JAGS models use in Palaeodose_Computation

A list of JAGS models use to a Bayesian analysis of OSL palaeodose of one or various samples. There are models for various growth curves and various distrubution to describe equivalent dose distribution around the palaeodose.

data("Model_Palaeodose")

Format

This list contains:

PalaeodosesMultiBF_EXPLIN

a list of 4 models that all consider a saturating exponential plus linear growth. These 4 models have different distribution to describe equivalent dose values around the palaeodose.

PalaeodosesMultiBF_EXP

a list of 4 models that all consider a saturating exponential growth. These 4 models have different distribution to describe equivalent dose values around the palaeodose.

PalaeodosesMultiBF_EXPZO

a list of 4 models that all consider a saturating exponential plus linear growth and fitting through the origin. These 4 models have different distribution to describe equivalent dose values around the palaeodose.

PalaeodosesMultiBF_EXPLINZO

a list of 4 models that all consider a saturating exponential growth and fitting through the origin. These 4 models have different distribution to describe equivalent dose values around the palaeodose.

Details

The different distibutions to describe equivalent dose values around the palaeodose are:

cauchy

a Cauchy distribution with postition parameter equal to the palaeodose of the sample

gaussian

a Gaussian distribution with mean equal to the palaeodose of the sample

lognormal_A

a log-normal distribution with mean or Average equal to the palaeodose of the sample

lognormal_M

a log-normal distribution with Median equal to the palaeodose of the sample

For more information we refer to the function Palaeodose_Computation, section Details.

References

Plummer, M. (2003). JAGS: A program for analysis of Bayesian graphical models using Gibbs sampling. In Proceedings of the 3rd international workshop on distributed statistical computing, volume 124, page 125. Technische Universit at Wien, Austria.

Plummer, M. (2015). JAGS Version 4.0. 0 user manual.

See also

rjags

Examples

data(Model_Palaeodose)
writeLines(Model_Palaeodose$PalaeodosesMultiBF_EXPLIN$gaussian)
#> model {
#> 
#>   for(i0 in 1:I){
#>   D[i0]~dunif(xbound[1],xbound[2])
#>   sD[i0]~dt(0,pow(0.16*D[i0],-2),1)T(0,)
#>   pD[i0]<-pow(sD[i0],-2)
#>   }
#> 
#>   # Likelihood:
#>   for(i in 1:I){
#>   for(bf in (CSBinPerSample[i]-BinPerSample[i]+1):(CSBinPerSample[i])){
#>   for(j in 1:J[bf]){
#>   # prior on growth function
#>   xa[(index[bf]+j)]~dnorm(6.5,1/(9.2^2))T(0,)
#>   xb[(index[bf]+j)]~dnorm(50,1/(1000^2))T(0,)
#>   xc[(index[bf]+j)]~dnorm(0.002,1/(0.01^2))T(0,)
#>   xd[(index[bf]+j)]~dnorm(0.5,1/(2.5^2))T(-xa[(index[bf]+j)],)
#>   sigmaf[(index[bf]+j)]~dexp(20)
#> 
#>   De[(index[bf]+j),1]~dnorm(D[i],pD[i])
#>   #
#>   xprecision[(index[bf]+j),1]<-1/(sigmaf[(index[bf]+j)]^2+sN[(index[bf]+j),1]^2) ##<-- ???? sN[j,1]^2 ????
#>   N[(index[bf]+j),1]~dnorm(xQ[(index[bf]+j),1],xprecision[(index[bf]+j),1])
#>   xQ[(index[bf]+j),1]<-xa[(index[bf]+j)]*(1-exp(-De[(index[bf]+j),1]/xb[(index[bf]+j)]))+xc[(index[bf]+j)]*De[(index[bf]+j),1]+xd[(index[bf]+j)]
#> 
#>   for(k in 2:K[bf]){
#>   xprecision[(index[bf]+j),k]<-1/(sigmaf[(index[bf]+j)]^2+sN[(index[bf]+j),k]^2)
#>   N[(index[bf]+j),k]~dnorm(xQ[(index[bf]+j),k],xprecision[(index[bf]+j),k])
#>   xQ[(index[bf]+j),k]<-xa[(index[bf]+j)]*(1-exp(-De[(index[bf]+j),k]/xb[(index[bf]+j)]))+xc[(index[bf]+j)]*De[(index[bf]+j),k]+xd[(index[bf]+j)]
#>   De[(index[bf]+j),k]<-IT[(index[bf]+j),(k-1)]*sDlab[bf]
#>   }
#>   }
#>   }
#>   }
#> 
#>   }