Description Usage Arguments Details Value Author(s) References Examples
View source: R/combinationWithOutliers.R
Bayesian modeling for combining Gaussian dates with known variance and that may be outliers. These dates are assumed to be contemporaneous of the target date and have non identical distributions as the variance may be different for each date. The posterior distribution of the modeling is sampled by a MCMC algorithm implemented in JAGS.
1 2 3 4  combinationWithOutliers_Gauss(M, s, refYear=NULL, outliersIndivVariance,
outliersBernouilliProba, studyPeriodMin, studyPeriodMax, numberChains = 2,
numberAdapt = 10000, numberUpdate = 10000, variable.names = c("theta"),
numberSample = 50000, thin = 10)

M 
vector of measurement 
s 
vector of measurement errors 
refYear 
vector of year of reference for ages 
outliersIndivVariance 
vector of individual variance for delta[i] 
outliersBernouilliProba 
vector of Bernouilli probability for each date. Reflects a prior assumption that the date is an outlier. 
studyPeriodMin 
numerical value corresponding to the start of the study period in BC/AD format 
studyPeriodMax 
numerical value corresponding to the end of the study period in BC/AD format 
numberChains 
number of Markov chains simulated 
numberAdapt 
number of iterations in the Adapt period of the MCMC algorithm 
numberUpdate 
number of iterations in the Update period of the MCMC algorithm 
variable.names 
names of the variables whose Markov chains are kept 
numberSample 
number of iterations in the Acquire period of the MCMC algorithm 
thin 
step between consecutive iterations finally kept 
If there are Nbobs measurements M associated with their error s, the model is the following one :
for j in (1:Nbobs)
Mj ~ N(muj, sj^2)
muj < theta + deltaj * phij
deltaj ~ N(0, sigma.deltaj^2)
phij ~ Bern(pj)
theta ~ U(ta, tb)
This function returns a Markov chain of the posterior distribution. The MCMC chain is in date format BC/AD, that is the reference year is 0. Only values for the variables defined by 'variable.names' are given.
Anne Philippe & MarieAnne Vibet
Bronk Ramsey C., Dealing with outliers and offsets in Radiocarbon dating, Radiocarbon, 2009, 51:102345.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15  data(sunspot)
MCMC1 = combinationWithOutliers_Gauss(M=sunspot$Age[1:10], s= sunspot$Error[1:10],
refYear=rep(2016,10), outliersIndivVariance = rep(1,10),
outliersBernouilliProba=rep(0.2, 10), studyPeriodMin=800, studyPeriodMax=1500,
variable.names = c('theta'))
plot(MCMC1)
gelman.diag(MCMC1)
# Influence of outliersIndivVariance
MCMC2 = combinationWithOutliers_Gauss(M=sunspot$Age[1:10], s= sunspot$Error[1:10],
refYear=rep(2016,10), outliersIndivVariance = rep(10,10),
outliersBernouilliProba=rep(0.2, 10), studyPeriodMin=800, studyPeriodMax=1500,
variable.names = c('theta'))
plot(MCMC2)
gelman.diag(MCMC2)

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