RRPP: An r package for fitting linear models to high‐dimensional data using residual randomization

• Published in: Methods in ecology and evolution Vol. 9; no. 7; pp. 1772 - 1779
• Main Authors: Collyer, Michael L., Adams, Dean C., Freckleton, Robert
• Format: Journal Article
• Language: English
• Published: London John Wiley & Sons, Inc 01-07-2018
• Subjects: Coefficients; Data processing; dissimilarity; Downstream effects; Empirical analysis; generalized least‐squares; high‐dimensional data; Mathematical models; Matrix methods; multivariate; Multivariate analysis; Permutations; Randomization; Statistical analysis; Statistical tests; Variance analysis
• ISSN: 2041-210X; 2041-210X
• DOI: 10.1111/2041-210X.13029

Residual randomization in permutation procedures (RRPP) is an appropriate means of generating empirical sampling distributions for ANOVA statistics and linear model coefficients, using ordinary or generalized least‐squares estimation. This is an especially useful approach for high‐dimensional (multivariate) data.
Here, we present an r package that provides a comprehensive suite of tools for applying RRPP to linear models. Important available features include choices for OLS or GLS coefficient estimation, data or dissimilarity matrix analysis capability, choice among types I, II, or III sums of squares and cross‐products, various effect size estimation methods, and an ability to perform mixed‐model ANOVA.
The lm.rrpp function is similar to the lm function in many regards, but provides coefficient and ANOVA statistics estimates over many random permutations. The S3 generic functions commonly used with lm also work with lm.rrpp. Additionally, a pairwise function provides statistical tests for comparisons of least‐squares means or slopes, among designated groups. Users have many options for varying random permutations. Compared to similar available packages and functions, RRPP is extremely fast and yields comprehensive results for downstream analyses and graphics, following model fits with lm.rrpp.
The RRPP package facilitates analysis of both univariate and multivariate response data, even when the number of variables exceeds the number of observations.