Return Pearson product-moment correlation coefficients. Except for the handling of missing data this function does the same as `numpy.corrcoef`. For more details and examples, see `numpy.corrcoef`. Parameters ---------- x : array_like A 1-D or 2-D array containing
(x, y=None, rowvar=True, allow_masked=True,
)
| 1673 | |
| 1674 | |
| 1675 | def corrcoef(x, y=None, rowvar=True, allow_masked=True, |
| 1676 | ): |
| 1677 | """ |
| 1678 | Return Pearson product-moment correlation coefficients. |
| 1679 | |
| 1680 | Except for the handling of missing data this function does the same as |
| 1681 | `numpy.corrcoef`. For more details and examples, see `numpy.corrcoef`. |
| 1682 | |
| 1683 | Parameters |
| 1684 | ---------- |
| 1685 | x : array_like |
| 1686 | A 1-D or 2-D array containing multiple variables and observations. |
| 1687 | Each row of `x` represents a variable, and each column a single |
| 1688 | observation of all those variables. Also see `rowvar` below. |
| 1689 | y : array_like, optional |
| 1690 | An additional set of variables and observations. `y` has the same |
| 1691 | shape as `x`. |
| 1692 | rowvar : bool, optional |
| 1693 | If `rowvar` is True (default), then each row represents a |
| 1694 | variable, with observations in the columns. Otherwise, the relationship |
| 1695 | is transposed: each column represents a variable, while the rows |
| 1696 | contain observations. |
| 1697 | allow_masked : bool, optional |
| 1698 | If True, masked values are propagated pair-wise: if a value is masked |
| 1699 | in `x`, the corresponding value is masked in `y`. |
| 1700 | If False, raises an exception. Because `bias` is deprecated, this |
| 1701 | argument needs to be treated as keyword only to avoid a warning. |
| 1702 | |
| 1703 | See Also |
| 1704 | -------- |
| 1705 | numpy.corrcoef : Equivalent function in top-level NumPy module. |
| 1706 | cov : Estimate the covariance matrix. |
| 1707 | |
| 1708 | Examples |
| 1709 | -------- |
| 1710 | >>> import numpy as np |
| 1711 | >>> x = np.ma.array([[0, 1], [1, 1]], mask=[0, 1, 0, 1]) |
| 1712 | >>> np.ma.corrcoef(x) |
| 1713 | masked_array( |
| 1714 | data=[[--, --], |
| 1715 | [--, --]], |
| 1716 | mask=[[ True, True], |
| 1717 | [ True, True]], |
| 1718 | fill_value=1e+20, |
| 1719 | dtype=float64) |
| 1720 | |
| 1721 | """ |
| 1722 | # Estimate the covariance matrix. |
| 1723 | corr = cov(x, y, rowvar, allow_masked=allow_masked) |
| 1724 | # The non-masked version returns a masked value for a scalar. |
| 1725 | try: |
| 1726 | std = ma.sqrt(ma.diagonal(corr)) |
| 1727 | except ValueError: |
| 1728 | return ma.MaskedConstant() |
| 1729 | corr /= ma.multiply.outer(std, std) |
| 1730 | return corr |
| 1731 | |
| 1732 | #####-------------------------------------------------------------------------- |
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