.. _docs-meteoinfolab-numeric-funcitons-corrcoef:
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corrcoef
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.. currentmodule:: mipylib.numeric.minum
.. function:: corrcoef(x, y)
Return Pearson product-moment correlation coefficients.
The relationship between the correlation coefficient matrix, `R`, and the
covariance matrix, `C`, is
.. math:: R_{ij} = \frac{C_{ij}} {\sqrt{C_{ii} * C_{jj}}}
The values of `R` are between -1 and 1, inclusive.
:param x: (*array_like*) A 1-D or 2-D array containing multiple variables and observations.
Each row of x represents a variable, and each column a single observation of all those
variables.
:param y: (*array_like*) An additional set of variables and observations. y has the same
shape as x.
:returns: The correlation coefficient matrix of the variables.
**Examples**
::
y = [29.81,30.04,41.7,43.71,28.75,37.73,52.25,32.41,25.67,28.17,25.71,36.05,37.62,34.28,38.82,40.15,35.69,28.36,39.56,52.56,54.14,50.76,39.35,43.16]
x = [51.6,46,64.3,83.4,65.9,49.5,88.6,101.4,55.9,41.8,33.4,57.3,66.5,40.5,72.3,70,83.3,65.8,63.1,83.4,102,94,77,77]
r = corrcoef(x, y)
print r
y1 = array(x) * 2
r1 = corrcoef(x, y1)
print r1
Output:
::
>>> run script...
array([[1.0, 0.7007980346679688]
[0.7007980346679688, 1.0]])
array([[1.0, 1.0]
[1.0, 1.0]])