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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Very low values of explained variance for the first Axes from PCoA

I am comparing different sites based on their floristic composition in R. Therefore, I have created a huge community datamatrix (presence/absence data) from 53 sites including over 1000 species. To ...
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Is PCA suitable when there are continuous variables with many zeros?

I am dealing with a database where frequencies of behaviors are recorded, thus being continuous data but with many zeros. Aiming to reduce the dimensions of the seven variables, I have carried out a ...
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Which is the 'best' method to detect outliers from a PCoA analysis?

I'm trying to find a suitable method for detection of outliers from a PCoA output. This analysis is used to visualize the results from a distance matrix between a set of sample applying the Chemical ...
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Jaccard/binary (dis)similarity calculation to multidimensional scaling analysis

I have n N x n dataset of features (n) and subjects (N) in which I am attempting to cluster into a lower-dimensional space via multi-dimensional scaling. I'm confused about which MDS setup I need to ...
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What type of PCA to run with only two environmental variables and 1 factor?

I study soil insects, and sample monthly for insects. Each month, I sample at 8 different sites. Each site is divided roughly into 4 meter square quadrants. From each quadrant, I pick out a random sub-...
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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 (...
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173 views

Does a PCoA (or MDS) assume normality of the variables behind distances?

More precisely, if I conduct a cmdscale (classical multidimensional scaling) on an Euclidean distance matrix by considering $n$ observations of $p$ variables i.e. $D_{ij}=\sqrt{ \sum_p (x_{ip} - x_{...
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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}, ...
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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 ...
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2k views

How does Principal Coordinate Analysis (PCoA) work, as compared to PCA? [duplicate]

I am familiar with PCA from Making sense of principal component analysis, eigenvectors & eigenvalues where you either normalize the data (to standard normal or ...
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703 views

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 ...
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594 views

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 ...
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Representing experimental data by unconstrained ordination: PCA, PCoA, or NMDS?

I have a dataset composed by presence of different bacterial families in function at different pesticide treatment. I need to find a good representation of my data but I don't know which method (...
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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 ...
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1k views

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 ...
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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 ...
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9k views

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 ...
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608 views

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 ...
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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?