Package: spm2 1.1.3

spm2: Spatial Predictive Modeling

An updated and extended version of 'spm' package, by introducing some further novel functions for modern statistical methods (i.e., generalised linear models, glmnet, generalised least squares), thin plate splines, support vector machine, kriging methods (i.e., simple kriging, universal kriging, block kriging, kriging with an external drift), and novel hybrid methods (228 hybrids plus numerous variants) of modern statistical methods or machine learning methods with mathematical and/or univariate geostatistical methods for spatial predictive modelling. For each method, two functions are provided, with one function for assessing the predictive errors and accuracy of the method based on cross-validation, and the other for generating spatial predictions. It also contains a couple of functions for data preparation and predictive accuracy assessment.

Authors:Jin Li [aut, cre]

spm2_1.1.3.tar.gz
spm2_1.1.3.zip(r-4.5)spm2_1.1.3.zip(r-4.4)spm2_1.1.3.zip(r-4.3)
spm2_1.1.3.tgz(r-4.4-any)spm2_1.1.3.tgz(r-4.3-any)
spm2_1.1.3.tar.gz(r-4.5-noble)spm2_1.1.3.tar.gz(r-4.4-noble)
spm2_1.1.3.tgz(r-4.4-emscripten)spm2_1.1.3.tgz(r-4.3-emscripten)
spm2.pdf |spm2.html✨
spm2/json (API)

# Install 'spm2' in R:
install.packages('spm2', repos = c('https://jinli22.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Datasets:
  • bees - A dataset of bees count data and relevant information in oilseed Brassica fields in an Australian temperate landscape.
  • sponge2 - A dataset of sponge species richness in the Timor Sea region, northern Australia marine margin
  • spongelonglat - A dataset of sponge species richness in the Timor Sea region, northern Australia marine margin

On CRAN:

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

35 exports 1.24 score 78 dependencies 2 dependents 2 mentions 2 scripts 303 downloads

Last updated 1 years agofrom:e98b877988. Checks:OK: 7. Indexed: yes.

TargetResultDate
Doc / VignettesOKAug 28 2024
R-4.5-winOKAug 28 2024
R-4.5-linuxOKAug 28 2024
R-4.4-winOKAug 28 2024
R-4.4-macOKAug 28 2024
R-4.3-winOKAug 28 2024
R-4.3-macOKAug 28 2024

Exports:ccrdatasplitdecimaldigitgbmkrigeidwcvgbmkrigeidwpredglmcvglmidwcvglmidwpredglmkrigecvglmkrigeidwcvglmkrigeidwpredglmkrigepredglmnetcvglmpredglscvglsidwcvglsidwpredglskrigecvglskrigeidwcvglskrigeidwpredglskrigepredglspredkrigecvkrigepredrfkrigeidwcvrfkrigeidwpredsvmcvsvmidwcvsvmidwpredsvmkrigecvsvmkrigeidwcvsvmkrigeidwpredsvmkrigepredsvmpredtpscv

Dependencies:abindbiomod2classclassIntclicodetoolscolorspaceDBIdotCall64dplyre1071fansifarverfieldsFNNforeachgbmgenericsggplot2glmnetgluegstatgtableintervalsisobanditeratorsKernSmoothlabelinglatticelifecyclemagrittrmapsMASSMatrixmgcvmunsellnlmepillarpkgconfigplyrPresenceAbsencepROCproxypsyR6randomForestrangerRColorBrewerRcppRcppEigenreshapereshape2rlangrparts2scalessfsftimeshapespspacetimespamspmstarsstringistringrsurvivalterratibbletidyselectunitsutf8vctrsviridisLitewithrwkxtszoo

Readme and manuals

Help Manual

Help pageTopics
A dataset of bees count data and relevant information in oilseed Brassica fields in an Australian temperate landscape.bees
Correct classification rate for predictive models based on cross -validationccr
Note on notescran-comments
Split data for k-fold cross-validationdatasplit
Digit number after decimal point for a numeric variabledecimaldigit
Cross validation, n-fold and leave-one-out for the hybrid methods of generalized boosted regression modeling ('gbm'), 'kriging' and inverse distance weighted ('IDW').gbmkrigeidwcv
Generate spatial predictions using the hybrid methods of generalized boosted regression modeling ('gbm'), 'kriging' and inverse distance weighted ('IDW').gbmkrigeidwpred
Cross validation, n-fold and leave-one-out for generalised linear models ('glm')glmcv
Cross validation, n-fold and leave-one-out for the hybrid method of generalised linear models ('glm') and inverse distance weighted ('IDW') ('glmidw')glmidwcv
Generate spatial predictions using the hybrid method of generalised linear models ('glm') and inverse distance weighted ('IDW') ('glmidw')glmidwpred
Cross validation, n-fold and leave-one-out for the hybrid method of generalised linear models ('glm') and 'krige' ('glmkrige')glmkrigecv
Cross validation, n-fold and leave-one-out for the hybrid methods of generalised linear models ('glm'), 'kriging' and inverse distance weighted ('IDW').glmkrigeidwcv
Generate spatial predictions using the hybrid methods of generalised linear models ('glm'), 'kriging' and inverse distance weighted ('IDW').glmkrigeidwpred
Generate spatial predictions using the hybrid method of generalised linear models ('glm') and 'krige'glmkrigepred
Cross validation, n-fold and leave-one-out, for 'glmnet' in 'glmnet' packageglmnetcv
Generate spatial predictions using generalised linear models ('glm')glmpred
Cross validation, n-fold and leave-one-out for generalized least squares ('gls')glscv
Cross validation, n-fold and leave-one-out for the hybrid method of generalized least squares ('gls') and inverse distance weighted ('idw') (glsidw)glsidwcv
Generate spatial predictions using the hybrid method of generalized least squares ('gls') and inverse distance weighted ('IDW') ('glsidw')glsidwpred
Cross validation, n-fold and leave-one-out for the hybrid method of generalized least squares ('gls') and kriging ('krige') ('glskrige')glskrigecv
Cross validation, n-fold and leave-one-out for the hybrid methods of generalised least squares ('gls'), 'kriging' and inverse distance weighted ('IDW')glskrigeidwcv
Generate spatial predictions using the hybrid methods of generalised least squares ('gls'), 'kriging' and inverse distance weighted ('IDW')glskrigeidwpred
Generate spatial predictions using the hybrid method of generalized least squares ('gls') and kriging ('krige') ('glskrige')glskrigepred
Generate spatial predictions using generalized least squares ('gls')glspred
Cross validation, n-fold and leave-one-out for kriging methods ('krige')krigecv
Generate spatial predictions using kriging methods ('krige')krigepred
Cross validation, n-fold and leave-one-out for the hybrid methods of 'random forest' ('RF'), 'kriging' and inverse distance weighted ('IDW')rfkrigeidwcv
Generate spatial predictions using the hybrid methods of 'random forest' ('RF'), 'kriging' and inverse distance weighted ('IDW').rfkrigeidwpred
A dataset of sponge species richness in the Timor Sea region, northern Australia marine marginsponge2
A dataset of sponge species richness in the Timor Sea region, northern Australia marine marginspongelonglat
Cross validation, n-fold and leave-one-out for support vector machine ('svm')svmcv
Cross validation, n-fold and leave-one-out for the hybrid method of support vector machine ('svm') regression and inverse distance weighted ('IDW') (svmidw)svmidwcv
Generate spatial predictions using the hybrid method of support vector machine ('svm') regression and inverse distance weighted ('IDW') ('svmidw')svmidwpred
Cross validation, n-fold and leave-one-out for the hybrid method of support vector machine ('svm') regression and 'krige' (svmkrige)svmkrigecv
Cross validation, n-fold and leave-one-out for the hybrid methods of support vector machine ('svm') regression , 'kriging' and inverse distance weighted ('IDW').svmkrigeidwcv
Generate spatial predictions using the hybrid methods of support vector machine ('svm') regression , 'kriging' and inverse distance weighted ('IDW').svmkrigeidwpred
Generate spatial predictions using the hybrid method of support vector machine ('svm') regression and 'krige' (svmkrige)svmkrigepred
Generate spatial predictions using support vector machine ('svm')svmpred
Cross validation, n-fold and leave-one-out for thin plate splines ('TPS')tpscv