Re: [R] testing whether clusters in a PCA plot are significantly different from one another

Dear Micheal So I would be much better off just reporting the PCA as is and conclude what i can from plot cheers Julian Julian R. Marchesi Deputy Director and Professor of Clinical Microbiome Research at the Centre for Digestive and Gut Health, Imperial College London, London W2 1NY Tel: +4

Re: [R] testing whether clusters in a PCA plot are significantly different from one another

Significance tests for group differences in a MANOVA of lm(cbind(pc1, pc2) ~ group) will get you what you want, but you are advised DON'T DO THIS, at least without a huge grain of salt and a slew of mea culpas. Otherwise, you are committing p-value abuse and contributing to the notion that sign

Re: [R] testing whether clusters in a PCA plot are significantly different from one another

, Julian; r-help@r-project.org Subject: RE: [R] testing whether clusters in a PCA plot are significantly different from one another In that case you should be able to use manova where pc1 and pc2 are the independent (response) variables and group (Baseline, HFD+P, HFD) is the dependent (explanatory

Re: [R] testing whether clusters in a PCA plot are significantly different from one another

: Marchesi, Julian [mailto:j.march...@imperial.ac.uk] Sent: Friday, January 6, 2017 9:02 AM To: David L Carlson Subject: Re: [R] testing whether clusters in a PCA plot are significantly different from one another Dear David The clusters are defined by the metadata which tells R where to draw the lines

[R] testing whether clusters in a PCA plot are significantly different from one another

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