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

## 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()`

## 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
```