limix.stats.pca

limix.stats.pca(X, ncomp)[source]

Principal component analysis.

Parameters

  • X (array_like) – Samples-by-dimensions array.

  • ncomp (int) – Number of components.

Returns

  • components (ndarray) – First components ordered by explained variance.

  • explained_variance (ndarray) – Explained variance.

  • explained_variance_ratio (ndarray) – Percentage of variance explained.

Examples

>>> from numpy import round
>>> from numpy.random import RandomState
>>> from limix.stats import pca
>>>
>>> X = RandomState(1).randn(4, 5)
>>> r = pca(X, ncomp=2)
>>> r['components']
array([[-0.75015369,  0.58346541, -0.07973564,  0.19565682, -0.22846925],
       [ 0.48842769,  0.72267548,  0.01968344, -0.46161623, -0.16031708]])
>>> r['explained_variance'] 
array([6.44655993, 0.51454938])
>>> r['explained_variance_ratio'] 
array([0.92049553, 0.07347181])