evaluation_stats

Calculate median and standard deviation of evaluation results for all
candidate models considering cross-validation kfolds.

A set of tools to perform Ecological Niche Modeling with
presence-absence data. It includes algorithms for data partitioning,
model fitting, calibration, evaluation, selection, and prediction.
Other functions help to explore signals of ecological niche using univariate
and multivariate analyses, and model features such as variable response
curves and variable importance. Unique characteristics of this package are
the ability to exclude models with concave quadratic responses, and the
option to clamp model predictions to specific variables. These tools are
implemented following principles proposed in
Cobos et al., (2022) <doi:10.17161/bi.v17i.15985>,
Cobos et al., (2019) <doi:10.7717/peerj.6281>,
and Peterson et al., (2008) <doi:10.1016/j.ecolmodel.2007.11.008>.

Luis F. Arias-Giraldo

enmpa

Ecological Niche Modeling using Presence-Absence Data

Marlon E. Cobos

A. Townsend Peterson

evaluation_stats function

<dl><dt>evaluation_results</dt>
<dd>data.frame model evaluation results. These results
are the output of the function <code>model_validation</code>.</dd>
<dt>bimodal_toexclude</dt>
<dd>(logical) whether models in which binomial variable
response curves were detected will be excluded during selection processes.</dd></dl>

Arguments

Summary of evaluation statistics for candidate models — evaluation_stats

<dl>

<dt>evaluation_results</dt>
<dd>data.frame model evaluation results. These results
are the output of the function <code>model_validation</code>.</dd>


<dt>bimodal_toexclude</dt>
<dd>(logical) whether models in which binomial variable
response curves were detected will be excluded during selection processes.</dd>

</dl>

evaluation_stats: Summary of evaluation statistics for candidate models

Description

Usage

Value

Arguments

Examples