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 -3.000 0.00 0.0 1.00 1.000
#> 10 11.000 35.00 62.0 149.00 549.050
#> 20 21.975 71.75 123.0 258.00 824.075
#> 30 33.950 106.00 184.5 352.25 967.450
#> 40 45.000 143.50 245.5 431.25 1056.000
#> 50 57.975 181.75 304.0 508.25 1199.025
#> 60 70.000 219.00 357.0 592.25 1287.125
#> 70 81.975 255.00 402.0 657.50 1341.725
#> 80 98.875 290.75 455.5 716.25 1416.325
#> 90 115.950 332.00 507.5 777.25 1463.025
#> 100 132.975 378.75 564.5 841.25 1531.375
#> 110 150.975 416.00 621.5 891.00 1577.125
#> 120 167.975 455.00 674.0 947.25 1597.025
#> 130 188.000 498.00 731.5 999.50 1629.250
#> 140 206.000 553.50 784.5 1066.25 1683.300
#> 150 220.975 605.75 832.0 1118.25 1722.075
#> 160 241.900 653.00 882.5 1172.50 1789.075
#> 170 266.975 700.00 935.0 1224.00 1826.100
#> 180 305.700 748.00 986.5 1266.75 1853.100
#> 190 333.425 784.00 1038.5 1306.00 1881.225
#> 200 353.000 831.50 1087.5 1370.00 1934.050
#> 210 372.900 875.75 1128.0 1440.00 1966.025
#> 220 402.725 921.00 1184.0 1493.75 1990.225
#> 230 445.525 965.50 1234.5 1539.75 2017.025
#> 240 486.825 1012.25 1287.0 1578.25 2038.050
#> 250 527.425 1065.00 1334.5 1626.25 2061.025
#> 260 555.950 1120.00 1391.0 1666.25 2087.175
#> 270 589.950 1159.25 1449.5 1703.25 2117.000
#> 280 619.775 1206.25 1505.0 1759.25 2139.275
#> 290 668.775 1260.00 1563.5 1804.50 2158.875
#> 300 691.775 1318.75 1621.5 1858.00 2215.150
#> 310 720.925 1373.75 1674.0 1900.00 2233.050
#> 320 765.775 1431.75 1725.5 1938.75 2254.100
#> 330 809.875 1479.00 1783.0 1981.25 2270.025
#> 340 882.775 1538.75 1828.5 2023.00 2285.025
#> 350 957.825 1618.75 1881.0 2063.00 2303.150
#> 360 1032.625 1682.50 1935.0 2103.00 2345.025
#> 370 1114.575 1751.75 1985.5 2136.00 2374.225
#> 380 1206.825 1814.00 2036.0 2169.00 2418.100
#> 390 1255.900 1893.00 2091.0 2204.00 2456.275
#> 400 1375.950 1982.50 2141.5 2248.25 2479.450
#> 410 1546.475 2080.00 2194.5 2290.00 2531.125
#> 420 1825.925 2168.75 2269.0 2332.00 2595.000
#> 430 2189.950 2324.00 2398.5 2514.00 3163.675
#> 440 2240.925 2410.00 2536.5 2761.25 3765.250
#> 450 2284.000 2485.50 2675.5 2965.50 4210.325
#> 460 2329.000 2552.00 2786.0 3140.25 4507.675
#> 470 2373.875 2620.75 2896.5 3321.25 4705.050
#> 480 2395.000 2707.75 3010.5 3502.00 4913.375
#> 490 2433.975 2782.75 3107.0 3641.50 5107.050
#> 500 2469.925 2847.50 3209.5 3797.50 5290.400
#> 510 2499.825 2917.25 3322.5 3955.75 5507.850
#> 520 2548.000 2984.50 3420.5 4071.00 5743.125
#> 530 2609.600 3062.75 3537.5 4175.00 5968.100
#> 540 2637.825 3149.00 3658.5 4281.50 6088.200
#> 550 2659.925 3249.25 3768.5 4395.25 6270.100
#> 560 2704.875 3314.75 3861.0 4527.75 6513.750
#> 570 2739.850 3399.25 3966.5 4645.50 6649.425
#> 580 2770.800 3467.75 4060.0 4752.25 6918.525
#> 590 2804.850 3563.00 4168.5 4865.75 7032.125
#> 600 2827.975 3658.00 4279.0 4993.00 7151.050
#> 610 2860.825 3737.00 4381.5 5105.50 7227.025
#> 620 2882.000 3838.75 4488.0 5211.00 7298.225
#> 630 2909.950 3930.50 4582.5 5333.25 7443.675
#> 640 2939.950 4014.50 4679.0 5473.00 7593.375
#> 650 2965.800 4116.50 4767.5 5608.50 7689.225
#> 660 2994.975 4210.75 4865.0 5697.75 7786.775
#> 670 3021.975 4309.25 4978.0 5801.50 7932.650
#> 680 3047.975 4399.25 5093.5 5889.25 8005.000
#> 690 3095.775 4496.50 5208.5 6010.75 8052.975
#> 700 3127.800 4587.75 5289.5 6121.50 8124.300
#> 710 3184.025 4673.75 5403.5 6196.00 8158.175
#> 720 3254.525 4766.00 5500.5 6299.75 8228.050
#> 730 3335.825 4864.00 5612.5 6406.25 8259.750
#> 740 3371.750 4945.50 5744.5 6502.75 8397.825
#> 750 3394.850 5006.25 5829.0 6606.50 8522.075
#> 760 3435.900 5115.00 5945.5 6745.25 8599.000
#> 770 3471.750 5199.25 6044.0 6890.50 8634.050
#> 780 3508.700 5306.50 6149.0 6979.00 8776.500
#> 790 3602.100 5401.75 6244.0 7087.75 8913.225
#> 800 3718.500 5504.25 6336.5 7180.75 8950.175
#> 810 3792.475 5602.75 6454.5 7280.75 8988.700
#> 820 3863.125 5693.75 6564.0 7394.25 9040.350
#> 830 3892.275 5839.50 6666.0 7506.00 9091.325
#> 840 3939.600 5969.25 6766.5 7583.50 9129.425
#> 850 4023.900 6061.50 6859.5 7672.50 9166.325
#> 860 4111.175 6166.75 6973.5 7762.50 9207.100
#> 870 4158.925 6260.75 7103.5 7874.00 9244.350
#> 880 4281.400 6362.00 7205.5 7972.50 9360.025
#> 890 4335.725 6456.25 7323.0 8055.25 9404.225
#> 900 4398.800 6572.25 7424.0 8144.00 9439.350
#> 910 4555.175 6712.00 7524.0 8229.00 9479.175
#> 920 4613.850 6822.00 7647.0 8343.25 9528.475
#> 930 4664.250 6922.50 7757.0 8440.25 9565.425
#> 940 4734.600 7061.50 7861.0 8548.25 9631.075
#> 950 4823.475 7154.00 7961.5 8636.75 9726.625
#> 960 4943.625 7294.75 8077.0 8726.25 9785.075
#> 970 5147.775 7395.00 8190.5 8785.75 9809.325
#> 980 5297.600 7495.00 8294.0 8880.50 9825.775
#> 990 5479.275 7615.50 8390.5 8951.50 9868.425
#> 1000 5520.800 7758.75 8487.5 9040.75 9910.075
#> 1010 5619.500 7851.75 8586.5 9126.25 9936.050
#> 1020 5748.725 7962.75 8691.5 9214.00 9960.250
#> 1030 5823.325 8083.50 8797.0 9304.25 10000.075
#> 1040 5998.400 8221.00 8905.5 9388.00 10027.475
#> 1050 6264.500 8354.25 9011.0 9478.50 10073.025
#> 1060 6489.700 8469.50 9107.0 9560.50 10104.125
#> 1070 6600.925 8579.25 9196.0 9644.75 10129.200
#> 1080 6747.025 8706.75 9302.0 9718.25 10166.025
#> 1090 6856.050 8847.25 9417.5 9798.25 10194.100
#> 1100 7152.025 8998.75 9529.0 9874.25 10215.175
#> 1110 7360.625 9137.75 9638.5 9952.00 10235.125
#> 1120 7539.175 9278.50 9751.5 10022.25 10263.050
#> 1130 7932.675 9447.75 9865.5 10093.25 10296.025
#> 1140 8187.950 9635.00 9984.5 10159.50 10326.150
#> 1150 8785.250 9848.75 10108.0 10217.00 10362.075
#> 1160 9369.750 10123.50 10226.5 10299.00 10430.075
#> 1170 10290.000 10374.00 10427.0 10491.00 10787.025
#> 1180 10416.950 10610.00 10659.5 10716.00 11010.050
#> 1190 10527.800 10847.75 10897.0 10943.25 11121.000
#> 1200 10734.950 11084.75 11127.0 11167.25 11261.025
#> 1210 11167.975 11251.00 11293.0 11370.00 11777.025
#> 1220 11220.925 11322.00 11387.0 11528.00 12036.075
#> 1230 11248.875 11385.50 11477.0 11660.00 12170.400
#> 1240 11277.950 11457.75 11569.0 11758.50 12274.050
#> 1250 11299.000 11528.75 11655.5 11876.00 12374.075
#> 1260 11334.975 11597.75 11742.0 11974.25 12452.100
#> 1270 11360.975 11659.00 11834.0 12064.50 12512.275
#> 1280 11399.650 11733.50 11927.0 12172.25 12567.000
#> 1290 11443.975 11811.75 12010.0 12237.25 12602.275
#> 1300 11477.875 11893.75 12094.5 12321.00 12640.025
#> 1310 11508.950 11972.50 12184.5 12389.25 12688.225
#> 1320 11573.650 12053.00 12277.5 12468.25 12727.075
#> 1330 11610.925 12152.75 12372.0 12537.25 12756.125
#> 1340 11696.900 12251.00 12456.0 12598.25 12786.100
#> 1350 11779.950 12369.75 12544.5 12660.00 12835.000
#> 1360 11904.900 12498.25 12629.0 12716.25 12870.050
#> 1370 12084.925 12623.50 12717.0 12782.00 12922.050
#> 1380 12468.975 12752.00 12804.0 12860.00 13013.025
#> 1390 12823.000 12887.00 12938.0 13007.00 13209.000
#> 1400 12878.975 12999.00 13049.0 13113.00 13286.075
#> 1410 12934.975 13108.75 13157.5 13211.00 13348.000
#> 1420 12990.775 13213.75 13267.0 13312.00 13401.025
#> 1430 13142.975 13325.00 13372.0 13415.00 13513.075
#> 1440 13359.000 13444.75 13502.0 13630.25 14419.000
#> 1450 13378.950 13500.75 13616.0 13829.00 14883.250
#> 1460 13399.000 13556.75 13723.0 13994.50 15316.975
#> 1470 13420.975 13610.00 13821.5 14149.25 15603.925
#> 1480 13441.950 13662.00 13917.5 14281.50 15833.300
#> 1490 13463.000 13717.75 14023.0 14408.75 16029.100
#> 1500 13483.975 13774.75 14116.5 14547.50 16192.175
summary(GlenOut, type = "convergence") # Check model convergence
#> Convergence check (watch for too many small p-values):
#> p-value
#> RateMean 0.00016
#> Outlier 6 0.01238
#> Outlier 3 0.04132
#> Outlier 2 0.04726
#> RateVar 0.06189
#> Beta-100901 0.10741
#> Beta-100899 0.11573
#> Beta-100897 0.15349
#> Top-1 0.24645
#> Beta-122061 0.29706
#> Beta-100900 0.30687
#> Outlier 5 0.42540
#> Outlier 1 0.42872
#> Outlier 4 0.42872
summary(GlenOut, type = "outliers") # Look at outlier probabilities
#> Posterior outlier probability by date:
#> Date OutlierProb
#> Top-1 0.009
#> Beta-122061 0.009
#> Beta-100901 0.006
#> Beta-100900 0.009
#> Beta-100899 0.011
#> Beta-100897 0.012
# 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!
# }