reveal.model.designs

Function returns every full and reduced model for model terms used in 
lm.rrpp fits. This function is useful for revealing 
the null and full model that would be used in the pairwise function, 
if a specific null model is not declared as an argument
(fit.null in the <code>pairwise</code> function).
It also helps to demonstrate how sums of squares and cross-products 
(SSCP) are calculated in lm.rrpp permutations (iterations),
from the difference between fitted values for null and full designs.

analysis

Linear model calculations are made for many random versions of data.
Using residual randomization in a permutation procedure, sums of squares are
calculated over many permutations to generate empirical probability distributions
for evaluating model effects.  This packaged is described by
Collyer & Adams (2018).  Additionally, coefficients, statistics, fitted values, and residuals generated over many
permutations can be used for various procedures including pairwise tests, prediction, classification, and
model comparison.  This package should provide most tools one could need for the analysis of
high-dimensional data, especially in ecology and evolutionary biology, but certainly other fields, as well.

Michael Collyer

RRPP

Linear Model Evaluation with Randomized Residuals in a
Permutation Procedure

Dean Adams

reveal.model.designs function

<dl><dt>fit</dt>
<dd>A linear model fit from <code>lm.rrpp</code>.</dd></dl>

Arguments

Author

Reveal model designs used in lm.rrpp fit — reveal.model.designs

<dl>

<dt>fit</dt>
<dd>A linear model fit from <code>lm.rrpp</code>.</dd>

</dl>

reveal.model.designs: Reveal model designs used in lm.rrpp fit

Description

Usage

Arguments

Author

Examples