Statistical Analysis Stack Exchange
Statistical Analysis Stack Exchange

Questions tagged [pcoa]

Principal Coordinate analysis (PCoA), aka Torgerson's metric multidimensional scaling, is the oldest form of Multidimensional Scaling (MDS). Its algorithm is based on Principal Component analysis (PCA).

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What's the difference between principal component analysis and multidimensional scaling?

How are PCA and classical MDS different? How about MDS versus nonmetric MDS? Is there a time when you would prefer one over the other? How do the interpretations differ?
Stephen Turner
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Appropriate negative eigenvalue correction for PCoA of genetic distances

I am trying to find the best way to represent genetic distances in a plane so that they may use them as response variables in canonical redundancy analysis (using rda() in vegan). While there are admittedly many genetic distances to choose from,…
Xavier GB
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How to interpret variation explained by principal coordinates?

I have recently seen a couple of Principal Coordinates Analysis (PCoA) projection plots which show "percentage variation explained" by the respective principal coordinates. Given that the analysis is not done on a co-variance matrix (it is usually…
Paul Igor Costea
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Meaningful inference about data structure based on components with low variance in PCA

A lot of microbiome (microbial ecology) papers that I have come across use either principal component analysis (PCA) or principal coordinate analysis (PCoA) to make conclusions about the data. A lot of these claims are based upon…
skeletor_999
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Why do PCA and PCoA give the same components but different explained variances?

I'm quite familiar with Principal Component Analysisis, as I use it to study genetic structure. Lately, I was revisiting some of the functions I was using in R (pcoa() from the ape package and prcomp()) and I realized they don't give the same…
Athe
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Using metric MDS with non-metric distances and assessing the fit quality

I'm going to perform MDS by means of cmdscale function of standard R library. I spent several hours googling it and finally have a number of questions (some of them more general, some could be more specific to its implementations in R ). I have a…
Denis
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At what spatial scale should PCA be analysed on? Why do the loadings appear so different at each scale?

My dataset has 6 sites. Each site has four quadrants (qi) that I sampled for 12 months to estimate species abundances. I Hellinger transformed the data prior to the analysis. For each quadrant I have environment data - temperature, salinity, pH etc.…
Biotechgeek
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Can I rotate a (classical) MDS result with varimax etc.?

I have a matrix of (scaled) co-occurence counts which I would like to summarise using (classical, i.e. PCA-related) Multi-Dimensional Scaling (MDS), and then rotate (with varimax(), quartimax(), or really any other optimisation routine). Let Azra be…
v.kasto
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Out-of-sample embedding into principal coordinate space

I'm trying to project a point into an existing PCOA (Principal Coordinates Analysis) space (in R). I am under the impression this must be possible, but I can't figure out how to go about it. Here's how far I've gotten (a toy example): x <- c(1:10) y…
Paul Igor Costea
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Contribution of variables on axis in PCoA

I am trying to analyze data using Principal Coordinates Analysis (Classical Multidimensional Scaling (CMDS)) in R. I've tried some different ways (i.e., pcoa {ape}, cmdscale, capscale {vegan}). I've found that the results are the same and the plots…
Terka Dračková
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How does Principal Coordinate Analysis (PCoA) work, as compared to PCA?

I am familiar with PCA from Making sense of principal component analysis, eigenvectors & eigenvalues where you either normalize the data (to standard normal or centered?) and construct a covariance matrix or just construct the correlation matrix,…
O.rka
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Negative eigenvalues in principal coordinates analysis

In principal coordinates analysis with the presence of negative eigenvalues, what's the best way to calculate the percentage of variation explained by each principal coordinate? Does it make sense to consider only the principal coordinates…
Paul L
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Calculate PCoA scores for dataframe "x", based on the distance matrix of dataframe "y"

I'm trying to use multivariate techniques to compare two datasets (same structure) that were collected using different sampling techniques. I'd like to compute a PCoA for the first dataset (D1), and then see how the data from dataset 2 (D2)…
Jeff
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How to perform PCA analysis on space-time data for few species in R?

The data-set I'm looking to analyze has 6 sites. Each site was sampled at five unique locations each month for a year. We could identify abundance of 4 species across the data set. Together, I have 360 points in space-time of 4 species. I also have…
Biotechgeek
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Using Principal Coordinates scores in subsequent analyses

If I do PCoA on a dataset, can I use these scores in subsequent analyses? My understanding is that Using PCA scores in subsequent regression is valid. However, it seems like this doesn't hold for NMDS scores. However, it seems also seems like PCA is…
Jacob Weverka
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