statsmodels.graphics.agreement.mean_diff_plot¶

statsmodels.graphics.agreement.mean_diff_plot(m1, m2, sd_limit=1.96, ax=None, scatter_kwds=None, mean_line_kwds=None, limit_lines_kwds=None)[source]¶

Construct a Tukey/Bland-Altman Mean Difference Plot.

Tukey’s Mean Difference Plot (also known as a Bland-Altman plot) is a graphical method to analyze the differences between two methods of measurement. The mean of the measures is plotted against their difference.

For more information see https://en.wikipedia.org/wiki/Bland-Altman_plot

Parameters:

m1array_like: A 1-d array.
m2array_like: A 1-d array.
sd_limitfloat: The limit of agreements expressed in terms of the standard deviation of the differences. If md is the mean of the differences, and sd is the standard deviation of those differences, then the limits of agreement that will be plotted are md +/- sd_limit * sd. The default of 1.96 will produce 95% confidence intervals for the means of the differences. If sd_limit = 0, no limits will be plotted, and the ylimit of the plot defaults to 3 standard deviations on either side of the mean.
axAxesSubplot: If ax is None, then a figure is created. If an axis instance is given, the mean difference plot is drawn on the axis.
scatter_kwdsdict: Options to to style the scatter plot. Accepts any keywords for the matplotlib Axes.scatter plotting method
mean_line_kwdsdict: Options to to style the scatter plot. Accepts any keywords for the matplotlib Axes.axhline plotting method
limit_lines_kwdsdict: Options to to style the scatter plot. Accepts any keywords for the matplotlib Axes.axhline plotting method

Returns:

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

References

Bland JM, Altman DG (1986). “Statistical methods for assessing agreement between two methods of clinical measurement”

Examples

Load relevant libraries.

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

Making a mean difference plot.

>>> # Seed the random number generator.
>>> # This ensures that the results below are reproducible.
>>> np.random.seed(9999)
>>> m1 = np.random.random(20)
>>> m2 = np.random.random(20)
>>> f, ax = plt.subplots(1, figsize = (8,5))
>>> sm.graphics.mean_diff_plot(m1, m2, ax = ax)
>>> plt.show()

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