Slice5: Slice5

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

Slice5

Description

MC Analysis TL (slice +gibbs) from linear regression

Usage

Slice5(
  Dose,
  df.T,
  df.y,
  n.iter,
  n.burnin = n.iter/2,
  n.thin = max(1, floor(n.iter - n.burnin)/10),
  threshold = min(df.y[df.y > 0])
)

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
n.iter	numeric (required) the number of iteration
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.
threshold	numeric (with default) measured point below or equal to the threshold are rejected. Default is the minimum measured value.

Value

an mcmc object,

column	Type	Description
intercept	numeric	intercept
x	numeric	slope
std.dev	numeric	standard deviation
Temperature	numeric	Temperature
⁠natural dose⁠	numeric	estimated 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<-Slice5(Dose,df.T,df.y,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<-Slice5(Dose,df.T,df.y,n.iter=100)
#
## Not run: 
data(TLetru)
table<-Lum(TLetru,Doseb0=360,Dosea=0,alpha=FALSE,supra=FALSE)
B<-table$b
N<-table$n
table.norm<-B
 for (j in 1:3){
   for (i in 1:3)
     {
       table.norm[,i,j]<-(B[,i,j])/sum(N[seq(350,400),1,(j-1)*3+i])
       }
   }
 table.data<-cbind(table.norm[,,1],table.norm[,,2],table.norm[,,3])
 ii<-c(1,4,7,2,5,8,3,6,9)
 table.data<-table.data[seq(1,475),ii]
 Dose<-as.numeric(colnames(table.data))
 df.T<-matrix(rep(seq(26,500),9),475,9)
 df.y<-table.data[,1:9]
 plot(seq(26,500),table.data[,8])
 Slice5(Dose,df.T,df.y,n.iter=10)
 
## End(Not run)

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