ReplicateChar package
The ReplicateChar package in R language provides functions for visualization and analysis of replicated charcoal counts within a discreet sedimentary horizon. The package consists of 1000+ lines of code and requires Windows OS for full functionality. It also integrates and automates the CharAnalysis.exe software (Higuera et.al) and allows for more efficient run times of high volumes of original and simulated core data. Data processing incorporates three major steps: 1) testing and visualizing of original replicate data, 2) creating artificial cores and simulations from the original replicate counts to run them in CharAnalysis.exe, and 3) analyzing and visualizing of CharAnalysis.exe output. Step number two can take a substantial amount of time depending on the chosen number of simulations, generally about 40 seconds per simulated core. The README file in the root level includes a sample workflow showing all types of visualizations the package can create. The code is generalized and can be used for processing datasets that are arranged in the form suggested in the guidelines of README file. The package automatically outputs plots and tables for all core sites included in the input data folder. By working through the sample workflow, one can reproduce figures and tables presented in Anderson et al. 2022. The intent of this package is to encourage other interested researchers in collection of replicate intra-level data and to enable analyses and visualization of the results based on the concepts and algorithms presented here.
Citation Information
| Publication Year | 2022 |
|---|---|
| Title | ReplicateChar package |
| DOI | 10.5066/P9D27CPD |
| Authors | Liubov S Presnetsova |
| Product Type | Software Release |
| Record Source | USGS Digital Object Identifier Catalog |
| USGS Organization | Geology, Minerals, Energy, and Geophysics Science Center |
Related Content
Assessing reproducibility in sedimentary macroscopic charcoal count data
Related Content
- Publications
Assessing reproducibility in sedimentary macroscopic charcoal count data
Current understanding of global late Quaternary fire history is largely drawn from sedimentary charcoal data. Since publication, CharAnalysis increasingly has been relied upon as a robust method for analyzing these data. However, several underlying assumptions of the algorithm have not been tested. This study uses replicated charcoal count data to examine the assumption of Poisson distribution andAuthorsLysanna Anderson, Liubov S. Presnetsova, David Wahl, Geoffrey Phelps, Alan Gous - Connect