Learn R Programming

spm2 (version 1.1.3)

Spatial Predictive Modeling

Description

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.

Copy Link

Version

Install

install.packages('spm2')

Monthly Downloads

227

Version

1.1.3

License

GPL (>= 2)

Maintainer

Jin Li

Last Published

April 6th, 2023

Functions in spm2 (1.1.3)

Digit number after decimal point for a numeric variable

Cross validation, n-fold and leave-one-out for the hybrid method of generalised linear models ('glm') and inverse distance weighted ('IDW') ('glmidw')

Cross validation, n-fold and leave-one-out for generalised linear models ('glm')

Split data for k-fold cross-validation

Cross validation, n-fold and leave-one-out for the hybrid methods of generalized boosted regression modeling ('gbm'), 'kriging' and inverse distance weighted ('IDW').

Generate spatial predictions using the hybrid method of generalised linear models ('glm') and inverse distance weighted ('IDW') ('glmidw')

gbmkrigeidwpred

Generate spatial predictions using the hybrid methods of generalized boosted regression modeling ('gbm'), 'kriging' and inverse distance weighted ('IDW').

A dataset of bees count data and relevant information in oilseed Brassica fields in an Australian temperate landscape.

Correct classification rate for predictive models based on cross -validation

glmkrigeidwpred

Generate spatial predictions using the hybrid methods of generalised linear models ('glm'), 'kriging' and inverse distance weighted ('IDW').

Cross validation, n-fold and leave-one-out for the hybrid method of generalised linear models ('glm') and 'krige' ('glmkrige')

Cross validation, n-fold and leave-one-out for the hybrid method of generalized least squares ('gls') and inverse distance weighted ('idw') (glsidw)

Cross validation, n-fold and leave-one-out, for 'glmnet' in 'glmnet' package

Generate spatial predictions using the hybrid method of generalized least squares ('gls') and inverse distance weighted ('IDW') ('glsidw')

Cross validation, n-fold and leave-one-out for the hybrid method of generalized least squares ('gls') and kriging ('krige') ('glskrige')

Cross validation, n-fold and leave-one-out for generalized least squares ('gls')

Generate spatial predictions using the hybrid method of generalised linear models ('glm') and 'krige'

Cross validation, n-fold and leave-one-out for the hybrid methods of generalised linear models ('glm'), 'kriging' and inverse distance weighted ('IDW').

Generate spatial predictions using generalised linear models ('glm')

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

Cross validation, n-fold and leave-one-out for kriging methods ('krige')

glskrigeidwpred

Generate spatial predictions using the hybrid methods of generalised least squares ('gls'), 'kriging' and inverse distance weighted ('IDW')

Cross validation, n-fold and leave-one-out for the hybrid methods of 'random forest' ('RF'), 'kriging' and inverse distance weighted ('IDW')

Cross validation, n-fold and leave-one-out for the hybrid methods of generalised least squares ('gls'), 'kriging' and inverse distance weighted ('IDW')

Generate spatial predictions using the hybrid method of generalized least squares ('gls') and kriging ('krige') ('glskrige')

Generate spatial predictions using kriging methods ('krige')

Generate spatial predictions using the hybrid methods of 'random forest' ('RF'), 'kriging' and inverse distance weighted ('IDW').

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

Generate spatial predictions using generalized least squares ('gls')

svmkrigeidwpred

Generate spatial predictions using the hybrid methods of support vector machine ('svm') regression , 'kriging' and inverse distance weighted ('IDW').

Cross validation, n-fold and leave-one-out for thin plate splines ('TPS')

Generate spatial predictions using the hybrid method of support vector machine ('svm') regression and inverse distance weighted ('IDW') ('svmidw')

Cross validation, n-fold and leave-one-out for the hybrid methods of support vector machine ('svm') regression , 'kriging' and inverse distance weighted ('IDW').

Cross validation, n-fold and leave-one-out for the hybrid method of support vector machine ('svm') regression and 'krige' (svmkrige)

Generate spatial predictions using the hybrid method of support vector machine ('svm') regression and 'krige' (svmkrige)

Cross validation, n-fold and leave-one-out for the hybrid method of support vector machine ('svm') regression and inverse distance weighted ('IDW') (svmidw)

Generate spatial predictions using support vector machine ('svm')

Cross validation, n-fold and leave-one-out for support vector machine ('svm')