Plateau5: Plateau5

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

Plateau5

Description

MC Analysis TL from linear regression using plateau samplerS

Usage

Plateau5(
  Dose,
  df.T,
  df.y,
  Ti = 200,
  Tf = 500,
  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
n.iter	numeric (required) the number of iteration
inv	logical (with default) TRUE to calculate the inverse estimator. Default is FALSE: i.e. the direct estimator
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 × 6)-matrix,

column	Type	Description
alpha	numeric	intercept
beta	numeric	slope
sigma2	numeric	variance
T1	numeric	lower limit of the temperature range
T2	numeric	upper limit of the temperature range
De	numeric	'True' Dose value

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<-Plateau5(Dose,df.T,df.y,Ti=50,Tf=400,n.iter=n.iter)
#
if(dev.cur()!=1) dev.off()
data(TLpan, envir = environment())
Dose<-c(0,0,80,80,80,160,160,160)
df.T<-matrix(rep(seq(26,500),8),475,8)
df.y<-TL.Pan[,1:8]
Pan<-Plateau5(Dose,df.T,df.y,n.iter=100,inv=TRUE)

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