calibration

Internal use only. Jane Elith/John Leathwick 17th March 2005. Calculates
calibration statistics for either binomial or count data but the family
argument must be specified for the latter a conditional test for the latter
will catch most failures to specify the family.

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

calibration function

<dl><dt>obs</dt>
<dd>Observed data.</dd>
<dt>preds</dt>
<dd>Predicted data.</dd>
<dt>family</dt>
<dd>Statistical distribution family. Choose one.</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

calibration — calibration

<dl>

<dt>obs</dt>
<dd>Observed data.</dd>


<dt>preds</dt>
<dd>Predicted data.</dd>


<dt>family</dt>
<dd>Statistical distribution family. Choose one.</dd>

</dl>

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

calibration: calibration

Description

Usage

Value

Arguments

Author