summary.pairwise

Object from <code><a rd-options="" href="/link/pairwise?package=RRPP&version=0.6.1" data-mini-rdoc="RRPP::pairwise">pairwise</a></code>

object

Logical argument for whether results should be returned in one table 
(if TRUE) or separate pairwise tables (if FALSE)

stat.table

Whether distances or vector correlations between vectors or variances (dispersion of residuals)
should be used in the test.

test.type

If test.type = "VC", whether angle results are expressed in radians or degrees.

angle.type

Confidence level to use for upper confidence limit; default = 0.95 (alpha = 0.05)

confidence

Logical value to indicate whether vectors should be printed.

show.vectors

Other arguments passed onto pairwise

utilities

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

Linear Model Evaluation with Randomized Residuals in a
Permutation Procedure

Dean Adams

summary.pairwise function

Object from <code><a rd-options='' href='pairwise'>pairwise</a></code>

summary.pairwise: Print/Summary Function for RRPP

Description

Usage

Arguments