BchronRSL() R function from [Bchron]

Relative sea level rate (RSL) estimation


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

Returns

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

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)

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

BchronRSL function