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Technique that renders observed or computed (dis)similarities among objects into distances in a low-dimensional space (usually Euclidean). It thus constructs dimensions for the data; the objects can be plotted and conceptualized in those dimensions

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I am currently reading https://arxiv.org/pdf/2305.10869 , in this paper they have used a modified stress function , basically the weight values are 1 if the distance value in the matrix is known and 0 ...
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I am interested in the following problem; say I have a bunch of continuous and categorical variables. I wish to compute dissimilarities among my observations by considering kernel functions. In this ...
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In multidimensional scaling (MDS), the goal is to find a transformation of a set of data points $x_1,\ldots,x_n$ with $x_i\in\mathbb{R}^p$ into the lower dimensional space $\mathbb{R}^k$ (e.g., for ...
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I sampled spiders in 5 different forest-stands. I originally had a sample size of 32 per forest-type, but as I had to pool the data I now just have 8 samples per forest type. I did an ordination with ...
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I have seen such a model in publications many times, which I created on my own data. It is a model of three latent features based on observable variables (likert scales) which also has a superior ...
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Suppose that you observe two variables $X, Y$ (regressor and regressand) that are statistically associated, $Y \sim X$. Your data are iid samples $\mathcal{D}:=\{(x_j, y_j) \mid j=1,\ldots, N\}\subset ...
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I have abundance data of spiders that I captured in different forest stands and I want to perform an NMDS. It generally works, although the stress value is relatively high. However, I get the message ...
Jenny s.'s user avatar
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For an undirected graph with one connected component and distance matrix given by the shortest path between nodes, I would like to embed the nodes in a high dimensional Euclidean space where all ...
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I am looking at species count data. Due to large counts of certain species, I initially fourth root transformed the data. I am using Bray-Curtis distance measurement. However when using vegan::metaMDS,...
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I have a question about dimensionality reduction. I want to understand how methods like MDS and t-SNE work. In particular, I'd like to understand the difference when I precompute the distance matrix ...
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First post here, so let me know if I'm missing things. I'm working with a multidimensional scaling (MDS) plot generated in MATLAB using cmdscale(1-(corr(Betas))). Based on prior experiments, I suspect ...
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I've tried to research this question but have had to rely on answers to only somewhat similar scenarios which have led me in different directions. For example, I've been advised to calculate variance ...
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I have conducted a PCA and identified that the principal components (PC) are not driven by a single environmental parameter, but are affected by several for each PC. I was then advised to retrieve the ...
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Im comparing two multidimensional MDS solutions, the solutions have the same number of dimensions. I don't think I can use the permutation version of procrustes analysis (commonly, PROTEST in R::vegan)...
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The question is basically the title. I have a matrix $Q$ that I know is positive semi-definite. I now want to find the $Y$ that approximates this matrix under some kernel function $f(y_i, y_j)$. I ...
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relatively new to this and this question has been plaguing me. Say I have a dataset with feature A, feature B, and feature C. I need to scale for my model. Based on their distributions, feature A is ...
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My ultimate goal is a way to evaluate a group of "m" covariance matrices (all size n*n) so I can pick an arbitrary one and calculate "this one is tighter than the average covariance ...
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I'm performing an NMDS analysis on a Jaccard distance matrix of 347 variables across 341 subjects. I'm using mMDS from the Vegan package in R, everything seems to run fine, I get convergence and a ...
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For context, I have a input dataset of 156 images and I'm extracting the feature maps for each image at the last fully connected layer of the AlexNet model. I get 156 feature maps, each of size [1, ...
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I've got a follow-up question to this post regarding correlating [non-]linear environmental variables to NMDS ordination axes. My original plan was to use function ...
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Data + setup: I've constructed an NMDS ordination from a Bray-Curtis distance matrix calculated from relativized abundances (basal areas) of trees. Samples include ~40 forested plots that have been ...
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I have computed a matrix of MDS distances using R's dist() function and then reduced to two-dimensional coordinates using cmdscale() function. If I apply PAM or k-means clustering (the choice of ...
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Do I need to account for autocorrelation when assessing correlations of environmental variables on an NMDS (nonmetric multidimensional scaling) ordination of species data? If so, how? Data + setup: I'...
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I have dataset composed of samples (here corresponding to sites), species, environmental variables linked to the species (e.g.: species biomass, abundance and size) and explanatory variables (...
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I am conducting both constrained and unconstrained analyses on the same species abundance data. For the constrained ordination, I ran the RDA (redundancy analysis) on log x+1 and hellinger transformed ...
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I've created a Non-metric MultiDimensional Scaling (NMDS) ordination from a Bray-Curtis dissimilarity matrix. (Starting data were basal areas of various tree species across multiple research plots). I'...
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I have a data set with lots of small integer values and occasional large integers. For instance 1,1,1,3,2,1,320,2,3,4. I would like to scale my outlier values such that I can perform regression on my ...
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The actual values of NMDS axes are essentially arbitrary, as I understand them - there's no meaning to be gained from a value of 1 or 100 or 10,000. In keeping with good design principles should I ...
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Since Dimension 1 is longer than the Dimension 2, I want to make the plot vertically long rather than horizontally long so that it's easier to include it in my thesis. But is it acceptable to make ...
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I am analysing a large dataset (2000 rows by 250 columns) of the presence of species in several locations over the last 20 years. I have conducted a NMDS in order to identify differences between the ...
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Coordinates and Labels Take the simple case of 3 distinct object classes and 5 instances of each class situated in 3D Euclidean space. The coordinates and labels might look like the following: ...
Sterling's user avatar
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I've always been taught that a major downside of NMDS is that there's no way to calculate the amount of variance captured by each axis. Variance doesn't come into the calculation at all so this made ...
Dubukay's user avatar
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Short I wonder is it possible to sum the principal components together to obtain a score? For example, PC1 + PC2. Details I got the below dataframe: admin_username sales sign book team_sales team_sign ...
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I have some broker prices incoming in real-time for several products. Sometimes a broker makes a typo and sends a wrong price, or my parsing engine assigns the price to the wrong product - these are ...
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I have performed an Non-metric multidimensional scaling (NMDS) to see if my two stations were different in terms of plankton abundances, using the metaMDS function in r (before I have performed a sqrt ...
Franc's user avatar
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If I have an arbitrary Euclidean distance matrix $D=(d_{ij}:i=1,\ldots, I; j=1,\ldots, I)$ and I want to reconstruct its elements (pairwise Euclidean distances) via classical Euclidean MDS. That is ...
Jim's user avatar
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Suppose you have a data set that doesn't appear to be normal when its distribution is first plotted (e.g., it's qqplot is curved). If after some kind of transformation is applied (e.g., log, square ...
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I'm doing a non-metric multidimensional scaling analysis. The analysis results in two convergent solutions and the output all look good, but when I made a stress plot to check the data I am getting an ...
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I'm basing my question off of someone else's stackoverflow post. My questions are the following: 1. A widely used R package vegan has a function called procrustes, ...
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I have a data set in which two variables are collinear (r^2 ≈ 0.7). I decided to extract the principal components, and then include these in a regression analysis to see which of the two variables ...
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I am dealing with panel data and want to scale it in order to use it for some ML models: id year A B C 1 2000 3,539,101 265.152 .0683649 1 2001 3,539.101 2,485.833 .0683649 1 2002 3,539.101 2,939.903 ....
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Ok so I'm confused about the whole concept of standardizing data. I get the concept of why we need to standardize data for, let's say multiple linear regression so the data points are similar, but ...
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I am in process of writing a grant where I am explaining my planned methylation analysis using R software "minfi". In the text of the grant I am mentioning looking for samples containing ...
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I have 10x10 distance matrix where the distance metrics is (1 - overlap coefficient). I want to represent the observations in this matrix in a low dimensional space to see how observations relate to ...
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I am testing ideas on clustering a particular graph. After testing a set of graph clustering/community detection algorithms I thought about mapping the graph to a vector space and using vector space ...
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I have data and tried to do clustering on it. every time I run the code with the below statements it changes the angle of the shape but still the same below shape ...
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My goal is to carry out an hierarchical cluster analysis using the principal components that explain most of the variance. None of my variables is normal and therefore I think I should transform them (...
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I would like to start by saying that I have looked across several sites on the StackExchange website, and have determined this would be the best to ask my question as it regards data-visualisation ...
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I am trying to contrast results of various MDS approaches applied on the same dataset and understand their comparative interpretation. I calculate the goodness of fit for the various models with the ...
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I have a set of data points in high-dimensional space that I wish to map onto a lower dimension (3D or 2D). Question : How do I obtain the Projection (Hyper)Plane (e.g., its normal vector or its set ...
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