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clumpedcalib: Calculate and Apply Clumped Isotope Calibrations Using Bootstrapping

Written by Ilja J. Kocken Feel free to use this stuff, as long as you adhere to the file:LICENSE.md

NOTE: This is just the method I use, I’m not publishing the actual calibration dataset that I’m using here!

  • Calculate bootstrapped York regression from clumped isotope calibration data. clumped_calib_boot().
  • Calculate bootstrapped sample means for the age, d13C, d18O, and D47. bootstrap_means().
  • Apply the bootstrapped calibration to calculate temperature and d18Osw. temp_d18Osw_calc().
  • Summarize bootstrapped values and calculate 68 and 95% CIs. our_summary().
  • Also comes with the classic d18Occ calibrations. See temp_calc().
  • As well as the inverses, for when you have constraints on temperature and wish to calculate d18Osw. See d18Osw_calc().

How to calculate bootstrapped averages and apply a clumped-isotope specific temperature calibration.

To use this:

  1. Install this package clumpedcalib, see #installation.
  2. Update the input calibration data file:dat/example_calib.csv. Make sure the column names include X, D47, sd_X, sd_D47.
  3. Add your sample data at the replicate level.
  4. Check to see if you like the function that calculates d18Osw. Currently, it defaults to the Kim & O'Neil 1997 calibration, as adjusted by Bemis et al., 1998.
  5. If you want to use the wrapper function: create your own definition of filter outliers so that it filters out bad measurements and NA's for your data.
  6. Use the wrapper function to do it all at once apply_calib_and_d18O_boot(), or better yet:
  7. Walk through the example workflow step-by-step or apply the bulletpoint functions from above one-by-one.
  8. Have a look at the functions to better understand what’s happening.

The bootstrapped calibration idea is based on a Matlab script written by Alvaro Fernandez.

installation

To install this development package:

remotes::install_github("japhir/clumpedcalib")

Or you can look at all the functions and only copy what you like.