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First get the components matrix and the noise variance once you have performed factor analysis,let fa be your fitted model. m = fa.components_ n = fa.noise_variance_ Square this matrix. m1 = m**2 Compute the sum of each of the columns of m1. m2 = np.sum(m1,axis=1) Now the %variance explained by the first factor will be. pvar1 = (100*m2[0])/np ...
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