插图框p值重要批注 [英] Plotly box p-value significant annotation

问题描述

我已经开始使用并且喜欢用框图来表示我的数据。然而，我很难找到一种方法来对比这两组人。在使用Ploly时，有没有办法在数据之间引入统计学意义上的比较？我想创建这样的图表：

其中*对应于p值scipy.stats.ttest_ind()和stats.ttest_ind_from_stats()可以很容易地找到两个分布的p值。

我没有在网上找到任何相关的帖子，我认为这是一个相当有用的实现，所以如果有任何帮助，我们将不胜感激！

推荐答案

如果有人觉得它有帮助，我写了这个函数add_p_value_annotation。它创建一个括号注释，并用星号指定两个框图之间的p值。当您的图形中有子图时，它也应该起作用。

def add_p_value_annotation(fig, array_columns, subplot=None, _format=dict(interline=0.07, text_height=1.07, color='black')):
    ''' Adds notations giving the p-value between two box plot data (t-test two-sided comparison)
    
    Parameters:
    ----------
    fig: figure
        plotly boxplot figure
    array_columns: np.array
        array of which columns to compare 
        e.g.: [[0,1], [1,2]] compares column 0 with 1 and 1 with 2
    subplot: None or int
        specifies if the figures has subplots and what subplot to add the notation to
    _format: dict
        format characteristics for the lines

    Returns:
    -------
    fig: figure
        figure with the added notation
    '''
    # Specify in what y_range to plot for each pair of columns
    y_range = np.zeros([len(array_columns), 2])
    for i in range(len(array_columns)):
        y_range[i] = [1.01+i*_format['interline'], 1.02+i*_format['interline']]

    # Get values from figure
    fig_dict = fig.to_dict()

    # Get indices if working with subplots
    if subplot:
        if subplot == 1:
            subplot_str = ''
        else:
            subplot_str =str(subplot)
        indices = [] #Change the box index to the indices of the data for that subplot
        for index, data in enumerate(fig_dict['data']):
            #print(index, data['xaxis'], 'x' + subplot_str)
            if data['xaxis'] == 'x' + subplot_str:
                indices = np.append(indices, index)
        indices = [int(i) for i in indices]
        print((indices))
    else:
        subplot_str = ''

    # Print the p-values
    for index, column_pair in enumerate(array_columns):
        if subplot:
            data_pair = [indices[column_pair[0]], indices[column_pair[1]]]
        else:
            data_pair = column_pair

        # Mare sure it is selecting the data and subplot you want
        #print('0:', fig_dict['data'][data_pair[0]]['name'], fig_dict['data'][data_pair[0]]['xaxis'])
        #print('1:', fig_dict['data'][data_pair[1]]['name'], fig_dict['data'][data_pair[1]]['xaxis'])

        # Get the p-value
        pvalue = stats.ttest_ind(
            fig_dict['data'][data_pair[0]]['y'],
            fig_dict['data'][data_pair[1]]['y'],
            equal_var=False,
        )[1]
        if pvalue >= 0.05:
            symbol = 'ns'
        elif pvalue >= 0.01: 
            symbol = '*'
        elif pvalue >= 0.001:
            symbol = '**'
        else:
            symbol = '***'
        # Vertical line
        fig.add_shape(type="line",
            xref="x"+subplot_str, yref="y"+subplot_str+" domain",
            x0=column_pair[0], y0=y_range[index][0], 
            x1=column_pair[0], y1=y_range[index][1],
            line=dict(color=_format['color'], width=2,)
        )
        # Horizontal line
        fig.add_shape(type="line",
            xref="x"+subplot_str, yref="y"+subplot_str+" domain",
            x0=column_pair[0], y0=y_range[index][1], 
            x1=column_pair[1], y1=y_range[index][1],
            line=dict(color=_format['color'], width=2,)
        )
        # Vertical line
        fig.add_shape(type="line",
            xref="x"+subplot_str, yref="y"+subplot_str+" domain",
            x0=column_pair[1], y0=y_range[index][0], 
            x1=column_pair[1], y1=y_range[index][1],
            line=dict(color=_format['color'], width=2,)
        )
        ## add text at the correct x, y coordinates
        ## for bars, there is a direct mapping from the bar number to 0, 1, 2...
        fig.add_annotation(dict(font=dict(color=_format['color'],size=14),
            x=(column_pair[0] + column_pair[1])/2,
            y=y_range[index][1]*_format['text_height'],
            showarrow=False,
            text=symbol,
            textangle=0,
            xref="x"+subplot_str,
            yref="y"+subplot_str+" domain"
        ))
    return fig

如果我们现在创建一个图形并测试该函数，我们应该会得到以下输出。

from scipy import stats
import plotly.express as px
import plotly.graph_objects as go
import numpy as np

tips = px.data.tips()

fig = go.Figure()
for day in ['Thur','Fri','Sat','Sun']:
    fig.add_trace(go.Box(
        y=tips[tips['day'] == day].total_bill,
        name=day,
        boxpoints='outliers'
    ))
fig = add_p_value_annotation(fig, [[0,1], [0,2], [0,3]])
fig.show()

这篇关于插图框p值重要批注的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！