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statsmodels.graphics.correlation.plot_corr_grid

statsmodels.graphics.correlation.plot_corr_grid(dcorrs, titles=None, ncols=None, normcolor=False, xnames=None, ynames=None, fig=None, cmap='RdYlBu_r')[source]

Create a grid of correlation plots.

The individual correlation plots are assumed to all have the same variables, axis labels can be specified only once.

Parameters:

dcorrs : list or iterable of ndarrays

List of correlation matrices.

titles : list of str, optional

List of titles for the subplots. By default no title are shown.

ncols : int, optional

Number of columns in the subplot grid. If not given, the number of columns is determined automatically.

normcolor : bool or tuple, optional

If False (default), then the color coding range corresponds to the range of dcorr. If True, then the color range is normalized to (-1, 1). If this is a tuple of two numbers, then they define the range for the color bar.

xnames : list of str, optional

Labels for the horizontal axis. If not given (None), then the matplotlib defaults (integers) are used. If it is an empty list, [], then no ticks and labels are added.

ynames : list of str, optional

Labels for the vertical axis. Works the same way as xnames. If not given, the same names as for xnames are re-used.

fig : Matplotlib figure instance, optional

If given, this figure is simply returned. Otherwise a new figure is created.

cmap : str or Matplotlib Colormap instance, optional

The colormap for the plot. Can be any valid Matplotlib Colormap instance or name.

Returns:

fig : Matplotlib figure instance

If ax is None, the created figure. Otherwise the figure to which ax is connected.

Examples

>>> import numpy as np
>>> import matplotlib.pyplot as plt
>>> import statsmodels.api as sm

In this example we just reuse the same correlation matrix several times. Of course in reality one would show a different correlation (measuring a another type of correlation, for example Pearson (linear) and Spearman, Kendall (nonlinear) correlations) for the same variables.

>>> hie_data = sm.datasets.randhie.load_pandas()
>>> corr_matrix = np.corrcoef(hie_data.data.T)
>>> sm.graphics.plot_corr_grid([corr_matrix] * 8, xnames=hie_data.names)
>>> plt.show()

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