R: Spatial Predictive Modeling

# Spatial Predictive Modeling

## Documentation for package ‘spm2’ version 1.1.3

- DESCRIPTION file

## Help Pages

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