chrono_Gauss {ArchaeoChron} | R Documentation |
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.
chrono_Gauss(M, s, refYear=NULL, 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 |
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
### simulated data (see examples(chronoEvent_Gauss)) # Number of events 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 = 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 = chrono_Gauss(M, s, studyPeriodMin=-10, studyPeriodMax=30) plot(MCMCSample)