o 0Gf@s*dZddlZddlmZ  dddZdS) zE Bland-Altman mean-difference plots Author: Joses Ho License: BSD-3 N)utils\(\?cCs:t|\}}t|t|krtd|dkrtd|dtj||gdd}||} t| } tj| dd} |p;i}d|vrDd|d<|pGi}|pKi}||fD]} d| vrZd | d<d | vrbd | d <qPd |vrkd | d <d |vrsd| d <|j|| fi||j| fi||j dt | ddddddd|dkrd|| } | | | | | || }| |}| |}t ||gD] \}}|j|fi|q|j d|d|ddddddd|j d|d|ddddddn|dkrd | } | | | | | |j d!d"d#|jd$d"d#|jd%d&||S)'a 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 ---------- m1 : array_like A 1-d array. m2 : array_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() .. plot:: plots/graphics-mean_diff_plot.py z'm1 does not have the same length as m2.rz sd_limit (z) is less than 0.)ZaxissZcolorZgrayZ linewidthrZ linestylez--:z mean diff: )Gz?g?rightcenterz axes fraction)xyhorizontalalignmentZverticalalignmentfontsizexycoordsg?-z SD: z0.2g)r gQ?Zbottom+)r gq= ףp?)r rrrZ Difference)rZMeans )Z labelsize)rZ create_mpl_axlen ValueErrornpZmeanZstdZscatterZaxhlineZannotateroundZset_ylim enumerateZ set_ylabelZ set_xlabelZ tick_paramsZ tight_layout)Zm1Zm2Zsd_limitZaxZ scatter_kwdsZmean_line_kwdsZlimit_lines_kwdsZfigZmeansZdiffsZ mean_diffZstd_diffkwdsZ half_ylimZlimit_of_agreementlowerupperjZlimr>lib/python3.10/site-packages/statsmodels/graphics/agreement.pymean_diff_plot sI       r!)rNNNN)__doc__Znumpyrrr!rrrr s