# mars.tensor.corrcoef#

mars.tensor.corrcoef(x, y=None, rowvar=True)[source]#

Return Pearson product-moment correlation coefficients.

Please refer to the documentation for cov for more detail. 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. Also see rowvar below.

• y (array_like, optional) – An additional set of variables and observations. y has the same shape as x.

• rowvar (bool, optional) – If rowvar is True (default), then each row represents a variable, with observations in the columns. Otherwise, the relationship is transposed: each column represents a variable, while the rows contain observations.

Returns

R – The correlation coefficient matrix of the variables.

Return type

Tensor

cov