Author: Dewey Dunnington
Date: 2025-10-18

# Age-depth models

#### Dewey Dunnington

#### 2025-10-18

Given a known depths and known (or modelled) ages, it is often convenient to approximate age as a continuous function of depth in an archive. This package provides tools to flexibly create age-depth relationships with various rules for interpolating age within known age-depth values, and extrapolating above and below these values. Typically, this is interpolation between known values and extrapolating using average sedimentation rates based on ages known at discrete points in a core.

## Example

Using the built-in dataset `alta_lake_210Pb_ages`, which contains a Lead-210 (CRS) age-depth relationship for a core from Alta Lake, Whistler, British Columbia, we can create an age-depth model (note that `age` and `depth` are evaluated within `.data`, if it is provided, and support tidy evaluation):

    library(tidypaleo)
    alta_lake_adm <- age_depth_model(
      alta_lake_210Pb_ages,
      depth = depth_cm, age = age_year_ad,
      age_max = age_year_ad + age_error_yr,
      age_min = age_year_ad - age_error_yr
    )
    alta_lake_adm
    #> <age_depth_model>
    #> Call:
    #> age_depth_model(
    #>   .data = alta_lake_210Pb_ages, depth = depth_cm,
    #>   age = age_year_ad, age_min = age_year_ad - age_error_yr,
    #>   age_max = age_year_ad + age_error_yr
    #> )

Then, we can plot the relationship:

    plot(alta_lake_adm)

…Or predict raw data:

    predict(alta_lake_adm, depth = seq(-1, 10, 0.5))

depth | age | age_min | age_max | method
--- | --- | --- | --- | ---
-1.0 | 2031.701 | NA | NA | extrapolate_above
-0.5 | 2023.150 | NA | NA | extrapolate_above
0.0 | 2014.600 | 2014.60 | 2014.60 | interpolate
0.5 | 2011.300 | NA | NA | interpolate
1.0 | 2008.000 | 2007.66 | 2008.34 | interpolate
1.5 | 2003.400 | 2002.84 | 2003.96 | interpolate
2.0 | 1998.100 | 1997.24 | 1998.96 | interpolate
2.5 | 1989.950 | NA | NA | interpolate
3.0 | 1981.800 | 1979.55 | 1984.05 | interpolate
3.5 | 1973.700 | NA | NA | interpolate
4.0 | 1965.600 | 1960.87 | 1970.33 | interpolate
4.5 | 1956.400 | NA | NA | interpolate
5.0 | 1947.200 | 1938.99 | 1955.41 | interpolate
5.5 | 1934.750 | NA | NA | interpolate
6.0 | 1922.300 | 1895.28 | 1949.32 | interpolate
6.5 | 1909.150 | NA | NA | interpolate
7.0 | 1896.000 | 1838.81 | 1953.19 | interpolate
7.5 | 1887.450 | NA | NA | extrapolate_below
8.0 | 1878.899 | NA | NA | extrapolate_below
8.5 | 1870.349 | NA | NA | extrapolate_below
9.0 | 1861.798 | NA | NA | extrapolate_below
9.5 | 1853.248 | NA | NA | extrapolate_below
10.0 | 1844.697 | NA | NA | extrapolate_below

The default behaviour is to interpolate within known ages/depths, and extrapolate using a linear fit of ages/depths. These can be specified using transform functions, which take XY data and produce forward and inverse predictions based on them. The default call is:

    age_depth_model(
      ...,
      interpolate_age = age_depth_interpolate,
      extrapolate_age_below = ~age_depth_extrapolate(.x, .y, x0 = last, y0 = last),
      extrapolate_age_above = ~age_depth_extrapolate(.x, .y, x0 = first, y0 = first),
      interpolate_age_limits = trans_exact,
      extrapolate_age_limits_below = trans_na,
      extrapolate_age_limits_above = trans_na
    )

To customize the behaviour of the predictions (e.g., disable extrapolating above or below), specify a transform function in the appropriate category. One-sided formulas are turned into functions using the `rlang::as_function()`. A more advanced way might be to only use the first/last few observations to extrapolate above and below, which one could do like this:

    alta_lake_adm2 <- age_depth_model(
      alta_lake_210Pb_ages,
      depth = depth_cm, age = age_year_ad,
      age_max = age_year_ad + age_error_yr,
      age_min = age_year_ad - age_error_yr,
      extrapolate_age_below = ~age_depth_extrapolate(
        tail(.x, 3), tail(.y, 3), x0 = dplyr::last, y0 = dplyr::last
      ),
      extrapolate_age_above = ~age_depth_extrapolate(
        head(.x, 3), head(.y, 3), x0 = dplyr::first, y0 = dplyr::first
      )
    )

    plot(alta_lake_adm2)