Automated Boosted Regression Tree Modelling and Mapping Suite
Defines breakpoints for draw.grid and legend.grid; mapplots fork
calibration
gbm.auto: Automated Boosted Regression Tree Modelling and Mapping Suit...
Automated Boosted Regression Tree modelling and mapping suite
Creates Basemaps for Gbm.auto mapping from your data range
Calculates minimum Bag Fraction size for gbm.auto
Conservation Area Mapping
Creates ggplots of marginal effect for factorial variables from plot.g...
Plot linear models for all expvar against the resvar
Calculate Coefficient Of Variation surfaces for gbm.auto predictions
Maps of predicted abundance from Boosted Regression Tree modelling
Maps of predicted abundance from Boosted Regression Tree modelling
Representativeness Surface Builder
Function to assess optimal no of boosting trees using k-fold cross val...
Subset gbm.auto input datasets to 2 groups using the partial deviance ...
Decision Support Tool that generates (Marine) Protected Area options u...
Plot linear model for two variables with R2 & P printed and saved
roc
Automates delta log-normal boosted regression tree abundance prediction. Loops through parameters provided (LR (learning rate), TC (tree complexity), BF (bag fraction)), chooses best, simplifies, & generates line, dot & bar plots, & outputs these & predictions & a report, makes predicted abundance maps, and Unrepresentativeness surfaces. Package core built around 'gbm' (gradient boosting machine) functions in 'dismo' (Hijmans, Phillips, Leathwick & Jane Elith, 2020 & ongoing), itself built around 'gbm' (Greenwell, Boehmke, Cunningham & Metcalfe, 2020 & ongoing, originally by Ridgeway). Indebted to Elith/Leathwick/Hastie 2008 'Working Guide' <doi:10.1111/j.1365-2656.2008.01390.x>; workflow follows Appendix S3. See <https://www.simondedman.com/> for published guides and papers using this package.