toydata_seldvs

Selected derived variables and tables showing forward model selection of
derived variables for distribution modeling of a small, synthetic data set
used in Halvorsen (2013).

datasets

Tools for training, selecting, and evaluating maximum entropy
(and standard logistic regression) distribution models. This package
provides tools for user-controlled transformation of explanatory variables,
selection of variables by nested model comparison, and flexible model
evaluation and projection. It follows principles based on the maximum-
likelihood interpretation of maximum entropy modeling, and uses infinitely-
weighted logistic regression for model fitting. The package is described in
Vollering et al. (2019; <doi:10.1002/ece3.5654>).

Julien Vollering

MIAmaxent

A Modular, Integrated Approach to Maximum Entropy Distribution
Modeling

Sabrina Mazzoni

Rune Halvorsen

Steven Phillips

Michael Bedward

toydata_seldvs function

List with 2 elements:<ol>
<li>A list of 3, with the
 response variable followed by data frames each containing the derived
 variables selected for a given explanatory variable.</li>
<li>A list of the
 response variable and forward model selection trails used to select derived
 variables.</li>
</ol>

Format

Selected derived variables accompanied by selection trails, from toy data. — toydata_seldvs

Selected derived variables accompanied by selection trails, from toy data.

toydata_seldvs: Selected derived variables accompanied by selection trails, from toy data.

Description

Usage

Arguments

Format

References