Question

我对编程和统计有点新，所以如果正式不正确，请帮助我改进这个问题。

我有很多参数和一些我在MonteCarlo模拟中生成的结果向量。现在我想测试每个参数对结果的影响。我已经有了一个与Kendall的Tau合作的剧本。现在我想与Spearman和Pearson rho进行比较。一个例子：

from scipy.stats import spearmanr, kendalltau, pearsonr
result = [106, 86, 100, 101, 99, 103, 97, 113, 112, 110]
parameter = ['A', 'B', 'A', 'B', 'A', 'B', 'A', 'B', 'A', 'B']
kendalltau(parameter, result)

>> (0.14907119849998596, 0.54850624613917143)

但是，如果我为spearmanr或pearsonr尝试相同操作，我会收到错误消息。显然，此功能未在Scipy中实现。您是否知道获取分类数据的相关系数的简单方法？

Answer 1

实际上spearmanr有效，但是pearsonr不需要计算数组的平均值，dtype对于字符串不正确。见下文：

from scipy.stats import spearmanr, kendalltau, pearsonr

result = [106, 86, 100, 101, 99, 103, 97, 113, 112, 110]

parameter = ['A', 'B', 'A', 'B', 'A', 'B', 'A', 'B', 'A', 'B']

spearmanr(result, parameter)

（0.17407765595569782,0.63053607555697644）

help(pearsonr)

Help on function pearsonr in module scipy.stats.stats:

pearsonr(x, y)
    Calculates a Pearson correlation coefficient and the p-value for testing
    non-correlation.

    The Pearson correlation coefficient measures the linear relationship
    between two datasets. Strictly speaking, Pearson's correlation requires
    that each dataset be normally distributed. Like other correlation
    coefficients, this one varies between -1 and +1 with 0 implying no
    correlation. Correlations of -1 or +1 imply an exact linear
    relationship. Positive correlations imply that as x increases, so does
    y. Negative correlations imply that as x increases, y decreases.

    The p-value roughly indicates the probability of an uncorrelated system
    producing datasets that have a Pearson correlation at least as extreme
    as the one computed from these datasets. The p-values are not entirely
    reliable but are probably reasonable for datasets larger than 500 or so.

    Parameters
    ----------
    x : 1D array
    y : 1D array the same length as x

    Returns
    -------
    (Pearson's correlation coefficient,
     2-tailed p-value)

    References
    ----------
    http://www.statsoft.com/textbook/glosp.html#Pearson%20Correlation

转换＆＃39; A＆＃39; 1，＆＃39; B＆＃39;到2，例如

params = [1 if el == 'A' else 2 for el in parameter]

print params

[1, 2, 1, 2, 1, 2, 1, 2, 1, 2]

pearsonr(params, result)

(-0.012995783552244984, 0.97157652425566488)

希望这会有所帮助。

Python：分类数据的排序顺序相关性

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