ct.possible.models

Count all combinations of predictor variables in a multivariate regression model.

Perform multivariate modeling of evolved traits, with special attention to
understanding the interplay of the multi-factorial determinants of their origins
in complex ecological settings (Stephens, 2007 <doi:10.1016/j.tree.2006.12.003>).
This software primarily concentrates on phylogenetic regression analysis, enabling
implementation of tree transformation averaging and visualization functionality.
Functions additionally support information theoretic approaches
(Grueber, 2011 <doi:10.1111/j.1420-9101.2010.02210.x>;
Garamszegi, 2011 <doi:10.1007/s00265-010-1028-7>)
such as model averaging and selection of phylogenetic models.
Accessory functions are also implemented for coef standardization (Cade 2015),
selection uncertainty, and variable importance (Burnham & Anderson 2000).
There are other numerous functions for visualizing confounded variables,
plotting phylogenetic trees, as well as reporting and exporting modeling results.
Lastly, as challenges to ecology are inherently multifarious, and therefore often
multi-dataset, this package features several functions to support the identification,
interpolation, merging, and updating of missing data and outdated nomenclature.

David Schruth

mmodely

Modeling Multivariate Origins Determinants - Evolutionary
Lineages in Ecology

ct.possible.models function

<dl><dt>q</dt>
<dd>number of predictor variables</dd></dl>

Arguments

Count all possible model combinations — ct.possible.models

<dl>

<dt>q</dt>
<dd>number of predictor variables</dd>

</dl>

ct.possible.models: Count all possible model combinations

Description

Usage

Value

Arguments

Examples