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)
```

[Package *ArchaeoChron* version 0.1 Index]