RRPP-package {RRPP}

RRPP: Linear Model Evaluation with Randomized Residuals in a Permutation Procedure

Description

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.

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.

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.

Value

Key functions for this package:

\link{lm.rrpp}

Fits linear models, using RRPP. plus model comparisons.

\link{coef.lm.rrpp}

Extract coefficients or perform test on coefficients, using RRPP.

\link{predict.lm.rrpp}

Predict values from lm.rrpp fits and generate bootstrapped confidence intervals.

\link{pairwise}

Perform pairwise tests, based on lm.rrpp model fits.

Author(s)

Maintainer: Michael Collyer [e-mail] (ORCID)

Authors:

• Dean Adams (ORCID)

Michael Collyer and Dean Adams

See Also

Useful links:

• https://github.com/mlcollyer/RRPP