aPCoA: Covariate Adjusted PCoA Plot

In fields such as ecology, microbiology, and genomics, non-Euclidean distances are widely applied to describe pairwise dissimilarity between samples. Given these pairwise distances, principal coordinates analysis (PCoA) is commonly used to construct a visualization of the data. However, confounding covariates can make patterns related to the scientific question of interest difficult to observe. We provide 'aPCoA' as an easy-to-use tool to improve data visualization in this context, enabling enhanced presentation of the effects of interest. Details are described in Yushu Shi, Liangliang Zhang, Kim-Anh Do, Christine Peterson and Robert Jenq (2020) <arXiv:2003.09544>.

Version: 1.0
Imports: vegan, randomcoloR, mvabund, ape, car, cluster
Published: 2020-03-25
Author: Yushu Shi
Maintainer: Yushu Shi <shiyushu2006 at gmail.com>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: no
CRAN checks: aPCoA results

Downloads:

Reference manual: aPCoA.pdf
Package source: aPCoA_1.0.tar.gz
Windows binaries: r-devel: aPCoA_1.0.zip, r-devel-gcc8: aPCoA_1.0.zip, r-release: aPCoA_1.0.zip, r-oldrel: aPCoA_1.0.zip
OS X binaries: r-release: aPCoA_1.0.tgz, r-oldrel: not available

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