M/Ph*dZddlZddlmZ ddZdS)zE Bland-Altman mean-difference plots Author: Joses Ho License: BSD-3 N)utils\(\?ctj|\}}t|t|krtd|dkrtd|dt j||gd}||z } t j| } t j| d} |pi}d|vrd|d<|pi}|pi}||fD]} d| vrd | d<d | vrd | d <d |vrd | d <d |vrd| d <|j|| fi||j| fi|| dt j | dddddd|dkrd|z| z} | | | z | | z|| z}| |z }| |z}t||gD]\}}|j|fi|| d|d|dddddd| d|d|dddddn'|dkr!d | z} | | | z | | z| d!d"#|d$d"#|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.)axisscolorgray linewidthr linestylez--:z mean diff: )Gz?g?rightcenterz axes fraction)xyhorizontalalignmentverticalalignmentfontsizexycoordsg?-z SD: z0.2g)rgQ?bottom+)rgq= ףp?)rrrr Difference)rMeans ) labelsize)r create_mpl_axlen ValueErrornpmeanstdscatteraxhlineannotateroundset_ylim enumerate set_ylabel set_xlabel tick_params tight_layout)m1m2sd_limitax scatter_kwdsmean_line_kwdslimit_lines_kwdsfigmeansdiffs mean_diffstd_diffkwds half_ylimlimit_of_agreementlowerupperjlims ^/var/www/html/test/jupyter/venv/lib/python3.11/site-packages/statsmodels/graphics/agreement.pymean_diff_plotrF smR!"%%GC 2ww#b''BCCC!||AhAAABBB GRH1 % % %E GEIve!$$$H%2L , S#)rN'-2!12"" $  "DM d " " !D .(( [***[BJue,,|,,,BJy++N+++KK7rx 15577$+"*( ***!||8^x/  I ) ) + + +%0....// 0 0FAs BJs / /. / / / / 333u333#(/&.,  . . . 333u333#(/,  . . . . QL  I ) ) + + +MM,M,,,MM'BM'''NNRN    J)rNNNN)__doc__numpyr%rrFrGrErLs]AE9=MMMMMMrG