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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Accounting for 0 observations in presence/absence community data, PCoA/PCA: is this idea credible?
I have a solution in mind for this problem, but I'm not sure if it is defensible (which is why I'm asking you all!). I have a data frame where each row represents an individual site, each column ...
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What do the % variances mean on a Bray curtis dissimilarity plot?
I'm new to microbiome data analysis but what do the percentages on the Axis mean? This plot is comparing two groups (SPF n=8 and ABX n= 4 mice). I can't figure out what these mean. I thought the axis ...
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PCoA plots in R on presence/absence community data: rows filled with zeros give an error
I am currently making PCoA plots on Presence/Absence community data. My rows are populated with samples and my column headings are different taxa that were detected. However, since the experiment is a ...
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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 ...
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How to perform PCA analysis on space-time data for few species in R? [closed]
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 ...
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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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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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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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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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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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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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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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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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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?
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