BchronRSL: 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 Bchronology

RSLmean

A vector of RSL mean estimates of the same length as the number of predictPositions given to the Bchronology function

RSLsd

A vector RSL standard deviations of the same length as the number of predictPositions given to the Bchronology function

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

Value

An object of class BchronRSLRun with elements itemize

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

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

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

Examples

Run this code

# NOT RUN {
# Load in data
data(TestChronData)
data(TestRSLData)

# Run through Bchronology
RSLrun = Bchronology(ages=TestChronData$ages,
                     ageSds=TestChronData$ageSds,
                     positions=TestChronData$position,
                     positionThicknesses=TestChronData$thickness,
                     ids=TestChronData$id,
                     calCurves=TestChronData$calCurves,
                     predictPositions=TestRSLData$Depth,
                     jitterPositions = TRUE)

# Now run through BchronRSL
RSLrun2 = BchronRSL(RSLrun,RSLmean=TestRSLData$RSL,RSLsd=TestRSLData$Sigma,degree=3)

# Summarise it
summary(RSLrun2)

# Plot it
plot(RSLrun2)
# }

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