Plateau4: Plateau4

In Zink-Antoine/TLpack: Analyse TL measurement using Bayesian approach, MCMC, Gibbs and slice samplers

Plateau4

Description

MC Analysis TL (Plateau +gibbs) following gibbs4.R Hickey (2006) Bayesian approach without expert judgement additional scalar variable T (Plateau sampler)

Usage

Plateau4(
  Dose,
  df.T,
  df.y,
  Ti = 200,
  Tf = 500,
  k = 1,
  yn = 0,
  n.iter,
  inv = FALSE,
  n.burnin = n.iter/2,
  n.thin = max(1, floor(n.iter - n.burnin)/10)
)

Arguments

Dose	numeric (required) set of the irradiation doses
df.T	matrix (required) Luminescence data: the temperatures
df.y	matrix (required) Luminescence data; the luminescence signal
Ti	numeric (with default) temperature minimum for the plateau
Tf	numeric (with default) temperature maximum for the plateau
k	numeric (required) the number of parameters in the group involving sigma
yn	numeric (with default) the reference luminescence signal. For MAAD TL, it is extrapolated to yn=0.
n.iter	numeric (required) the number of iteration
inv	logical (with default value) inverse (TRUE) or direct (FALSE) regression (default value FALSE)
n.burnin	numeric (with default) number of iterations to discard at the beginning (burn in). Default is n.iter/2, that is, discarding the first half of the simulations.
n.thin	numeric (with default) thinning rate. Must be a positive integer. Set n.thin > 1 to save memory and computation time if n.iter is large. Default is max(1, floor((n.iter-n.burnin) / 10)) which will only thin if there are at least 20 simulations.

Value

an (r × 5)-matrix (mcmc class),

column	Type	Description
alpha	numeric	intercept
beta	numeric	slope
sigma2	numeric	variance
T	numeric	Temperature
De	numeric	'True' Dose value

References

Gibbs sampler: Hickey, G. L. 2006. « The Linear Calibration Problem: A Bayesian Analysis ». PhD Thesis, PhD dissertation, University of Durham. 1–148. http://www.dur.ac.uk/g.l.hickey/dissertation.pdf.

chapter 6.3.6 and Appendix G.7

Examples

##load data
if(dev.cur()!=1) dev.off()
data(multiTL, envir = environment())
Dose<-multiTL$Dose
df.T<-multiTL$df.T
df.y<-multiTL$df.y
n.iter<-multiTL$n.iter
test<-Plateau4(Dose,df.T,df.y,k=1,n.iter=n.iter)

Zink-Antoine/TLpack documentation built on April 14, 2025, 1:58 p.m.