CAST: 'caret' Applications for Spatial-Temporal Models
In CAST: 'caret' Applications for Spatial-Temporal Models

CAST	R Documentation

'caret' Applications for Spatial-Temporal Models

Description

Supporting functionality to run 'caret' with spatial or spatial-temporal data. 'caret' is a frequently used package for model training and prediction using machine learning. CAST includes functions to improve spatial-temporal modelling tasks using 'caret'. It includes the newly suggested 'Nearest neighbor distance matching' cross-validation to estimate the performance of spatial prediction models and allows for spatial variable selection to selects suitable predictor variables in view to their contribution to the spatial model performance. CAST further includes functionality to estimate the (spatial) area of applicability of prediction models by analysing the similarity between new data and training data. Methods are described in Meyer et al. (2018); Meyer et al. (2019); Meyer and Pebesma (2021); Milà et al. (2022); Meyer and Pebesma (2022); Linnenbrink et al. (2023). The package is described in detail in Meyer et al. (2024).

Details

'caret' Applications for Spatio-Temporal models

Author(s)

Hanna Meyer, Carles Milà, Marvin Ludwig, Jan Linnenbrink, Fabian Schumacher

References

Meyer, H., Ludwig, L., Milà, C., Linnenbrink, J., Schumacher, F. (2024): The CAST package for training and assessment of spatial prediction models in R. arXiv, https://doi.org/10.48550/arXiv.2404.06978.
Linnenbrink, J., Milà, C., Ludwig, M., and Meyer, H.: kNNDM: k-fold Nearest Neighbour Distance Matching Cross-Validation for map accuracy estimation, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2023-1308, 2023.
Milà, C., Mateu, J., Pebesma, E., Meyer, H. (2022): Nearest Neighbour Distance Matching Leave-One-Out Cross-Validation for map validation. Methods in Ecology and Evolution 00, 1– 13.
Meyer, H., Pebesma, E. (2022): Machine learning-based global maps of ecological variables and the challenge of assessing them. Nature Communications. 13.
Meyer, H., Pebesma, E. (2021): Predicting into unknown space? Estimating the area of applicability of spatial prediction models. Methods in Ecology and Evolution. 12, 1620– 1633.
Meyer, H., Reudenbach, C., Wöllauer, S., Nauss, T. (2019): Importance of spatial predictor variable selection in machine learning applications - Moving from data reproduction to spatial prediction. Ecological Modelling. 411, 108815.
Meyer, H., Reudenbach, C., Hengl, T., Katurji, M., Nauß, T. (2018): Improving performance of spatio-temporal machine learning models using forward feature selection and target-oriented validation. Environmental Modelling & Software 101: 1-9.