Automated Boosted Regression Tree Modelling and Mapping Suite

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Documentation for package ‘gbm.auto’ version 2023.08.31

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Adult_Females Data: Numbers of 4 adult female rays caught in 2137 Irish Sea trawls, 1994 to 2014
AllPreds_E Data: Predicted abundances of 4 ray species generated using gbm.auto
AllScaledData Data: Scaled abundance data for 2 subsets of 4 rays in the Irish Sea, by gbm.cons
breaks.grid Defines breakpoints for draw.grid and legend.grid; mapplots fork
calibration calibration
gbm.auto Automated Boosted Regression Tree modelling and mapping suite
gbm.basemap Creates Basemaps for Gbm.auto mapping from your data range
gbm.bfcheck Calculates minimum Bag Fraction size for gbm.auto
gbm.cons Conservation Area Mapping
gbm.factorplot Creates ggplots of marginal effect for factorial variables from plot.gbm in gbm.auto.
gbm.lmplots Plot linear models for all expvar against the resvar
gbm.loop Calculate Coefficient Of Variation surfaces for gbm.auto predictions
gbm.map Maps of predicted abundance from Boosted Regression Tree modelling
gbm.mapsf Maps of predicted abundance from Boosted Regression Tree modelling
gbm.rsb Representativeness Surface Builder
gbm.step.sd Function to assess optimal no of boosting trees using k-fold cross validation
gbm.subset Subset gbm.auto input datasets to 2 groups using the partial deviance plots
gbm.valuemap Decision Support Tool that generates (Marine) Protected Area options using species predicted abundance maps
grids Data: Explanatory variables for rays in the Irish Sea
Juveniles Data: Explanatory and response variables for 4 juvenile rays in the Irish Sea
lmplot Plot linear model for two variables with R2 & P printed and saved
roc roc
samples Data: Numbers of 4 ray species caught in 2137 Irish Sea trawls, 1994 to 2014