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_limit
float
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.
- ax
AxesSubplot
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_kwds
dict
Options to to style the scatter plot. Accepts any keywords for the matplotlib Axes.scatter plotting method
- mean_line_kwds
dict
Options to to style the scatter plot. Accepts any keywords for the matplotlib Axes.axhline plotting method
- limit_lines_kwds
dict
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()
(Source code, png, hires.png, pdf)