This shows examples of CorrPlot, Graph, TSNEPlot, and LocalGaussianCorrelationPlot. CorrPlot displays correlation matrices with hierarchical clustering dendrograms. Graph visualizes correlations as a network. TSNEPlot performs t-SNE dimensionality reduction in the browser. LocalGaussianCorrelationPlot shows how correlation varies across the joint distribution of two variables.
A Correlation Plot with Dendrogram shows relationships between variables using hierarchical clustering. The dendrogram (top) groups similar variables based on their correlation patterns, with clustering performed dynamically in your browser. Note that it will only appear if i) you select order by dendrogram and ii) you select all of the variables. The correlation matrix (bottom) uses two different correlation measures: Pearson correlations (top-right triangle, marked with 'P:') measure linear relationships, while Spearman correlations (bottom-left triangle, marked with 'S:') measure monotonic relationships and are robust to outliers. You can change the clustering linkage method (Ward, Average, Single, Complete) to see different groupings. Variables are automatically reordered by the clustering to reveal correlation blocks. See here for CorrPlot examples
Data: stock_corr_data.parquet
This Graph allows you to visualise the relationships between variables. The dataformat to make this is a DataFrame with columns: node1, node2, strength, scenario, correlation_method, sector. See here for more Graph examples
Data: stock_corr_data.parquet
TSNEPlot performs t-SNE dimensionality reduction entirely in the browser. This example shows 10 stocks positioned by similarity across 6 statistical features. Key Features: Interactive feature selection (Available/Selected lists let you choose which variables define similarity), Rescaling options (None, Z-score, Z-score capped, Quantile), Configurable early exaggeration (iterations and factor), Three step modes: 'Step (small)' for single non-exaggerated steps, 'Exaggerated Step' for large movements, 'Run to Convergence' for automatic optimization. You can drag nodes to manually reposition them and resume iteration. Color by sector to see if similar stocks cluster together. See here for TSNEPlot examples
Data: tsne_stock_data.parquet
LocalGaussianCorrelationPlot visualizes how correlation between two variables changes across their joint distribution. See here for LocalGaussianCorrelationPlot examples
Local Gaussian Correlation shows how the correlation between two variables varies across their joint distribution. The heatmap displays the local correlation at each (x, y) point, computed using a Gaussian kernel to weight nearby observations.
The green line on the right shows the average correlation at each Y value (integrated over X), weighted by the kernel density. The red line at the bottom shows the average correlation at each X value (integrated over Y).
Blue = positive, Red = negative, White = zero.
Bootstrap t-statistic: Resamples the data 200 times to estimate the standard error of the local correlation. The t-statistic (correlation / SE) indicates statistical significance. Values beyond ±1.96 are significant at p < 0.05.
Data: lgc_stock_data.parquet