Runs the Compound Poisson-Gamma chronology model of Haslett and Parnell (2008)
Source:R/Bchronology.R
Bchronology.RdFits a non-parametric chronology model to age/position data according to the Compound Poisson-Gamma model defined by Haslett and Parnell (2008) <DOI:10.1111/j.1467-9876.2008.00623.x>. This version uses a slightly modified Markov chain Monte Carlo fitting algorithm which aims to converge quicker and requires fewer iterations. It also a slightly modified procedure for identifying outliers
Bchronology(
ages,
ageSds,
positions,
positionThicknesses = rep(0, length(ages)),
calCurves = rep("intcal20", length(ages)),
ids = NULL,
outlierProbs = rep(0.01, length(ages)),
predictPositions = seq(min(positions), max(positions), length = 100),
pathToCalCurves = system.file("data", package = "Bchron"),
artificialThickness = 0.01,
allowOutside = FALSE,
iterations = 10000,
burn = 2000,
thin = 8,
extractDate = 1950 - as.numeric(format(Sys.time(), "%Y")),
maxExtrap = 1000,
thetaStart = NULL,
thetaMhSd = 0.5,
muMhSd = 0.1,
psiMhSd = 0.1,
ageScaleVal = 1000,
positionEps = 1e-05,
positionNormalise = TRUE
)Arguments
- ages
A vector of ages provided in years before 1950.
- ageSds
A vector of 1-sigma values for the ages given above
- positions
Position values (e.g. depths) for each age. In the case of layers of non-zero thickness, this should be the middle value of the slice
- positionThicknesses
Thickness values for each of the positions. The thickness value should be the full thickness value of the slice. By default set to zero.
- calCurves
A vector of values containing either
intcal20,shcal20,marine20, ornormal(older calibration curves are supposed such as intcal13). Should be the same length the number of ages supplied. Non-standard calibration curves can be used provided they are supplied in the same format as those previously mentioned and are placed in the same directory. Normal indicates a normally-distributed (non-14C) age.- ids
ID names for each age
- outlierProbs
A vector of prior outlier probabilities, one for each age. Defaults to 0.01
- predictPositions
A vector of positions (e.g. depths) at which predicted age values are required. Defaults to a sequence of length 100 from the top position to the bottom position
- pathToCalCurves
File path to where the calibration curves are located. Defaults to the system directory where the 3 standard calibration curves are stored.
- artificialThickness
Amount to add to the thickness values in the case of equal positions with no
positionThicknesses. Bchron may fail ifpositionThicknessesare zero and some positions are repeated. This value is added on to the zero thicknesses (only in the case of repeated positions) to stop this failure.- allowOutside
Whether to allow calibrations to run outside the range of the calibration curve. By default this is turned off as calibrations outside of the range of the calibration curve can cause severe issues with probability ranges of calibrated dates
- iterations
The number of iterations to run the procedure for
- burn
The number of starting iterations to discard
- thin
The step size for every iteration to keep beyond the burn-in
- extractDate
The top age of the core. Used for extrapolation purposes so that no extrapolated ages go beyond the top age of the core. Defaults to the current year
- maxExtrap
The maximum number of extrapolations to perform before giving up and setting the predicted ages to NA. Useful for when large amounts of extrapolation are required, i.e. some of the
predictPositionsare a long way from the dated positions- thetaStart
A set of starting values for the calendar ages estimated by Bchron. If NULL uses a function to estimate the ages. These should be in the same units as the posterior ages required. See example below for usage.
- thetaMhSd
The Metropolis-Hastings standard deviation for the age parameters
- muMhSd
The Metropolis-Hastings standard deviation for the Compound Poisson-Gamma mean
- psiMhSd
The Metropolis-Hastings standard deviation for the Compound Poisson-Gamma scale
- ageScaleVal
A scale value for the ages.
Bchronologyworks best when the ages are scaled to be approximately between 0 and 100. The default value is thus 1000 for ages given in years.- positionEps
A small value used to check whether simulated positions are far enough apart to avoid numerical underflow errors. If errors occur in model runs (e.g.
missing value where TRUE/FALSE neededincrease this value)- positionNormalise
Whether to normalise the position values.
Bchronologyworks best when the positions are normalised to be between 0 and 1 The default value isTRUE
Value
A list of class BchronologyRun which include elements:
- theta
The posterior estimated values of the ages
- phi
The posterior estimated outlier values (1=outlier, 2=not outlier). The means of this parameter give the posterior estimated outlier probabilities
- mu
The posterior values of the Compound Poisson-Gamma mean
- psi
The posterior values of the Compound Poisson-Gamma scale
- thetaPredict
The posterior estimated ages for each of the values in predictPosition
- predictPositions
The positions at which estimated ages were required
- calAges
The calibrated ages as output from
BchronCalibrate- inputVals
All of the input values to the
Bchronologyrun
Details
The Bchronology function fits a compound Poisson-Gamma distribution to the increments between the dated levels. This involves a stochastic linear interpolation step where the age gaps are Gamma distributed, and the position gaps are Exponential. Radiocarbon and non-radiocarbon dates (including outliers) are updated within the function also by MCMC.
References
Haslett, J., and Parnell, A. C. (2008). A simple monotone process with application to radiocarbon-dated depth chronologies. Journal of the Royal Statistical Society, Series C, 57, 399-418. DOI:10.1111/j.1467-9876.2008.00623.x Parnell, A. C., Haslett, J., Allen, J. R. M., Buck, C. E., and Huntley, B. (2008). A flexible approach to assessing synchroneity of past events using Bayesian reconstructions of sedimentation history. Quaternary Science Reviews, 27(19-20), 1872-1885. DOI:10.1016/j.quascirev.2008.07.009
See also
Examples
# \donttest{
# Data from Glendalough
data(Glendalough)
# Run in Bchronology - all but first age uses intcal20
GlenOut <- with(
Glendalough,
Bchronology(
ages = ages,
ageSds = ageSds,
calCurves = calCurves,
positions = position,
positionThicknesses = thickness,
ids = id,
predictPositions = seq(0, 1500, by = 10)
)
)
#> Running Bchronology...
#>
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#> Run completed!
# Summarise it a few different ways
summary(GlenOut) # Default is for quantiles of ages at predictPosition values
#> Quantiles of predicted ages by depth:
#> Depth 2.5% 25% 50% 75% 97.5%
#> 0 -1.000 -1.00 0.0 1.00 2.000
#> 10 11.000 35.75 55.0 118.00 516.500
#> 20 23.975 71.00 110.0 210.50 803.025
#> 30 35.000 109.00 165.0 292.25 921.025
#> 40 47.000 146.75 220.0 377.25 1087.350
#> 50 60.975 184.00 274.5 443.25 1163.175
#> 60 72.975 223.75 329.0 513.50 1233.350
#> 70 86.000 267.75 385.0 574.00 1285.725
#> 80 101.975 314.00 439.0 628.25 1338.875
#> 90 116.950 356.75 493.0 682.75 1431.025
#> 100 133.850 397.00 546.0 743.00 1465.050
#> 110 158.975 437.75 600.0 802.25 1503.200
#> 120 174.000 488.75 653.0 865.50 1595.175
#> 130 187.000 536.75 705.0 924.50 1643.075
#> 140 202.975 582.00 761.5 985.25 1663.850
#> 150 223.850 618.25 817.0 1041.25 1738.400
#> 160 237.850 657.75 871.0 1104.25 1786.000
#> 170 254.950 698.75 921.0 1162.25 1822.025
#> 180 274.975 743.75 975.0 1212.00 1847.600
#> 190 287.975 786.75 1030.0 1257.50 1876.525
#> 200 312.875 836.00 1085.0 1321.25 1911.075
#> 210 350.600 882.50 1138.0 1361.00 1973.025
#> 220 371.900 941.50 1183.0 1417.50 1991.200
#> 230 414.725 993.00 1238.0 1464.00 2008.000
#> 240 442.775 1055.75 1298.5 1511.00 2031.100
#> 250 492.800 1124.50 1347.5 1551.50 2064.050
#> 260 541.750 1171.00 1403.5 1604.25 2099.075
#> 270 586.650 1218.00 1453.0 1655.25 2135.025
#> 280 634.400 1269.75 1508.5 1698.25 2153.175
#> 290 668.975 1320.75 1563.5 1745.25 2181.025
#> 300 718.575 1372.50 1610.5 1792.75 2193.050
#> 310 747.000 1415.75 1665.5 1836.00 2210.050
#> 320 786.900 1485.00 1717.0 1877.25 2228.000
#> 330 848.750 1547.50 1765.5 1934.25 2239.075
#> 340 931.000 1603.00 1821.5 1971.00 2270.100
#> 350 1002.950 1669.75 1879.0 2016.00 2304.600
#> 360 1104.650 1729.75 1932.5 2056.25 2341.000
#> 370 1168.950 1798.00 1982.5 2102.50 2355.100
#> 380 1212.000 1857.75 2029.0 2144.00 2378.150
#> 390 1331.775 1935.00 2084.5 2187.25 2418.125
#> 400 1437.000 2010.00 2135.0 2233.00 2452.150
#> 410 1568.925 2089.00 2191.0 2278.00 2534.100
#> 420 1860.900 2166.50 2264.0 2325.50 2610.100
#> 430 2180.975 2311.50 2389.0 2494.50 3159.450
#> 440 2239.950 2396.00 2513.5 2725.25 4052.550
#> 450 2293.950 2480.00 2625.5 2940.50 4609.200
#> 460 2340.000 2560.00 2731.5 3125.25 4837.525
#> 470 2377.000 2639.00 2846.5 3316.00 5084.775
#> 480 2414.950 2720.75 2955.0 3454.25 5324.350
#> 490 2451.925 2803.50 3071.5 3575.00 5428.175
#> 500 2480.950 2891.75 3160.5 3706.00 5726.825
#> 510 2512.900 2971.75 3286.5 3868.50 5844.525
#> 520 2551.550 3047.75 3389.0 4001.50 6030.450
#> 530 2592.925 3123.75 3499.0 4126.25 6228.475
#> 540 2634.975 3212.00 3600.5 4237.25 6464.725
#> 550 2671.850 3283.50 3707.0 4391.50 6626.125
#> 560 2705.875 3367.00 3813.0 4525.50 6759.475
#> 570 2754.950 3461.00 3923.5 4642.25 6927.750
#> 580 2797.900 3536.25 4033.0 4787.25 7017.575
#> 590 2835.850 3636.50 4128.0 4899.00 7085.750
#> 600 2867.775 3705.00 4227.0 4995.25 7222.475
#> 610 2904.975 3804.00 4331.5 5093.25 7309.250
#> 620 2943.000 3913.00 4442.5 5216.00 7454.050
#> 630 2996.800 4026.00 4547.5 5317.50 7484.750
#> 640 3067.975 4106.50 4655.0 5416.75 7514.175
#> 650 3108.925 4181.75 4762.5 5521.50 7734.925
#> 660 3173.400 4284.00 4876.5 5649.75 7870.125
#> 670 3209.525 4374.00 4979.0 5785.25 7931.150
#> 680 3279.325 4472.50 5083.0 5906.75 7994.125
#> 690 3320.850 4555.00 5179.5 6011.75 8063.600
#> 700 3370.850 4644.25 5285.5 6089.75 8209.025
#> 710 3433.850 4740.50 5393.5 6208.25 8235.925
#> 720 3500.625 4827.75 5501.0 6300.75 8426.300
#> 730 3547.500 4932.25 5600.0 6364.00 8456.650
#> 740 3580.425 5030.25 5707.0 6484.75 8523.700
#> 750 3641.050 5119.75 5810.0 6607.00 8565.275
#> 760 3666.300 5213.75 5922.5 6714.25 8602.100
#> 770 3729.025 5286.25 6026.5 6814.25 8633.325
#> 780 3812.525 5369.50 6129.0 6929.25 8769.250
#> 790 3863.850 5472.00 6230.0 7057.00 8795.875
#> 800 3944.875 5565.50 6335.0 7136.25 8868.325
#> 810 3993.875 5665.50 6435.5 7233.50 8905.250
#> 820 4037.850 5754.00 6548.0 7325.25 8949.175
#> 830 4074.650 5880.00 6667.0 7428.75 9058.275
#> 840 4117.975 5995.50 6774.5 7518.75 9147.125
#> 850 4174.800 6096.50 6883.0 7624.00 9187.300
#> 860 4248.650 6214.00 6990.0 7718.25 9225.150
#> 870 4321.750 6320.75 7102.5 7804.00 9263.025
#> 880 4375.000 6452.75 7210.0 7895.25 9312.000
#> 890 4439.950 6558.75 7317.0 8000.00 9348.100
#> 900 4486.950 6652.25 7427.0 8075.25 9386.650
#> 910 4568.100 6758.75 7535.0 8184.25 9469.750
#> 920 4680.650 6878.75 7643.5 8273.50 9534.125
#> 930 4760.375 6989.00 7751.0 8357.00 9563.200
#> 940 4863.850 7098.50 7864.0 8446.00 9592.325
#> 950 4972.950 7210.25 7971.0 8542.75 9624.325
#> 960 5068.225 7345.75 8078.0 8634.00 9662.150
#> 970 5130.725 7453.25 8190.0 8730.75 9701.050
#> 980 5174.925 7585.75 8296.5 8817.25 9741.800
#> 990 5283.450 7702.75 8407.5 8898.25 9807.100
#> 1000 5336.400 7829.75 8514.0 8995.00 9841.125
#> 1010 5389.425 7977.25 8619.5 9092.75 9880.275
#> 1020 5570.800 8107.75 8719.0 9179.25 9913.075
#> 1030 5646.900 8218.75 8826.0 9233.25 9954.125
#> 1040 5775.050 8323.50 8928.0 9329.25 9984.175
#> 1050 5956.225 8458.50 9040.0 9408.75 10016.000
#> 1060 6087.400 8590.75 9140.0 9492.25 10048.175
#> 1070 6217.225 8721.75 9248.0 9570.00 10074.200
#> 1080 6419.750 8836.75 9356.5 9651.50 10124.050
#> 1090 6636.650 8964.00 9462.5 9733.50 10155.150
#> 1100 6820.550 9100.75 9563.5 9813.00 10186.150
#> 1110 7053.875 9253.25 9668.0 9891.25 10217.025
#> 1120 7400.275 9407.75 9778.5 9974.25 10259.075
#> 1130 7815.350 9564.25 9887.0 10063.25 10296.050
#> 1140 8161.575 9744.00 10001.0 10141.25 10330.050
#> 1150 8652.900 9935.75 10113.0 10214.00 10374.125
#> 1160 9227.700 10142.50 10231.5 10296.00 10430.025
#> 1170 10306.000 10376.00 10426.0 10494.25 10758.025
#> 1180 10460.775 10607.00 10656.5 10714.00 11014.150
#> 1190 10570.925 10843.75 10890.5 10936.00 11111.050
#> 1200 10801.925 11085.75 11128.0 11162.00 11250.075
#> 1210 11146.975 11250.00 11283.0 11345.25 11656.150
#> 1220 11213.975 11323.75 11371.5 11470.00 11963.325
#> 1230 11240.975 11393.75 11459.5 11595.00 12146.150
#> 1240 11271.975 11462.50 11549.0 11697.00 12220.200
#> 1250 11301.975 11524.50 11638.0 11793.25 12338.225
#> 1260 11327.925 11601.75 11726.0 11881.00 12424.200
#> 1270 11353.800 11675.75 11816.0 11976.25 12491.175
#> 1280 11371.950 11760.00 11904.0 12068.00 12556.150
#> 1290 11414.825 11849.75 11991.5 12146.50 12611.125
#> 1300 11448.975 11936.75 12083.0 12220.00 12662.000
#> 1310 11482.000 12029.50 12175.5 12306.50 12692.025
#> 1320 11545.725 12117.75 12264.0 12389.25 12714.350
#> 1330 11646.000 12218.00 12354.0 12463.25 12756.150
#> 1340 11703.000 12316.75 12443.0 12548.25 12793.050
#> 1350 11817.950 12414.50 12531.0 12623.25 12829.025
#> 1360 11945.650 12530.75 12622.0 12698.00 12873.050
#> 1370 12157.875 12643.75 12709.0 12773.50 12924.100
#> 1380 12441.525 12757.75 12806.0 12860.25 13000.050
#> 1390 12829.975 12887.00 12933.0 12996.00 13177.150
#> 1400 12899.000 12998.00 13044.0 13109.00 13295.075
#> 1410 12957.975 13110.00 13158.0 13211.00 13351.025
#> 1420 12998.950 13215.75 13265.0 13311.25 13413.025
#> 1430 13186.000 13324.00 13372.0 13416.00 13523.025
#> 1440 13348.975 13433.00 13491.5 13603.00 14259.400
#> 1450 13371.950 13483.50 13580.0 13780.50 14706.350
#> 1460 13388.000 13517.50 13656.0 13937.00 15123.125
#> 1470 13399.950 13557.75 13739.5 14083.25 15303.925
#> 1480 13407.950 13598.00 13826.5 14239.25 15495.000
#> 1490 13417.900 13647.00 13907.0 14348.00 15753.325
#> 1500 13423.000 13689.75 13989.0 14458.25 16011.125
summary(GlenOut, type = "convergence") # Check model convergence
#> Convergence check (watch for too many small p-values):
#> p-value
#> RateVar 0.00300
#> Beta-100899 0.01327
#> Outlier 5 0.02243
#> RateMean 0.03645
#> Outlier 2 0.04093
#> Outlier 4 0.04602
#> Beta-100900 0.06419
#> Outlier 1 0.07844
#> Beta-122061 0.09278
#> Outlier 3 0.10291
#> Beta-100901 0.11095
#> Top-1 0.22592
#> Beta-100897 0.34461
#> Outlier 6 0.35838
summary(GlenOut, type = "outliers") # Look at outlier probabilities
#> Posterior outlier probability by date:
#> Date OutlierProb
#> Top-1 0.005
#> Beta-122061 0.008
#> Beta-100901 0.005
#> Beta-100900 0.013
#> Beta-100899 0.008
#> Beta-100897 0.005
# Predict for some new positions
predictAges <- predict(GlenOut,
newPositions = c(150, 725, 1500),
newPositionThicknesses = c(5, 0, 20)
)
#>
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# Plot the output
plot(GlenOut) +
ggplot2::labs(
title = "Glendalough",
xlab = "Age (cal years BP)",
ylab = "Depth (cm)"
)
# If you need to specify your own starting values
startingAges <- c(0, 2000, 10000, 11000, 13000, 13500)
GlenOut <- with(
Glendalough,
Bchronology(
ages = ages,
ageSds = ageSds,
calCurves = calCurves,
positions = position,
positionThicknesses = thickness,
ids = id,
predictPositions = seq(0, 1500, by = 10),
thetaStart = startingAges
)
)
#> Running Bchronology...
#>
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#> Run completed!
# }