# Automated Boosted Regression Tree Modelling and Mapping Suite

## Documentation for package ‘gbm.auto’ version 2023.08.31

- DESCRIPTION file.
- Package NEWS.

## Help Pages

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