Linear Model Evaluation with Randomized Residuals in a Permutation Procedure

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. 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.

RRPP

RRPP is a software package for evaluating linear models with residual randomization in a permutation procedure. S3 Generic used for the lm function can also be used with lm.rrpp, with the chief difference being that lm coefficients, fitted values, and residuals are estimated many times with random permutations of data.

To install the current RRPP R-package from CRAN:

Within R:

install.packages("RRPP")

To install the current version of the RRPP R-package from Github using devtools:

Within R:

install.packages("pak")

pak::pkg_install("mlcollyer/RRPP")

The version on github is updated regularly, especially if errors or programming bugs are discovered.