ehrapy.plot.variable_dependencies

ehrapy.plot.variable_dependencies(edata, *, layer, var_names=None, method='pearson', agg='mean', correction_method='bonferroni', alpha=0.05, abs_correlation_threshold=0.3, only_significant=True, width=600, height=600, cmap='RdBu_r', title=None)

Plot correlation dependencies as a chord diagram.

Computes pairwise correlations between selected variables from layer and visualizes them as a chord diagram. If the layer contains a time dimension, values are aggregated per variable before correlation is computed.

Parameters:

• edata (EHRData) – Central data object.

• layer (str) – Layer to extract data from.

• var_names (Sequence[str] | None, default: None) – List of variable names to compute correlation of. If None, uses all numeric variables.

• method (Literal['spearman', 'pearson', 'kendall'], default: 'pearson') – Correlation method: “spearman”, “kendall” or “pearson”.

• agg (Literal['mean', 'last', 'first'], default: 'mean') – How to aggregate time dimension: “mean”, “last” or “first”.

• correction_method (Literal['bonferroni', 'fdr_bh', 'fdr_tsbh', 'holm', 'none'], default: 'bonferroni') – Multiple testing correction method: * ‘bonferroni’ conservative Bonferroni correction. * ‘fdr_bh’ Benjamini-Hochberg false discovery rate (FDR) control. * ‘fdr_tsbh’ two-stage Benjamini-Hochberg, better calibrated when many variables are truly correlated. * ‘holm’ Holm-Bonferroni correction. * ‘none’ no multiple-testing correction.

• alpha (float, default: 0.05) – Significance threshold after correction.

• abs_correlation_threshold (float, default: 0.3) – Minimum absolute correlation to show a chord.

• only_significant (bool, default: True) – If True, only show significant correlations.

• width (int, default: 600) – Plot width in pixels.

• height (int, default: 600) – Plot height in pixels.

• cmap (str, default: 'RdBu_r') – Colormap for the chord diagram.

• title (str | None, default: None) – Set the title of the plot.

Return type:

Chord

Returns:

holoviews.element.Chord object.

Examples

>>> import ehrdata as ed
>>> import ehrapy as ep
>>> edata = ed.dt.ehrdata_blobs(n_variables=10, n_centers=5, n_observations=200, base_timepoints=3)
>>> ep.pl.variable_dependencies(
...     edata, layer="tem_data", method="pearson", agg="mean", correction_method="fdr_bh"
... )