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spm (version 1.2.2)

Spatial Predictive Modeling

Description

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) Li, J., Heap, A. D., Potter, A., Huang, Z. and Daniell, J. (2011) Li, J., Heap, A. D., Potter, A. and Daniell, J. (2011) Li, J., Potter, A., Huang, Z. and Heap, A. (2012) .

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Version

Install

install.packages('spm')

Monthly Downloads

238

Version

1.2.2

License

GPL (>= 2)

Maintainer

Last Published

May 6th, 2022

Functions in spm (1.2.2)

Cross validation, n-fold for the hybrid method of generalized boosted regression modeling and inverse distance weighting (gbmidw)

Averaged variable importance based on random forest

Cross validation, n-fold for the hybrid method of generalized boosted regression modeling and ordinary kriging (gbmok)

Generate spatial predictions using the hybrid method of generalized boosted regression modeling and inverse distance weighting (gbmidw)

Cross validation, n-fold for random forest (RF)

gbmokgbmidwpred

Generate spatial predictions using the average of the hybrid method of generalized boosted regression modeling and ordinary kriging and the hybrid method of generalized boosted regression modeling and inverse distance weighting (gbmokgbmidw)

Cross validation, n-fold for the average of the hybrid method of generalized boosted regression modeling and ordinary kriging and the hybrid method of generalized boosted regression modeling and inverse distance weighting (gbmokgbmidw)

Cross validation, n-fold for generalized boosted regression modeling (gbm)

Generate spatial predictions using the hybrid method of generalized boosted regression modeling and ordinary kriging (gbmok)

Generate spatial predictions using the hybrid method of random forest in ranger and inverse distance weighting (RGIDW)

Generate spatial predictions using generalized boosted regression modeling (`gbm`)

Cross validation, n-fold for ordinary kriging (OK)

Generate spatial predictions using ordinary kriging (OK)

Cross validation, n-fold for random forest in ranger (RG)

Cross validation, n-fold for the hybrid method of random forest in ranger and inverse distance weighting (RGIDW)

Relative variable influence based on generalized boosted regression modeling (gbm)

A dataset of sponge species richness in the Timor Sea region, northern Australia marine margin

Cross validation, n-fold for the hybrid method of random forest in ranger and ordinary kriging (RGFOK)

Cross validation, n-fold for the average of the hybrid method of random forest and ordinary kriging and the hybrid method of random forest and inverse distance weighting (RFOKRFIDW)

Generate spatial predictions using the hybrid method of random forest and ordinary kriging (RFOK)

A dataset of seabed hardness in the eastern Joseph Bonaparte Golf, northern Australia marine margin

A dataset of seabed sediments in the Petrel sub-basin in Australia Exclusive Economic Zone

Cross validation, n-fold for the average of the hybrid method of random forest in ranger (RG) and ordinary kriging and the hybrid method of RG and inverse distance weighting (RGOKRGIDW)

Generate spatial predictions using the hybrid method of random forest in ranger and ordinary kriging (RGOK)

Cross validation, n-fold for the hybrid method of random forest and ordinary kriging (RFOK)

A dataset of grids for producing spatial predictions of seabed sediment content in the Petrel sub-basin in Australia Exclusive Economic Zone

Generate spatial predictions using the hybrid method of random forest and inverse distance weighting (RFIDW)

Generate spatial predictions using inverse distance weighting (IDW)

Cross validation, n-fold for inverse distance weighting (IDW)

Generate spatial predictions using the average of the hybrid method of random forest and ordinary kriging and the hybrid method of random forest and inverse distance weighting (RFOKRFIDW)

Predictive error and accuracy measures for predictive models based on cross-validation

A dataset of seabed mud content in the southwest Australia Exclusive Economic Zone

Cross validation, n-fold for the hybrid method of random forest and inverse distance weighting (RFIDW)

A dataset of predictors for generating sponge species richness in a selected region in the Timor Sea region, northern Australia marine margin

Convert error measures to vecv

Generate spatial predictions using the average of the hybrid method of random forest in ranger (RG) and ordinary kriging and the hybrid method of RG and inverse distance weighting (RGOKRGIDW)

Generate spatial predictions using random forest (RF)

A dataset of grids for producing spatial predictions of seabed mud content in the southwest Australia Exclusive Economic Zone

Generate spatial predictions using random forest in ranger (RG)

Variance explained by predictive models based on cross-validation