sytbru/CV-clustered-data: First release
Creators
- 1. Wageningen University
- 2. The University of Sydney
Description
R scripts related to the manuscript "Dealing with clustered samples for assessing map accuracy by cross-validation" in Ecological Informatics.
DATA
The input data are supposed to be in a directory "data". The data can be downloaded from Zenodo: DOI:10.5281/zenodo.6513429
agb.tif = above ground biomass (AGB) map
AGBstack.tif = covariates used for predicting AGB
aggArea.tif = coarse grid used for simulation in the model-based methods
ocs.tif = soil organic carbon stock (OCS) map
OCSstack.tif = covariates used for predicting OCS
strata.xxx = geo-strata used (shp) for generating the clustered samples
TOTmask.tif = mask of the area covered by the covariates
RUNING THE SCRIPTS
First, the samples need to be prepared by running the scripts sample_*.R
Next, the cross-validation scripts named CV_*.R can be run.
Start by running "CV_random.R", as the other CV_*.R scripts depend on the results it produces.
The script "CV_model_based.R" should be run before running "CV_heteroscedastic.R".
The script figs.R can be used for reproducing several of the figures shown in the manuscript. Here it is assumed that the full set of results has been generated (see WARNING below).
WARNING
Note that running the (single core) scripts with the full sample size and number of replications as used in the paper requires a very long time to complete. Set n_samp, n_CV and nsim to numbers << 100 to check the approach without reproducing all the results. The code can easily be adapted to run on multiple cores.
Files
sytbru/CV-clustered-data-v0.5.zip
Files
(1.7 MB)
Name | Size | Download all |
---|---|---|
md5:12dfc9c2591e4db29022ce56222eaf30
|
1.7 MB | Preview Download |
Additional details
Related works
- Is supplement to
- Software: https://github.com/sytbru/CV-clustered-data (URL)
- Journal article: 10.1016/j.ecoinf.2022.101665 (DOI)
- Is supplemented by
- Dataset: 10.5281/zenodo.6513429 (DOI)
References
- de Bruin et al., 2022. Dealing with clustered samples for assessing map accuracy by cross-validation. https://doi.org/10.1016/j.ecoinf.2022.101665