Cargando…
Comparing spatial regression to random forests for large environmental data sets
Environmental data may be “large” due to number of records, number of covariates, or both. Random forests has a reputation for good predictive performance when using many covariates with nonlinear relationships, whereas spatial regression, when using reduced rank methods, has a reputation for good p...
Autores principales: | Fox, Eric W., Ver Hoef, Jay M., Olsen, Anthony R. |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2020
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7089425/ https://www.ncbi.nlm.nih.gov/pubmed/32203555 http://dx.doi.org/10.1371/journal.pone.0229509 |
Ejemplares similares
-
Indexing and partitioning the spatial linear model for large data sets
por: Ver Hoef, Jay M., et al.
Publicado: (2023) -
A Comparison of the Spatial Linear Model to Nearest Neighbor (k-NN) Methods for Forestry Applications
por: Ver Hoef, Jay M., et al.
Publicado: (2013) -
spmodel: Spatial statistical modeling and prediction in [Image: see text]
por: Dumelle, Michael, et al.
Publicado: (2023) -
Spatially Estimating Disturbance of Harbor Seals (Phoca vitulina)
por: Jansen, John K., et al.
Publicado: (2015) -
Robustness of close‐kin mark–recapture estimators to dispersal limitation and spatially varying sampling probabilities
por: Conn, Paul B., et al.
Publicado: (2020)