AgeDepth {ArchaeoPhases} | R Documentation |
age depth curve Compute the age-depth curve from the output of mcmc algorithm of ages and the known depth of each dated samples.
AgeDepth(
data,
depth,
new.depth = NULL,
max.iter = nrow(data),
sampling = FALSE
)
data |
Data frame containing the output of the MCMC algorithm. |
depth |
Vector of the depths of the dated samples |
new.depth |
Vector of the undated depths whose the age will be predict. By defauft |
max.iter |
a non-negative integer giving the limit number of MCMC itereations By defauft |
sampling |
should sampling be random. By defauft |
Estimate the Age-Depth relationship from the MCMC output of a Bayesian chronological model and the depth of each dated sample.
We assume it exists a function f
relating the age and the depth age = f( depth)
. We estimate the function using local regression (also called local polynomial regression): f = loess(age ~ depth)
.
This estimated function f
depends on the unknown dates. However, from the posterior distribution of the age/date sequence, we can evaluate the posterior distribution of the age function for each desired depth.
A list containing two matrix. data.stat
summarises the MCMC output from the L1 Bayes estimate and credible interval
at significance level 68% and 95%. age.depth
provide the L1 Bayes estimate, credible interval
at significance level 68% and 95% on the age at depth depth
and new.depth
Anne Philippe, Anne.Philippe@univ-nantes.fr
D.K. Jha , P. Sanyal and A. Philippe 2020. Multi-Proxy Evidence of Late Quaternary Climate and Vegetational History of North-Central India: Implication for the Paleolithic to Neolithic Phases. Quaternary Science Reviews 229: 106-121.
S. Ghosh, P. Sanyal, R. Bhushan, S. P Sati, A. Philippe, and N. Juyal. 2020. Early Holocene Indian summer monsoon and its impact on vegetation in the Central Himalaya. The Holecene 30:7, 1063-1074.
data = matrix(rnorm(6000,(1:6)^2), ncol=6 , byrow = TRUE )
depth= 1:6
AgeDepth(data,depth)
AgeDepth(data,depth, 1.5:6)