我是编程和统计学的新手,所以如果它在形式上不正确,请帮助我改善这个问题。

我在蒙特卡洛模拟中产生了很多参数和几个结果向量。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's Tau一起工作的脚本。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,如果对spearmanrpearsonr尝试相同的操作,则会收到错误消息。显然,此功能未在Scipy中实现。您知道获取分类数据相关系数的简单方法吗?

最佳答案

实际上spearmanr可以工作,但是pearsonr不会这样做,因为它需要计算数组的均值,对于字符串,dtype是不正确的。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


例如,将“ A”转换为1,将“ B”转换为2

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)


希望这可以帮助。

关于python - Python:分类数据的排名顺序相关性,我们在Stack Overflow上找到一个类似的问题:https://stackoverflow.com/questions/26489961/

10-11 15:03