有没有办法在Python中进行并行分析？

Question

我目前正在Python中运行探索性因素分析，该分析与factor_analyzer程序包（https://factor-analyzer.readthedocs.io/en/latest/factor_analyzer.html）配合良好。为了选择适当数量的因子，我使用了Kaiser准则和Scree图。但是，我想用Horn的平行分析来证实我的结果（Horn，1965）。在R中，我将使用parallel包中的psych函数。有谁知道Python中的等效方法/函数/包？我一直在寻找一段时间，但不幸的是没有成功。

非常感谢您的帮助！

最诚挚的问候

Answer 1

您现在可能已经找到了一个解决方案，但为了其他可能正在寻找它的人，这里有一些我用来模拟 psych 库中的并行分析的代码：

import pandas as pd
from factor_analyzer import FactorAnalyzer
import numpy as np
import matplotlib.pyplot as plt

def _HornParallelAnalysis(data, K=10, printEigenvalues=False):
    ################
    # Create a random matrix to match the dataset
    ################
    n, m = data.shape
    # Set the factor analysis parameters
    fa = FactorAnalyzer(n_factors=1, method='minres', rotation=None, use_smc=True)
    # Create arrays to store the values
    sumComponentEigens = np.empty(m)
    sumFactorEigens = np.empty(m)
    # Run the fit 'K' times over a random matrix
    for runNum in range(0, K):
        fa.fit(np.random.normal(size=(n, m)))
        sumComponentEigens = sumComponentEigens + fa.get_eigenvalues()[0]
        sumFactorEigens = sumFactorEigens + fa.get_eigenvalues()[1]
    # Average over the number of runs
    avgComponentEigens = sumComponentEigens / K
    avgFactorEigens = sumFactorEigens / K

    ################
    # Get the eigenvalues for the fit on supplied data
    ################
    fa.fit(data)
    dataEv = fa.get_eigenvalues()
    # Set up a scree plot
    plt.figure(figsize=(8, 6))

    ################
    ### Print results
    ################
    if printEigenvalues:
        print('Principal component eigenvalues for random matrix:\n', avgComponentEigens)
        print('Factor eigenvalues for random matrix:\n', avgFactorEigens)
        print('Principal component eigenvalues for data:\n', dataEv[0])
        print('Factor eigenvalues for data:\n', dataEv[1])
    # Find the suggested stopping points
    suggestedFactors = sum((dataEv[1] - avgFactorEigens) > 0)
    suggestedComponents = sum((dataEv[0] - avgComponentEigens) > 0)
    print('Parallel analysis suggests that the number of factors = ', suggestedFactors , ' and the number of components = ', suggestedComponents)


    ################
    ### Plot the eigenvalues against the number of variables
    ################
    # Line for eigenvalue 1
    plt.plot([0, m+1], [1, 1], 'k--', alpha=0.3)
    # For the random data - Components
    plt.plot(range(1, m+1), avgComponentEigens, 'b', label='PC - random', alpha=0.4)
    # For the Data - Components
    plt.scatter(range(1, m+1), dataEv[0], c='b', marker='o')
    plt.plot(range(1, m+1), dataEv[0], 'b', label='PC - data')
    # For the random data - Factors
    plt.plot(range(1, m+1), avgFactorEigens, 'g', label='FA - random', alpha=0.4)
    # For the Data - Factors
    plt.scatter(range(1, m+1), dataEv[1], c='g', marker='o')
    plt.plot(range(1, m+1), dataEv[1], 'g', label='FA - data')
    plt.title('Parallel Analysis Scree Plots', {'fontsize': 20})
    plt.xlabel('Factors/Components', {'fontsize': 15})
    plt.xticks(ticks=range(1, m+1), labels=range(1, m+1))
    plt.ylabel('Eigenvalue', {'fontsize': 15})
    plt.legend()
    plt.show();

如果你这样称呼上面的：

_HornParallelAnalysis(myDataSet)

您应该得到如下内容：

并行分析的示例输出：