statsmodels.tools.eval_measures.vare#

statsmodels.tools.eval_measures.vare(x1, x2, ddof=0, axis=0)[source]#

Variance of error

Parameters:

x1, x2array_like: The performance measure depends on the difference between these two arrays.
ddofint: Delta degrees of freedom used in the variance calculation.
axisint: axis along which the summary statistic is calculated

Returns:

varendarray or float: variance of difference along given axis.

Notes

If x1 and x2 have different shapes, then they need to broadcast. This uses numpy.asanyarray to convert the input. Whether this is the desired result or not depends on the array subclass.