BchronRSL {Bchron} | R Documentation |
Relative sea level rate (RSL) estimation
BchronRSL(
BchronologyRun,
RSLmean,
RSLsd,
degree = 1,
iterations = 10000,
burn = 2000,
thin = 8
)
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 |
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
An object of class BchronRSLRun with elements itemize
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
BchronCalibrate
, Bchronology
, BchronDensity
, BchronDensityFast
# 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)