This function runs a non-parametric phase model on 14C and non-14C ages via Gaussian Mixture density estimation
BchronDensity(
ages,
ageSds,
calCurves,
pathToCalCurves = system.file("data", package = "Bchron"),
dfs = rep(100, length(ages)),
numMix = 50,
iterations = 10000,
burn = 2000,
thin = 8,
updateAges = FALSE,
store_density = TRUE
)Arguments
- ages
A vector of ages (most likely 14C)
- ageSds
A vector of 1-sigma values for the ages given above
- calCurves
A vector of values containing either
intcal20,shcal20,marine20, ornormal(older calibration curves such as intcal13 are also supported). 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.- pathToCalCurves
File path to where the calibration curves are located. Defaults to the system directory where the 3 standard calibration curves are stored
- dfs
Degrees-of-freedom values for the t-distribution associated with the calibration calculation. A large value indicates Gaussian distributions assumed for the 14C ages
- numMix
The number of mixture components in the phase model. Might need to be increased if the data set is large and the phase behaviour is very complex
- iterations
The number of iterations to run for
- burn
The number of starting iterations to discard
- thin
The step size of iterations to keep
- updateAges
Whether or not to update ages as part of the MCMC run. Default is FALSE. Changing this to TRUE will improve performance but will fit a slightly invalid model
- store_density
Whether or not to store the density and age grid. Useful for plotting the output in other packages
Value
An object of class BchronDensityRun with the following elements:
- theta
The posterior samples of the restricted ages
- p
Posterior samples of the mixture proportions
- mu
Values of the means of each Gaussian mixture
- calAges
The calibrated ages from
BchronCalibrate- G
The number of mixture components. Equal to numMix
- age_grid
A grid of ages used for the final density estimate
- density
The density estimate based on the above age grid
Details
This model places a Gaussian mixture prior distribution on the calibrated ages and so estimates the density of the overall set of radiocarbon ages. It is designed to be a probabilistic version of the Oxcal SUM command which takes calibrated ages and sums the probability distributions with the aim of estimating activity through age as a proxy.
See also
Bchronology, BchronRSL, BchronDensityFast for a faster approximate version of this function
Examples
# \donttest{
# Read in some data from Sluggan Moss
data(Sluggan)
# Run the model
SlugDens <- with(
Sluggan,
BchronDensity(
ages = ages,
ageSds = ageSds,
calCurves = calCurves
)
)
#>
|
| | 0%
|
| | 1%
|
|= | 1%
|
|= | 2%
|
|== | 3%
|
|== | 4%
|
|=== | 4%
|
|=== | 5%
|
|=== | 6%
|
|==== | 6%
|
|==== | 7%
|
|===== | 8%
|
|===== | 9%
|
|====== | 9%
|
|====== | 10%
|
|====== | 11%
|
|======= | 11%
|
|======= | 12%
|
|======== | 13%
|
|======== | 14%
|
|========= | 14%
|
|========= | 15%
|
|========= | 16%
|
|========== | 16%
|
|========== | 17%
|
|=========== | 18%
|
|=========== | 19%
|
|============ | 19%
|
|============ | 20%
|
|============ | 21%
|
|============= | 21%
|
|============= | 22%
|
|============== | 23%
|
|============== | 24%
|
|=============== | 24%
|
|=============== | 25%
|
|=============== | 26%
|
|================ | 26%
|
|================ | 27%
|
|================= | 28%
|
|================= | 29%
|
|================== | 29%
|
|================== | 30%
|
|================== | 31%
|
|=================== | 31%
|
|=================== | 32%
|
|==================== | 33%
|
|==================== | 34%
|
|===================== | 34%
|
|===================== | 35%
|
|===================== | 36%
|
|====================== | 36%
|
|====================== | 37%
|
|======================= | 38%
|
|======================= | 39%
|
|======================== | 39%
|
|======================== | 40%
|
|======================== | 41%
|
|========================= | 41%
|
|========================= | 42%
|
|========================== | 43%
|
|========================== | 44%
|
|=========================== | 44%
|
|=========================== | 45%
|
|=========================== | 46%
|
|============================ | 46%
|
|============================ | 47%
|
|============================= | 48%
|
|============================= | 49%
|
|============================== | 49%
|
|============================== | 50%
|
|============================== | 51%
|
|=============================== | 51%
|
|=============================== | 52%
|
|================================ | 53%
|
|================================ | 54%
|
|================================= | 54%
|
|================================= | 55%
|
|================================= | 56%
|
|================================== | 56%
|
|================================== | 57%
|
|=================================== | 58%
|
|=================================== | 59%
|
|==================================== | 59%
|
|==================================== | 60%
|
|==================================== | 61%
|
|===================================== | 61%
|
|===================================== | 62%
|
|====================================== | 63%
|
|====================================== | 64%
|
|======================================= | 64%
|
|======================================= | 65%
|
|======================================= | 66%
|
|======================================== | 66%
|
|======================================== | 67%
|
|========================================= | 68%
|
|========================================= | 69%
|
|========================================== | 69%
|
|========================================== | 70%
|
|========================================== | 71%
|
|=========================================== | 71%
|
|=========================================== | 72%
|
|============================================ | 73%
|
|============================================ | 74%
|
|============================================= | 74%
|
|============================================= | 75%
|
|============================================= | 76%
|
|============================================== | 76%
|
|============================================== | 77%
|
|=============================================== | 78%
|
|=============================================== | 79%
|
|================================================ | 79%
|
|================================================ | 80%
|
|================================================ | 81%
|
|================================================= | 81%
|
|================================================= | 82%
|
|================================================== | 83%
|
|================================================== | 84%
|
|=================================================== | 84%
|
|=================================================== | 85%
|
|=================================================== | 86%
|
|==================================================== | 86%
|
|==================================================== | 87%
|
|===================================================== | 88%
|
|===================================================== | 89%
|
|====================================================== | 89%
|
|====================================================== | 90%
|
|====================================================== | 91%
|
|======================================================= | 91%
|
|======================================================= | 92%
|
|======================================================== | 93%
|
|======================================================== | 94%
|
|========================================================= | 94%
|
|========================================================= | 95%
|
|========================================================= | 96%
|
|========================================================== | 96%
|
|========================================================== | 97%
|
|=========================================================== | 98%
|
|=========================================================== | 99%
|
|============================================================| 99%
|
|============================================================| 100%
# plot it
plot(SlugDens)
# }