chrono_Gauss {ArchaeoChron} R Documentation

## Bayesian chronologies of Gaussian dates

### Description

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.

### Usage

chrono_Gauss(M, s, refYear=NULL, studyPeriodMin, studyPeriodMax,
numberChains = 2, numberAdapt = 10000, numberUpdate = 10000,
variable.names = c("theta"), numberSample = 50000, thin = 10)


### Arguments

 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

### Value

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.

### Author(s)

Anne Philippe & Marie-Anne Vibet

### Examples

  ### 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)


[Package ArchaeoChron version 0.1 Index]