gbm.rsb

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.

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.

Simon Dedman

gbm.auto

Automated Boosted Regression Tree Modelling and Mapping Suite

gbm.rsb function

<dl><dt>samples</dt>
<dd>Data frame with response and explanatory variables.</dd>
<dt>grids</dt>
<dd>Data frame of (more/different) explanatory variables and no
response variable, to be predicted to by gbm.predict.grids.</dd>
<dt>expvarnames</dt>
<dd>Vector of column names of explanatory variables being
tested. Can be length 1. Names must match in samples and grids.</dd>
<dt>gridslat</dt>
<dd>Column number for latitude in 'grids'.</dd>
<dt>gridslon</dt>
<dd>Column number for longitude in 'grids'.</dd></dl>

Arguments

Simon Dedman, <a href="/link/simondedman%40gmail.com?package=gbm.auto&version=2024.10.01" data-mini-rdoc="gbm.auto::simondedman@gmail.com">simondedman@gmail.com</a>

Author

Representativeness Surface Builder — gbm.rsb

<dl>

<dt>samples</dt>
<dd>Data frame with response and explanatory variables.</dd>


<dt>grids</dt>
<dd>Data frame of (more/different) explanatory variables and no
response variable, to be predicted to by gbm.predict.grids.</dd>


<dt>expvarnames</dt>
<dd>Vector of column names of explanatory variables being
tested. Can be length 1. Names must match in samples and grids.</dd>


<dt>gridslat</dt>
<dd>Column number for latitude in 'grids'.</dd>


<dt>gridslon</dt>
<dd>Column number for longitude in 'grids'.</dd>

</dl>

Simon Dedman, <a href='mailto:simondedman@gmail.com'>simondedman@gmail.com</a>

gbm.rsb: Representativeness Surface Builder

Description

Usage

Value

Arguments

Author

Examples