Package: spm 1.2.2

spm: Spatial Predictive Modeling

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>.

Authors:Jin Li [aut, cre]

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spm.pdf |spm.html✨
spm/json (API)

# Install 'spm' in R:
install.packages('spm', repos = c('https://jinli22.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Datasets:
  • hard - A dataset of seabed hardness in the eastern Joseph Bonaparte Golf, northern Australia marine margin
  • petrel - A dataset of seabed sediments in the Petrel sub-basin in Australia Exclusive Economic Zone
  • petrel.grid - A dataset of grids for producing spatial predictions of seabed sediment content in the Petrel sub-basin in Australia Exclusive Economic Zone
  • sponge - A dataset of sponge species richness in the Timor Sea region, northern Australia marine margin
  • sponge.grid - A dataset of predictors for generating sponge species richness in a selected region in the Timor Sea region, northern Australia marine margin
  • sw - A dataset of grids for producing spatial predictions of seabed mud content in the southwest Australia Exclusive Economic Zone
  • swmud - A dataset of seabed mud content in the southwest Australia Exclusive Economic Zone

On CRAN:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

5.46 score 3 stars 3 packages 107 scripts 239 downloads 12 mentions 33 exports 71 dependencies

Last updated 3 years agofrom:438736da4a. Checks:OK: 1 NOTE: 6. Indexed: yes.

TargetResultDate
Doc / VignettesOKOct 27 2024
R-4.5-winNOTEOct 27 2024
R-4.5-linuxNOTEOct 27 2024
R-4.4-winNOTEOct 27 2024
R-4.4-macNOTEOct 27 2024
R-4.3-winNOTEOct 27 2024
R-4.3-macNOTEOct 27 2024

Exports:avigbmcvgbmidwcvgbmidwpredgbmokcvgbmokgbmidwcvgbmokgbmidwpredgbmokpredgbmpredidwcvidwpredokcvokpredpred.accRFcvrfidwcvrfidwpredrfokcvrfokpredrfokrfidwcvrfokrfidwpredrfpredrgcvrgidwcvrgidwpredrgokcvrgokpredrgokrgidwcvrgokrgidwpredrgpredrvitovecvvecv

Dependencies:abindbiomod2classclassIntclicodetoolscolorspaceDBIdplyre1071fansifarverFNNforeachgbmgenericsggplot2gluegstatgtableintervalsisobanditeratorsKernSmoothlabelinglatticelifecyclemagrittrMASSMatrixmgcvmunsellnlmepillarpkgconfigplyrPresenceAbsencepROCproxypsyR6randomForestrangerRColorBrewerRcppRcppEigenreshapereshape2rlangrparts2scalessfsftimespspacetimestarsstringistringrsurvivalterratibbletidyselectunitsutf8vctrsviridisLitewithrwkxtszoo

A Brief Introduction to the spm Package

Jin Li ([email protected])

Rendered fromspm.Rmdusingknitr::rmarkdownon Oct 27 2024.

Last update: 2021-09-06
Started: 2017-08-25

Readme and manuals

Help Manual

Help pageTopics
Averaged variable importance based on random forestavi
Note on notescran-comments
Cross validation, n-fold for generalized boosted regression modeling (gbm)gbmcv
Cross validation, n-fold for the hybrid method of generalized boosted regression modeling and inverse distance weighting (gbmidw)gbmidwcv
Generate spatial predictions using the hybrid method of generalized boosted regression modeling and inverse distance weighting (gbmidw)gbmidwpred
Cross validation, n-fold for the hybrid method of generalized boosted regression modeling and ordinary kriging (gbmok)gbmokcv
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)gbmokgbmidwcv
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)gbmokgbmidwpred
Generate spatial predictions using the hybrid method of generalized boosted regression modeling and ordinary kriging (gbmok)gbmokpred
Generate spatial predictions using generalized boosted regression modeling (`gbm`)gbmpred
A dataset of seabed hardness in the eastern Joseph Bonaparte Golf, northern Australia marine marginhard
Cross validation, n-fold for inverse distance weighting (IDW)idwcv
Generate spatial predictions using inverse distance weighting (IDW)idwpred
Cross validation, n-fold for ordinary kriging (OK)okcv
Generate spatial predictions using ordinary kriging (OK)okpred
A dataset of seabed sediments in the Petrel sub-basin in Australia Exclusive Economic Zonepetrel
A dataset of grids for producing spatial predictions of seabed sediment content in the Petrel sub-basin in Australia Exclusive Economic Zonepetrel.grid
Predictive error and accuracy measures for predictive models based on cross-validationpred.acc
Cross validation, n-fold for random forest (RF)RFcv
Cross validation, n-fold for the hybrid method of random forest and inverse distance weighting (RFIDW)rfidwcv
Generate spatial predictions using the hybrid method of random forest and inverse distance weighting (RFIDW)rfidwpred
Cross validation, n-fold for the hybrid method of random forest and ordinary kriging (RFOK)rfokcv
Generate spatial predictions using the hybrid method of random forest and ordinary kriging (RFOK)rfokpred
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)rfokrfidwcv
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)rfokrfidwpred
Generate spatial predictions using random forest (RF)rfpred
Cross validation, n-fold for random forest in ranger (RG)rgcv
Cross validation, n-fold for the hybrid method of random forest in ranger and inverse distance weighting (RGIDW)rgidwcv
Generate spatial predictions using the hybrid method of random forest in ranger and inverse distance weighting (RGIDW)rgidwpred
Cross validation, n-fold for the hybrid method of random forest in ranger and ordinary kriging (RGFOK)rgokcv
Generate spatial predictions using the hybrid method of random forest in ranger and ordinary kriging (RGOK)rgokpred
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)rgokrgidwcv
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)rgokrgidwpred
Generate spatial predictions using random forest in ranger (RG)rgpred
Relative variable influence based on generalized boosted regression modeling (gbm)rvi
A dataset of sponge species richness in the Timor Sea region, northern Australia marine marginsponge
A dataset of predictors for generating sponge species richness in a selected region in the Timor Sea region, northern Australia marine marginsponge.grid
A dataset of grids for producing spatial predictions of seabed mud content in the southwest Australia Exclusive Economic Zonesw
A dataset of seabed mud content in the southwest Australia Exclusive Economic Zoneswmud
Convert error measures to vecvtovecv
Variance explained by predictive models based on cross-validationvecv