Spatial Predictive Modeling
Cross validation, n-fold for the average of the hybrid method of gener...
Generate spatial predictions using the average of the hybrid method of...
Generate spatial predictions using the hybrid method of generalized bo...
Generate spatial predictions using generalized boosted regression mode...
Averaged variable importance based on random forest
Note on notes
Cross validation, n-fold for generalized boosted regression modeling (...
Cross validation, n-fold for the hybrid method of generalized boosted ...
Generate spatial predictions using the hybrid method of generalized bo...
Cross validation, n-fold for the hybrid method of generalized boosted ...
Cross validation, n-fold for inverse distance weighting (IDW)
Generate spatial predictions using inverse distance weighting (IDW)
Cross validation, n-fold for ordinary kriging (OK)
Generate spatial predictions using ordinary kriging (OK)
Predictive error and accuracy measures for predictive models based on ...
Cross validation, n-fold for random forest (RF)
Cross validation, n-fold for the hybrid method of random forest and in...
Generate spatial predictions using the hybrid method of random forest ...
Cross validation, n-fold for the hybrid method of random forest and or...
Generate spatial predictions using the hybrid method of random forest ...
Cross validation, n-fold for the average of the hybrid method of rando...
Generate spatial predictions using the average of the hybrid method of...
Generate spatial predictions using random forest (RF)
Cross validation, n-fold for random forest in ranger (RG)
Cross validation, n-fold for the hybrid method of random forest in ran...
Generate spatial predictions using the hybrid method of random forest ...
Cross validation, n-fold for the hybrid method of random forest in ran...
Generate spatial predictions using the hybrid method of random forest ...
Cross validation, n-fold for the average of the hybrid method of rando...
Generate spatial predictions using the average of the hybrid method of...
Generate spatial predictions using random forest in ranger (RG)
Relative variable influence based on generalized boosted regression mo...
Convert error measures to vecv
Variance explained by predictive models based on cross-validation
Introduction to some novel accurate hybrid methods of geostatistical and machine learning methods for spatial predictive modelling. It contains two commonly used geostatistical methods, two machine learning methods, four hybrid methods and two averaging methods. For each method, two functions are provided. One function is for assessing the predictive errors and accuracy of the method based on cross-validation. The other one is for generating spatial predictions using the method. For details please see: Li, J., Potter, A., Huang, Z., Daniell, J. J. and Heap, A. (2010) <https:www.ga.gov.au/metadata-gateway/metadata/record/gcat_71407> Li, J., Heap, A. D., Potter, A., Huang, Z. and Daniell, J. (2011) <doi:10.1016/j.csr.2011.05.015> Li, J., Heap, A. D., Potter, A. and Daniell, J. (2011) <doi:10.1016/j.envsoft.2011.07.004> Li, J., Potter, A., Huang, Z. and Heap, A. (2012) <https:www.ga.gov.au/metadata-gateway/metadata/record/74030>.