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Age-Depth Modeling

Source: R/AllGenerics.R, R/depth.R
bury.Rd

Computes the age-depth curve from the output of the MCMC algorithm and the known depth of each dated samples.

Usage

bury(object, depth, ...)

# S4 method for EventsMCMC,numeric
bury(object, depth)

# S4 method for AgeDepthModel
predict(object, newdata)

# S4 method for AgeDepthModel
autoplot(object, ..., level = 0.95)

# S4 method for AgeDepthModel,missing
plot(x, level = 0.95, ...)

Arguments

object

An EventsMCMC object.

depth

A numeric vector giving of the depths of the dated samples.

...

Currently not used.

newdata

A numeric vector giving the depths at which ages will be predicted. If missing, the original data points are used.

level

A length-one numeric vector giving the confidence level.

x

An AgeDepthModel object.

Value

  • bury() returns an AgeDepthModel object.

  • predict() returns an EventsMCMC object.

  • autoplot() returns a ggplot object.

  • plot() is called it for its side-effects: it results in a graphic being displayed (invisibly returns x).

Details

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.

References

Jha, D. K., Sanyal, P. & Philippe, A. (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: 106121. doi:10.1016/j.quascirev.2019.106121 .

Ghosh, S., Sanyal, P., Roy, S., Bhushan, R., Sati, S. P., Philippe, A. & Juyal, N. (2020). Early Holocene Indian Summer Monsoon and Its Impact on Vegetation in the Central Himalaya: Insight from ΔD and δ13C Values of Leaf Wax Lipid. The Holocene, 30(7): 1063-1074. doi:10.1177/0959683620908639 .

See also

Other age-depth modeling tools: interpolate(), proxy()

Author

A. Philippe

Examples

## Coerce to MCMC
eve <- matrix(rnorm(6000, (1:6)^2), ncol = 6, byrow = TRUE)
eve <- as_events(eve, calendar = "CE")

## Compute an age-depth curve
age <- bury(eve, depth = 1:6)
plot(age)


## Predict new values
new <- predict(age, newdata = 1.5:5.5)

plot(eve)
#> Picking joint bandwidth of 0.224

plot(new)
#> Picking joint bandwidth of 0.141