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)