1190 1200 1210 1220 1230 1240 1250 1260 1270 1280 1290 1300 1310 1320 1330 1340 1350 1360 1370
# }