ehrapy.plot.missing_values_dendrogram

ehrapy.plot.missing_values_dendrogram(edata, *, method='average', filter=None, max_cols=0, max_percentage=0, orientation=None, figsize=None, fontsize=16, label_rotation=45, categoricals=False, layer=None)

Fits a scipy hierarchical clustering algorithm and visualizes the results as a scipy dendrogram.

The default vertical display will fit up to 50 columns. If more than 50 columns are specified and orientation is left unspecified the dendrogram will automatically swap to a horizontal display to fit the additional variables.

Parameters:

• edata (EHRData) – Central data object.

• method (str, default: 'average') – The distance measure being used for clustering. This parameter is passed to scipy.hierarchy.

• filter (str | None, default: None) – The filter to apply to the dendrogram. Should be one of “top”, “bottom”, or None.

• max_cols (int, default: 0) – The max number of columns from the data object to include.

• max_percentage (float, default: 0) – The max percentage fill of the columns from the data object.

• figsize (tuple | None, default: None) – The size of the figure to display.

• fontsize (float, default: 16) – The figure’s font size.

• orientation (str | None, default: None) – The way the dendrogram is oriented.

• label_rotation (float, default: 45) – What angle to rotate the text labels to. .

• categoricals (bool, default: False) – Whether to include “ehrapycat” columns to the plot.

• layer (str | None, default: None) – The layer to use.

Returns:

The plot axis.

Example

>>> import ehrdata as ed
>>> import ehrapy as ep
>>> edata = ed.dt.mimic_2()
>>> ep.pl.missing_values_dendrogram(edata, filter="bottom", max_cols=15, max_percentage=0.999)

Preview: