在Python/Numpy中包含NAN的数组的线性回归 [英] Linear regression of arrays containing NANs in Python/Numpy

问题描述

我有两个数组，比如说varx和variant.两者在不同位置都包含NAN值.但是，我想对两者进行线性回归，以显示两个数组之间的相关程度. 到目前为止，这非常有帮助: http://glowingpython.blogspot.de/2012/03 /linear-regression-with-numpy.html

I have two arrays, say varx and vary. Both contain NAN values at various positions. However, I would like to do a linear regression on both to show how much the two arrays correlate. This was very helpful so far: http://glowingpython.blogspot.de/2012/03/linear-regression-with-numpy.html

但是，使用此方法:

slope, intercept, r_value, p_value, std_err = stats.linregress(varx, vary)

为每个输出变量得出nans.最简单的方法是仅将两个数组中的有效值用作线性回归的输入?我听说过遮罩数组，但不确定其工作原理.

results in nans for every output variable. What is the most convenient way to take only valid values from both arrays as input to the linear regression? I heard about masking arrays, but am not sure how it works exactly.

推荐答案

您可以使用遮罩删除NaN:

You can remove NaNs using a mask:

mask = ~np.isnan(varx) & ~np.isnan(vary)
slope, intercept, r_value, p_value, std_err = stats.linregress(varx[mask], vary[mask])

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