BchronRSL {Bchron}

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

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 itemize

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)