print.predict.lm.rrpp

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

print.predict.lm.rrpp function

<dl><dt>x</dt>
<dd>Object from <code>predict.lm.rrpp</code></dd>
<dt>PC</dt>
<dd>Logical argument for whether to use predicted values 
rotated to their PCs</dd>
<dt>...</dt>
<dd>Other arguments passed onto predict.lm.rrpp</dd></dl>

Arguments

Author

Print/Summary Function for RRPP — print.predict.lm.rrpp

<dl>

<dt>x</dt>
<dd>Object from <code>predict.lm.rrpp</code></dd>


<dt>PC</dt>
<dd>Logical argument for whether to use predicted values 
rotated to their PCs</dd>


<dt>...</dt>
<dd>Other arguments passed onto predict.lm.rrpp</dd>

</dl>

print.predict.lm.rrpp: Print/Summary Function for RRPP

Description

Usage

Arguments

Author