chronoOutliers_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.

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.

### 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)

[Package *ArchaeoChron* version 0.1 Index]