RRPP-package

RRPP

<p>Functions in this package allow one to evaluate linear models 
with residual randomization.
The name, "RRPP", is an acronym for, "Randomization of Residuals in a 
Permutation Procedure."  Through
the various functions in this package, one can use randomization of 
residuals to generate empirical probability
distributions for linear model effects, for high-dimensional data or 
distance matrices.</p>
<p>An especially useful option of this package is to fit models with 
either ordinary or generalized
least squares estimation (OLS or GLS, respectively), using theoretic 
covariance matrices.  Mixed linear
effects can also be evaluated.</p>

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) <doi:10.1111/2041-210X.13029>.  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-package: Linear Model Evaluation with Randomized Residual Permutation Procedures

Description

Arguments

Value