如何使用 scipy.odr 估计拟合优度? [英] How to estimate goodness-of-fit using scipy.odr?

问题描述

我正在使用 scipy.odr 拟合数据与权重，但我不知道如何获得拟合优度或 R 平方的度量.有人对如何使用函数存储的输出获得此度量有建议吗?

I am fitting data with weights using scipy.odr but I don't know how to obtain a measure of goodness-of-fit or an R squared. Does anyone have suggestions for how to obtain this measure using the output stored by the function?

推荐答案

Output 是所谓的拟合卡方值，这是一种流行的选择拟合统计.不过，对于非线性拟合来说有些问题.您可以直接查看残差(out.delta 用于 X 残差，out.eps 用于 Y残差).如链接论文中所建议的那样，实施交叉验证或引导方法来确定拟合优度，留给读者作为练习.

The res_var attribute of the Output is the so-called reduced Chi-square value for the fit, a popular choice of goodness-of-fit statistic. It is somewhat problematic for non-linear fitting, though. You can look at the residuals directly (out.delta for the X residuals and out.eps for the Y residuals). Implementing a cross-validation or bootstrap method for determining goodness-of-fit, as suggested in the linked paper, is left as an exercise for the reader.

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