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Source code for performing bootstrapping for Gradient Forest objects

The University of Queensland
Mr Phil Dyer (Aggregated by) Mr Phil Dyer (Aggregated by) Mr Philip Dyer (Aggregated by) Mr Philip Dyer (Aggregated by)
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ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Adc&rfr_id=info%3Asid%2FANDS&rft_id=info:doi10.48610/0f21431&rft.title=Source code for performing bootstrapping for Gradient Forest objects&rft.identifier=RDM ID: e5ed8566-3e1f-4324-8dd2-e6033a807086&rft.publisher=The University of Queensland&rft.description=This dataset contains an R package that implements bootstrapping for Gradient Forest (Ellis et al. 2012). Bootstrapping provides estimates of uncertainty for Gradient Forest predictions, and allows the variability of the mean response to be calculated. When the mean difference between grid cells is large relative to the variance of each grid cell prediction, then the grid cells are deemed to be dissimilar. Otherwise, the grid cells are similar. Ellis, N., Smith, S. J., & Pitcher, C. R. (2012). Gradient forests: calculating importance gradients on physical predictors. Ecology, 93(1), 156-168. License: GNU General Public License V3&rft.creator=Mr Phil Dyer&rft.creator=Mr Phil Dyer&rft.creator=Mr Philip Dyer&rft.creator=Mr Philip Dyer&rft.date=2024&rft_rights= https://guides.library.uq.edu.au/deposit-your-data/license-reuse-data-agreement&rft_subject=eng&rft_subject=ENVIRONMENTAL SCIENCES&rft_subject=Conservation and biodiversity&rft_subject=Environmental management&rft_subject=MATHEMATICAL SCIENCES&rft_subject=Statistics&rft_subject=Applied statistics&rft.type=dataset&rft.language=English Access the data
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s4087576@student.uq.edu.au
School of Mathematics and Physics

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This dataset contains an R package that implements bootstrapping for Gradient Forest (Ellis et al. 2012). Bootstrapping provides estimates of uncertainty for Gradient Forest predictions, and allows the variability of the mean response to be calculated. When the mean difference between grid cells is large relative to the variance of each grid cell prediction, then the grid cells are deemed to be dissimilar. Otherwise, the grid cells are similar. Ellis, N., Smith, S. J., & Pitcher, C. R. (2012). Gradient forests: calculating importance gradients on physical predictors. Ecology, 93(1), 156-168. License: GNU General Public License V3

Issued: 28 03 2024

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Subjects
Applied Statistics | Conservation and Biodiversity | Environmental Sciences | Environmental Management | Mathematical Sciences | Statistics | eng |

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local : UQ:289097

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