-Each cell presents the cluster centroid separation Δ (brighter colours indicate stronger separation) after multi-dimensional scaling (MDS) was applied to simulated data of 1000 observations and 15 features. Separation is shown as a function of within-feature effect size (Cohen's d, x-axis), and the proportion of features that were different between clusters. Each row shows a different covariance structure: "mixed" indicates subgroups with different covariance structures, "random" with the same random covariance structure between all groups, and "no" for no correlation between any of the features). Each column shows a different type of population: with unequal (10 and 90%) subgroups, with two equally sized subgroups, and with three equally sized subgroups.

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... distance Δ is plotted as a function of both within-feature effect size δ and the proportion of different features after dimensionality reduction through MDS ( Figure 3) and UMAP ( Figure 4). The visualised datasets also differed in number of clusters (two unequally sized, two equally sized, or three equally sized), and covariance structure (no covariance, random covariance, or different covariances between clusters), adding up to 180 datasets per figure. ...