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Automatically runs numerous processes from R packages ‘gbm’ and ‘dismo’ and script ‘gbm.utils.R’ which contains Elith et al.’s functions: roc, calibration, and gbm.predict.grids, as well as running my packages gbm.bfcheck, gbm.basemap, gbm.map, gbm.rsb, gbm.cons, gbm.valuemap, and gbm.loop.
Also see each script’s Details section in the manual pages, as these frequently contain tips or common bugfixes.
I strongly recommend that you download papers 1 to 5 (or just the doctoral thesis) on https://www.simondedman.com, with emphasis on P4 (the guide) and P1 (statistical background). Elith et al 2008 (https://besjournals.onlinelibrary.wiley.com/doi/10.1111/j.1365-2656.2008.01390.x) is also strongly recommended. Also it’s imperative you read the R help files for each function before you use them. In RStudio: Packages tab, scroll to gbm.auto, click its name, the click the function to see its man (manual) page. Read the whole thing. Function man pages can also be accessed from the console by typing
?functionJust because you CAN try every conceivable combination of tc, lr, bf, all, at once doesn’t mean you should. Try a range of lr in shrinking orders of magnitude from 0.1 to 0.000001, find the best, THEN try tc c(2, n.expvars), find the best THEN bf c(0.5, 0.75, 0.9) and then in between if either outperform 0.5.
Automated Boosted Regression Tree modelling and mapping suite
Automates delta log normal boosted regression trees abundance prediction. Loops through all permutations of parameters provided (learning rate, tree complexity, bag fraction), chooses the best, then simplifies it. Generates line, dot and bar plots, and outputs these and the predictions and a report of all variables used, statistics for tests, variable interactions, predictors used and dropped, etc. If selected, generates predicted abundance maps, and Unrepresentativeness surfaces.
Calculates minimum Bag Fraction size for gbm.auto
Provides minimum bag fractions for gbm.auto, preventing failure due to bf & samples rows limit.
Creates Basemaps for Gbm.auto mapping from your data range
Downloads unzips crops & saves NOAAs global coastline shapefiles to user-set box. Use for ‘shape’ in gbm.map. If downloading in RStudio uncheck “Use secure download method for HTTP” in Tools > Global Options > Packages.
Maps of predicted abundance from Boosted Regression Tree modelling
Generates maps from the outputs of gbm.step then gbm.predict.grids, handled automatically within gbm.auto but can be run alone, and generates representativeness surfaces from the output of gbm.rsb.
Representativeness Surface Builder
Loops through explanatory variables comparing their histogram in ‘samples’ to their histogram in ‘grids’ to see how well the explanatory variable range in samples represents the range being predicted to in grids. Assigns a representativeness score per variable per site in grids, and takes the average score per site if there’s more than 1 expvar. Saves this to a CSV; it’s plotted by gbm.map if called in gbm.auto. This shows you which areas have the most and least representative coverage by samples, therefore where you can have the most/least confidence in the predictions from gbm.predict.grids. Can be called directly, and choosing a subset of expvars allows one to see their individual / collective representativeness.
Conservation Area Mapping
Runs gbm.auto for multiple subsets of the same overall dataset and scales the combined results, leading to maps which highlight areas of high conservation importance for multiple species in the same study area e.g. using juvenile and adult female subsets to locate candidate nursery grounds and spawning areas respectively.
Decision Support Tool that generates (Marine) Protected Area options using species predicted abundance maps
Scales response variable data, maps a user-defined explanatory variable to be avoided, e.g. fishing effort, combines them into a map showing areas to preferentially close. Bpa, the precautionary biomass required to protect the spawning stock, is used to calculate MPA size. MPA is then grown to add subsequent species starting from the most conservationally at-risk species, resulting in one MPA map per species, and a multicolour MPA map of all. All maps list the percentage of the avoid-variables total that is overlapped by the MPA in the map legend.
Calculate Coefficient Of Variation surfaces for gbm.auto predictions
Processes a user-specified number of loops through the same gbm.auto parameter combinations and calculates the Coefficient Of Variation in the predicted abundance scores for each site aka cell. This can be mapped to spatially demonstrate the output variance range.
ggplot-based update to PDP for factorial/categorical/character variables, allows changing order of categorical variables, and changing angle of x-axis labels to avoid them being cut off.
Linear plot of two variables.
Loops through lmplots for all expvars (x) against the same resvar (y).
Internal functions authored by Elith & Leathwick, used by gbm.auto.R
Local copy of dismo’s gbm.step, with added functions to generate model evaluation metrics such as root mean squared error and amount of deviance explained relative to null.
You can install the released version of gbm.auto from CRAN with:
install.packages("gbm.auto")And the development version from GitHub with:
# install.packages("devtools")
remotes::install_github("SimonDedman/gbm.auto")(See each function’s help file for specific examples, and the documents listed above)
See GitHub issues section https://github.com/SimonDedman/gbm.auto/issues Feel free to contribute to this!
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.