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I am working on principal component analysis of a matrix. I have already found the component matrix shown below

A = np.array([[-0.73465832 -0.24819766 -0.32045055]
              [-0.3728976   0.58628043 -0.63433607]
              [-0.72617152  0.53812819 -0.22846634]
              [ 0.34042864 -0.08063226 -0.80064174]
              [ 0.8804307   0.17166265  0.04381426]
              [-0.66313032  0.54576874  0.37964986]
              [ 0.286712    0.68305196  0.21769803]
              [ 0.94651412  0.14986739 -0.06825887]
              [ 0.40699665  0.73202276 -0.08462949]])

I need to perform varimax rotation in this component matrix but could not find the exact method and degree to rotate. Most of the examples are shown in R. However I need the method in python.

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2 Answers

up vote 1 down vote

Wikipedea has an example in python here!

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Actually I was looking for using numpy instead of scipy but thanx anyway. i have already looked into that – Rajsubit 33 mins ago
numpy doesn't have a svd method - you may have to seem if there is the same by another name or in another module. – Steve Barnes 12 mins ago
up vote 1 down vote

You can find a lot of examples even with Python. Here is an example I found for Python on Wikipedia:

def varimax(Phi, gamma = 1, q = 20, tol = 1e-6):
from scipy import eye, asarray, dot, sum, svd
p,k = Phi.shape
R = eye(k)
d=0
for i in xrange(q):
    d_old = d
    Lambda = dot(Phi, R)
    u,s,vh = svd(dot(Phi.T,asarray(Lambda)**3 - (gamma/p) * dot(Lambda, diag(diag(dot(Lambda.T,Lambda))))))
    R = dot(u,vh)
    d = sum(s)
    if d/d_old < tol: break
return dot(Phi, R)
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