>hF*SrSSKrSSKJr SSjrg)zE Bland-Altman mean-difference plots Author: Joses Ho License: BSD-3 N)utilsc |[R"U5ups[U5[U5:wa [S5eUS:a[SUS35e[R "X/SS9nX- n [R "U 5n [R "U SS9n U=(d 0nSU;aSUS'U=(d 0nU=(d 0nXV4Hn SU ;aS U S'S U ;dMS U S 'M S U;aS W S 'S U;aSW S 'UR"X40UD6 UR"U 40UD6 URS[R"U S53SSSSSS9 US:aSU-U -n URX- X-5 X+-nX- nX-n[UU/5HunnUR"U40UD6 M URSUSUS3SSSSSS9 URSUSUS3SSSSS9 O!US:XaS U -n URX- X-5 URS!S"S#9 URS$S"S#9 URS%S&9 UR!5 U$)'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 qC:\Users\julio\OneDrive\Documentos\Trabajo\Ideas Frescas\venv\Lib\site-packages\statsmodels/graphics/agreement.pymean_diff_plotrE sR!!"%GC 2w#b'BCC!|:hZ/@ABB GGRH1 %E GEIvve!$H%2L , S#)rN'-22 $ "DM d " !D  3 .( [**[JJu,|,JJy+N+KK,rxx 1567$+"*( *!|8^x/  I)) +%0../FAs JJs /. /0 azuTl3#(/&.,  . azuTl3#(/,  . QL  I)) +MM,M,MM'BM'NNRN  J)g\(\?NNNN)__doc__numpyr$rrErFrDrKs"AE9=MrF