python中带有置信区间边界的Bland-Altman图

Question

我正在尝试绘制 Bland-Altman 图。

import matplotlib.pyplot as plt
import numpy as np

def bland_altman_plot(data1, data2, *args, **kwargs):
    data1     = np.asarray(data1)
    data2     = np.asarray(data2)
    mean      = np.mean([data1, data2], axis=0)
    diff      = data1 - data2                   # Difference between data1 and data2
    md        = np.mean(diff)                   # Mean of the difference
    sd        = np.std(diff, axis=0)            # Standard deviation of the difference

    plt.scatter(mean, diff, *args, **kwargs)
    plt.axhline(md,           color='gray', linestyle='--')
    plt.axhline(md + 1.96*sd, color='gray', linestyle='--')
    plt.axhline(md - 1.96*sd, color='gray', linestyle='--')

from numpy.random import random

bland_altman_plot(random(10), random(10))
plt.title('Bland-Altman Plot')
plt.show()

以上代码产生以下结果：

我正在尝试绘制一个置信区间边界，如下图所示。假设顶行的置信区间坐标是：

左 (0.3, 0.5), (0.3, 0.4) 和右 (0.9, 0.5), (0.9, 0,4)

底线是：

左 (0.3, 0.7), (0.3, 0.5) 和右 (0.9, 0.7), (0.9, 0.5)

但我不知道如何在 ma​​tplotlib 中像这样绘制它。如果有人可以帮助我，那就太好了。

Answer 1

我想你想要errorbar:

def bland_altman_plot(data1, data2, *args, **kwargs):
    data1     = np.asarray(data1)
    data2     = np.asarray(data2)
    mean      = np.mean([data1, data2], axis=0)
    diff      = data1 - data2                   # Difference between data1 and data2
    md        = np.mean(diff)                   # Mean of the difference
    sd        = np.std(diff, axis=0)            # Standard deviation of the difference

    plt.scatter(mean, diff, *args, **kwargs)
    plt.axhline(md,           color='gray', linestyle='--')
    plt.axhline(md + 1.96*sd, color='gray', linestyle='--')
    plt.axhline(md - 1.96*sd, color='gray', linestyle='--')

    # set up params for ci_loa
    # change these accordingly
    ci_loa_height = np.std(mean)
    ci_loa_x = mean.min(), mean.max()

    # draw the errorbar/ci intervals
    # change color and capsize as wished
    plt.errorbar(ci_loa_x, [md + 1.96*sd]*2, 
                 yerr=ci_loa_height, fmt='none',
                 capsize=10, c='r')

    plt.errorbar(ci_loa_x, [md - 1.96*sd]*2, 
                 yerr=ci_loa_height, fmt='none',
                 capsize=10, c='r')
    plt.title('Bland-Altman Plot')
    plt.show()

然后：

np.random.seed(10)
bland_altman_plot(np.random.random(10), np.random.random(10))

产生：