Implementation of Dunn's multiple comparison test, following a Kruskal-Wallis 1-way ANOVA

Gist dfc010f37b8d56dd9d72

This example demonstrates how to perform a repeated-measures ANOVA with sphericity correction using a custom Python function. It shows the statistical results, including F-values, p-values, and corrected degrees of freedom, for each main effect and interaction term in a factorial within-subject design. The implementation relies on NumPy for matrix operations, SciPy for statistical distributions and detrending, and constructs effect codings through binary patterns. The code defines a `repanova` function that returns a list of named tuples containing the effect name, corrected degrees of freedom, F-value, and p-value, with optional Greenhouse-Geisser correction.

Aalpha-beta-soup

55% match

Visualization

This visualization shows the Davies-Bouldin Index values for K-Means clustering of citation data from the top 2000 papers of the 1990s, with each point representing a different cluster count. A lower DB index indicates better clustering, revealing that 15 clusters achieve a notably better score (5.9) than 2 clusters (9.2). The scatter plot uses d3.svg.axis for axes, d3.scale.linear for positioning, and d3.tip for tooltips, with d3.behavior.zoom enabling interactive panning and zooming. Data comes from a CSV file containing K values and their corresponding DB and W metrics.

Scatterplot Matrix

This scatterplot matrix visualizes pairwise correlations across six quantitative variables (k_median and five rank-conditional metrics) for 30 observations, colored by tiername (Highly selective vs. Nonselective). Each cell in the matrix is a scatterplot of two traits, with axes scaled independently to their domains. Diagonal cells display trait names as bold capitalized labels. The visualization, built with D3 v3 and SVG, includes axes with tick marks, light gray gridlines, and semitransparent circles colored by category. The data comes from a CSV of college rankings and selectivity categories. This example forks Mike Bostock's scatterplot matrix block, itself based on Anderson's iris flower data, and adapts it for the given dataset.

Aanaeliaovalle

Fork of test

This visualization displays a correlation matrix for the `mtcars` dataset, showing pairwise relationships between variables. The upper triangle presents correlation coefficients as colored text, while the lower triangle uses circles sized by the absolute correlation value, with color indicating the correlation strength from red (negative) to blue (positive). The diagonal labels each variable. Built with d3.v3, the code loads data from CSV, uses ordinal scales with `rangePoints` for positioning, and applies a linear color scale for the diverging color scheme.

Nnandugali

Gist b989c26735fde8214c19

This example demonstrates statistical power calculations for several genetic association models (DRAB1_sGMV, HLAB_ucca, HLAB_sGM, NFkb_WM) using R’s noncentral F distribution via the rpy2 interface, with power derived from effect sizes and sample sizes listed in a pandas DataFrame. It imports the `stats` package and uses `statsmodels.api` alongside scipy.stats, defining a custom `pf` function that calls R’s `qf` and `pf` functions to compute critical values and noncentral power. The notebook also loads precomputed intraclass correlation coefficients from a pickled file and displays the power table as an HTML-rendered pandas DataFrame.

Aakeshavan

Visualization

This visualization plots the Davies-Bouldin index against the number of clusters for a k-means analysis of philosophy papers, revealing how clustering quality varies with cluster count. The code uses d3.v3 with SVG circles positioned via linear scales, reading data from a CSV file that tracks citation patterns across analytical and continental philosophy journals. A d3.tip tooltip displays exact values on hover, while d3.behavior.zoom enables interactive panning and zooming of the scatterplot. The animation and transitions are handled through d3's built-in transition methods.

47% match

Visualization

This visualization plots the Davies-Bouldin Index against the number of clusters (k) for clustered citation data from philosophy journals, showing that 7 clusters yield the lowest index and thus the best clustering. The scatterplot uses d3.svg.axis, d3.scale.linear, and d3.behavior.zoom for panning and zooming, with circles colored by cluster and tooltips via d3.tip. Data comes from a CSV file containing clustering results from the author's analysis.

47% match

Visualization

This interactive scatter plot displays the Davies-Bouldin index (y-axis) against the number of clusters k (x-axis) for K-means clustering results on citation data from philosophy journals. It visualizes how clustering quality changes from 2 to 10 clusters, showing a minimum at k=7. The visualization uses D3 v3 with `d3.scale.linear`, `d3.svg.axis`, and `d3.scale.category20`, rendering circles in SVG. A button switches the x-axis to a different (unused) data column, while the underlying data comes from a CSV file loaded via `d3.csv`, with tooltips via the d3-tip library and zoom/pan through `d3.behavior.zoom`.