Linear Model Evaluation with Randomized Residual Permutation Procedures
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.
Key functions for this package:
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Fits linear models, using RRPP. |
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ANOVA on linear models, using RRPP, plus model comparisons. |
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Extract coefficients or perform test on coefficients, using RRPP. |
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Predict values from lm.rrpp fits and generate bootstrapped confidence intervals. |
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Perform pairwise tests, based on lm.rrpp model fits. |
Michael Collyer and Dean Adams
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