# corrcoef¶

mipylib.numeric.minum.corrcoef(x, y)

Return Pearson product-moment correlation coefficients.

The relationship between the correlation coefficient matrix, R, and the covariance matrix, C, is

$R_{ij} = \frac{C_{ij}} {\sqrt{C_{ii} * C_{jj}}}$

The values of R are between -1 and 1, inclusive.

Parameters: 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. y – (array_like) An additional set of variables and observations. y has the same shape as x. 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]])