Description Usage Arguments Value Author(s) References Examples
View source: R/chronoOutliers_Gauss.R
Bayesian modeling for combining Gaussian dates. These dates are assumed to be contemporaneous of the event date. The posterior distribution is sampled by a MCMC algorithm as well as those of all parameters of the Bayesian model.
1 2 3 4 | chronoOutliers_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 for coversion into calendar dates |
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 |
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 & Marie-Anne Vibet
Bronk Ramsey C., Dealing with outliers and offsets in Radiocarbon dating, Radiocarbon, 2009, 51:1023-45.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 | ### simulated data (see examples(chronoEvent_Gauss))
# Number of event
Nevt = 3
# number of dates by events
measurementsPerEvent = c(2,3,2)
# positions
pos = 1 + c(0, cumsum(measurementsPerEvent) )
# simulation of data
theta.evt = seq(1,10, length.out= Nevt)
theta.evt[3] <- theta.evt[3] - 3 # stratigraphic inversion
theta = NULL
for(i in 1:Nevt ){
theta = c(theta, rep(theta.evt[i],measurementsPerEvent[i]))
}
s = seq(1,1, length.out= sum(measurementsPerEvent))
M=NULL
for( i in 1:sum(measurementsPerEvent)){
M= c(M, rnorm(1, theta[i], s[i] ))
}
MCMCSample = chronoOutliers_Gauss(M, s, outliersIndivVariance = rep(5,7),
outliersBernouilliProba=rep(0.2,7), studyPeriodMin=-10, studyPeriodMax=30,
numberAdapt = 1000, numberUpdate = 1000, numberSample = 5000)
plot(MCMCSample)
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