factor_analyzer.factor_analyzer.calculate_kmo

factor_analyzer.factor_analyzer.calculate_kmo(x)

Calculate the Kaiser-Meyer-Olkin criterion for items and overall.

This statistic represents the degree to which each observed variable is predicted, without error, by the other variables in the dataset. In general, a KMO < 0.6 is considered inadequate.

Parameters

Name	Type	Description	Default
x	array - like	The array from which to calculate KMOs.	required

Returns

Name	Type	Description
kmo_per_variable	numpy.ndarray	The KMO score per item.
kmo_total	float	The overall KMO score.