Python中乏味的Altman情节

有可能在Python中创建一个应用程序吗？我似乎找不到关于它的任何东西

这种类型图的另一个名称是Tukey平均差图

示例：

---

也许我遗漏了什么，但这似乎很简单：

from numpy.random import random
import matplotlib.pyplot as plt

x = random(25)
y = random(25)

plt.title("FooBar")
plt.scatter(x,y)
plt.axhline(y=0.5,linestyle='--')
plt.show()

---

在这里，我只是在0和1之间创建了一些随机数据，并在y=0.5处随机放置了一条水平线——但您可以在任何地方放置任意数量的数据。

如果我正确理解了绘图背后的理论，这段代码应该提供基本绘图，而您可以根据自己的特殊需要进行配置

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='--')

data1

和

data2

中的相应元素用于计算绘制点的坐标

然后，您可以通过运行例如

from numpy.random import random

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

---

这现在在statsmodels中实现：

以下是他们的例子：

import statsmodels.api as sm
import numpy as np
import matplotlib.pyplot as plt

# 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()

这就产生了：

---

我接受了索德的答案，并精心实施了一项计划。这似乎是最容易分享的地方

from scipy.stats import linregress
import numpy as np
import plotly.graph_objects as go
def bland_altman_plot(data1, data2, data1_name='A', data2_name='B', subgroups=None, plotly_template='none', annotation_offset=0.05, plot_trendline=True, n_sd=1.96,*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


    fig = go.Figure()

    if plot_trendline:
        slope, intercept, r_value, p_value, std_err = linregress(mean, diff)
        trendline_x = np.linspace(mean.min(), mean.max(), 10)
        fig.add_trace(go.Scatter(x=trendline_x, y=slope*trendline_x + intercept,
                                 name='Trendline',
                                 mode='lines',
                                 line=dict(
                                        width=4,
                                        dash='dot')))
    if subgroups is None:
        fig.add_trace( go.Scatter( x=mean, y=diff, mode='markers', **kwargs))
    else:
        for group_name in np.unique(subgroups):
            group_mask = np.where(np.array(subgroups) == group_name)
            fig.add_trace( go.Scatter(x=mean[group_mask], y=diff[group_mask], mode='markers', name=str(group_name), **kwargs))



    fig.add_shape(
        # Line Horizontal
        type="line",
        xref="paper",
        x0=0,
        y0=md,
        x1=1,
        y1=md,
        line=dict(
            # color="Black",
            width=6,
            dash="dashdot",
        ),
        name=f'Mean {round( md, 2 )}',
    )
    fig.add_shape(
        # borderless Rectangle
        type="rect",
        xref="paper",
        x0=0,
        y0=md - n_sd * sd,
        x1=1,
        y1=md + n_sd * sd,
        line=dict(
            color="SeaGreen",
            width=2,
        ),
        fillcolor="LightSkyBlue",
        opacity=0.4,
        name=f'±{n_sd} Standard Deviations'
    )

    # Edit the layout
    fig.update_layout( title=f'Bland-Altman Plot for {data1_name} and {data2_name}',
                       xaxis_title=f'Average of {data1_name} and {data2_name}',
                       yaxis_title=f'{data1_name} Minus {data2_name}',
                       template=plotly_template,
                       annotations=[dict(
                                        x=1,
                                        y=md,
                                        xref="paper",
                                        yref="y",
                                        text=f"Mean {round(md,2)}",
                                        showarrow=True,
                                        arrowhead=7,
                                        ax=50,
                                        ay=0
                                    ),
                                   dict(
                                       x=1,
                                       y=n_sd*sd + md + annotation_offset,
                                       xref="paper",
                                       yref="y",
                                       text=f"+{n_sd} SD",
                                       showarrow=False,
                                       arrowhead=0,
                                       ax=0,
                                       ay=-20
                                   ),
                                   dict(
                                       x=1,
                                       y=md - n_sd *sd + annotation_offset,
                                       xref="paper",
                                       yref="y",
                                       text=f"-{n_sd} SD",
                                       showarrow=False,
                                       arrowhead=0,
                                       ax=0,
                                       ay=20
                                   ),
                                   dict(
                                       x=1,
                                       y=md + n_sd * sd - annotation_offset,
                                       xref="paper",
                                       yref="y",
                                       text=f"{round(md + n_sd*sd, 2)}",
                                       showarrow=False,
                                       arrowhead=0,
                                       ax=0,
                                       ay=20
                                   ),
                                   dict(
                                       x=1,
                                       y=md - n_sd * sd - annotation_offset,
                                       xref="paper",
                                       yref="y",
                                       text=f"{round(md - n_sd*sd, 2)}",
                                       showarrow=False,
                                       arrowhead=0,
                                       ax=0,
                                       ay=20
                                   )
                               ])
    return fig

---

pyCompare拥有平淡的Altman情节（参见）

pyCompare模块的详细信息

---

最终产品如下所示：

为什么不使用

plt.plot

绘制点，然后使用

plt.axhline

添加水平线？这个阴谋似乎很容易做到。是的，阴谋很容易。但我想可能在库中有一个模块也可以进行计算。例如，我传递2个信号，它会绘制我的绘图。它在x轴上总是平均值，在y轴上是两个信号的差值。水平线是平均标准偏差（neg和pos），不应该是

md+1.96*sd

和

md-1.96*sd

，而应该是

plt.xlabel（“两个测量值的平均值”）

和

plt。可以添加ylabel（“两个测量值之间的差异”）

，以澄清x轴和y轴。这应该是最新安装的正确答案！这不是乏味的奥特曼阴谋。在bland altman图中，y轴应该是两组数据之间的差值。

import pyCompare
method1 = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20]
method2 = [1.03, 2.05, 2.79, 3.67, 5.00, 5.82, 7.16, 7.69, 8.53, 10.38, 11.11, 12.17, 13.47, 13.83, 15.15, 16.12, 16.94, 18.09, 19.13, 19.54]
pyCompare.blandAltman(method1, method2)