Package: gbm.auto 2024.02.06

gbm.auto: Automated Boosted Regression Tree Modelling and Mapping Suite

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 <http://www.simondedman.com/> for published guides and papers using this package.

Authors:Simon Dedman [aut, cre]

gbm.auto_2024.02.06.tar.gz
gbm.auto_2024.02.06.zip(r-4.5)gbm.auto_2024.02.06.zip(r-4.4)gbm.auto_2024.02.06.zip(r-4.3)
gbm.auto_2024.02.06.tgz(r-4.4-any)gbm.auto_2024.02.06.tgz(r-4.3-any)
gbm.auto_2024.02.06.tar.gz(r-4.5-noble)gbm.auto_2024.02.06.tar.gz(r-4.4-noble)
gbm.auto_2024.02.06.tgz(r-4.4-emscripten)gbm.auto_2024.02.06.tgz(r-4.3-emscripten)
gbm.auto.pdf |gbm.auto.html✨
gbm.auto/json (API)
NEWS

# Install 'gbm.auto' in R:
install.packages('gbm.auto', repos = c('https://simondedman.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Bug tracker:https://github.com/simondedman/gbm.auto/issues

Datasets:
  • 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
  • Juveniles - Data: Explanatory and response variables for 4 juvenile rays in the Irish Sea
  • grids - Data: Explanatory variables for rays in the Irish Sea
  • samples - Data: Numbers of 4 ray species caught in 2137 Irish Sea trawls, 1994 to 2014

On CRAN:

16 exports 17 stars 6.33 score 94 dependencies 250 mentions 11 scripts 309 downloads

Last updated 2 months agofrom:bd927f0377. Checks:OK: 3 NOTE: 4. Indexed: yes.

TargetResultDate
Doc / VignettesOKSep 03 2024
R-4.5-winNOTESep 03 2024
R-4.5-linuxNOTESep 03 2024
R-4.4-winNOTESep 03 2024
R-4.4-macNOTESep 03 2024
R-4.3-winOKSep 03 2024
R-4.3-macOKSep 03 2024

Exports:breaks.gridgbm.autogbm.basemapgbm.bfcheckgbm.consgbm.factorplotgbm.lmplotsgbm.loopgbm.mapgbm.mapsfgbm.rsbgbm.step.sdgbm.subsetgbm.valuemaplmplotroc

Dependencies:abindaskpassaudiobeeprBHbitbit64bitopsclassclassIntclicliprcolorspacecpp11crayoncurldata.tableDBIdigestdismodplyre1071fansifarvergbmgenericsggmapggplot2ggspatialgluegridExtragtablehmshttrisobandjpegjsonliteKernSmoothlabelinglatticelifecyclelubridatemagrittrmapplotsMASSMatrixMetricsmgcvmimemunsellnabornlmenngeoopensslpillarpkgconfigplyrpngprettyunitsprogressproxypurrrR6rasterRColorBrewerRcppRcppEigenreadrrlangrosms2scalessfspstarsstarsExtrastringistringrsurvivalsysterratibbletidyrtidyselecttimechangetzdbunitsutf8vctrsviridisviridisLitevroomwithrwk

Readme and manuals

Help Manual

Help pageTopics
Data: Numbers of 4 adult female rays caught in 2137 Irish Sea trawls, 1994 to 2014Adult_Females
Data: Predicted abundances of 4 ray species generated using gbm.autoAllPreds_E
Data: Scaled abundance data for 2 subsets of 4 rays in the Irish Sea, by gbm.consAllScaledData
Defines breakpoints for draw.grid and legend.grid; mapplots forkbreaks.grid
calibrationcalibration
Automated Boosted Regression Tree modelling and mapping suitegbm.auto
Creates Basemaps for Gbm.auto mapping from your data rangegbm.basemap
Calculates minimum Bag Fraction size for gbm.autogbm.bfcheck
Conservation Area Mappinggbm.cons
Creates ggplots of marginal effect for factorial variables from plot.gbm in gbm.auto.gbm.factorplot
Plot linear models for all expvar against the resvargbm.lmplots
Calculate Coefficient Of Variation surfaces for gbm.auto predictionsgbm.loop
Maps of predicted abundance from Boosted Regression Tree modellinggbm.map
Maps of predicted abundance from Boosted Regression Tree modellinggbm.mapsf
Representativeness Surface Buildergbm.rsb
Function to assess optimal no of boosting trees using k-fold cross validationgbm.step.sd
Subset gbm.auto input datasets to 2 groups using the partial deviance plotsgbm.subset
Decision Support Tool that generates (Marine) Protected Area options using species predicted abundance mapsgbm.valuemap
Data: Explanatory variables for rays in the Irish Seagrids
Data: Explanatory and response variables for 4 juvenile rays in the Irish SeaJuveniles
Plot linear model for two variables with R2 & P printed and savedlmplot
rocroc
Data: Numbers of 4 ray species caught in 2137 Irish Sea trawls, 1994 to 2014samples