Heatmaps / Correlation heatmap
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Each cell is the correlation between two variables: red is positive, blue negative, darker is stronger; the diagonal is always 1.
Stars use the adjusted P value: * < 0.05, ** < 0.01, *** < 0.001. With many pairs tested at once, read the adjusted P rather than the raw P.
Pearson measures linear association and is sensitive to outliers; Spearman and Kendall are rank-based and suit non-normal data or data with outliers.
Cluster ordering places variables with similar correlation patterns together, revealing modules. Correlation is not causation, and coefficients vary widely in small samples.
P values: exact t test for Pearson, t approximation for Spearman, tau-b for Kendall (as scipy.stats); missing values removed pairwise; multiple-testing adjustment counts each pair once (Benjamini & Hochberg 1995, J R Stat Soc B 57:289–300).
Up to 200 variables; above 25 variables only stars are printed in cells.
Send us your data and research question and you will receive a written plan within 1 working day: analysis steps, parameter rationale, deliverables and timeline. Quoted per project.