model.matrix.lm.rrpp

<code>model.matrix.lm.rrpp</code> returns the design matrix constructed for 
an <code>lm.rrpp</code> object.

utilities

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

model.matrix.lm.rrpp function

<dl><dt>object</dt>
<dd>Object from <code>lm.rrpp</code></dd>
<dt>...</dt>
<dd>further arguments passed to or from other methods</dd></dl>

Arguments

Author

Extract the model design matrix from an lm.rrpp object — model.matrix.lm.rrpp

<dl>

<dt>object</dt>
<dd>Object from <code>lm.rrpp</code></dd>


<dt>...</dt>
<dd>further arguments passed to or from other methods</dd>

</dl>

model.matrix.lm.rrpp: Extract the model design matrix from an lm.rrpp object

Description

Usage

Arguments

Author