focusMEonSubjects

Reduces a plot.measurement.error to a single research subject. This can be 
for cases when many overlapping subjects in a plot obscure interpretation 
for specific subjects.

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

focusMEonSubjects function

<dl><dt>x</dt>
<dd>Plot from <code>plot.measurement.error</code></dd>
<dt>subjects</dt>
<dd>The specific subject to plot</dd>
<dt>shadow</dt>
<dd>A logical value for whether to show other subject values as
shadows of their locations.</dd>
<dt>...</dt>
<dd>Other arguments passed onto plot</dd></dl>

Arguments

Author

Plot Function for RRPP — focusMEonSubjects

<dl>

<dt>x</dt>
<dd>Plot from <code>plot.measurement.error</code></dd>


<dt>subjects</dt>
<dd>The specific subject to plot</dd>


<dt>shadow</dt>
<dd>A logical value for whether to show other subject values as
shadows of their locations.</dd>


<dt>...</dt>
<dd>Other arguments passed onto plot</dd>

</dl>

focusMEonSubjects: Plot Function for RRPP

Description

Usage

Arguments

Author