BchronRSL {Bchron} | R Documentation |
Relative sea level rate (RSL) estimation
Description
Relative sea level rate (RSL) estimation
Usage
BchronRSL(
BchronologyRun,
RSLmean,
RSLsd,
degree = 1,
iterations = 10000,
burn = 2000,
thin = 8
)
Arguments
BchronologyRun |
Output from a run of |
RSLmean |
A vector of RSL mean estimates of the same length as the number of predictPositions given to the |
RSLsd |
A vector RSL standard deviations of the same length as the number of predictPositions given to the |
degree |
The degree of the polynomial regression: linear=1 (default), quadratic=2, etc. Supports up to degree 5, though this will depend on the data given |
iterations |
The number of MCMC iterations to run |
burn |
The number of starting iterations to discard |
thin |
The step size of iterations to discard |
Details
This function fits an errors-in-variables regression model to relative sea level (RSL) data. An errors-in-variables regression model allows for uncertainty in the explanatory variable, here the age of sea level data point. The algorithm is more fully defined in the reference below
Value
An object of class BchronRSLRun with elements
- BchronologyRun
The output from the run of
Bchronology
- samples
The posterior samples of the regression parameters
- degree
The degree of the polynomial regression
- RSLmean
The RSL mean values given to the function
- RSLsd
The RSL standard deviations as given to the function
- const
The mean of the predicted age values. Used to standardise the design matrix and avoid computational issues
References
Andrew C. Parnell and W. Roland Gehrels (2013) 'Using chronological models in late holocene sea level reconstructions from salt marsh sediments' In: I. Shennan, B.P. Horton, and A.J. Long (eds). Handbook of Sea Level Research. Chichester: Wiley
See Also
BchronCalibrate
, Bchronology
, BchronDensity
, BchronDensityFast
Examples
# Load in data
data(TestChronData)
data(TestRSLData)
# Run through Bchronology
RSLrun <- with(TestChronData, Bchronology(
ages = ages,
ageSds = ageSds,
positions = position,
positionThicknesses = thickness,
ids = id,
calCurves = calCurves,
predictPositions = TestRSLData$Depth
))
# Now run through BchronRSL
RSLrun2 <- BchronRSL(RSLrun, RSLmean = TestRSLData$RSL, RSLsd = TestRSLData$Sigma, degree = 3)
# Summarise it
summary(RSLrun2)
# Plot it
plot(RSLrun2)