numpy.ma.corrcoef¶
- numpy.ma.corrcoef(x, y=None, rowvar=True, bias=False, allow_masked=True, ddof=None)[source]¶
Return correlation coefficients of the input array.
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 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.
bias : bool, optional
Default normalization (False) is by (N-1), where N is the number of observations given (unbiased estimate). If bias is 1, then normalization is by N. This keyword can be overridden by the keyword ddof in numpy versions >= 1.5.
allow_masked : bool, optional
If True, masked values are propagated pair-wise: if a value is masked in x, the corresponding value is masked in y. If False, raises an exception.
ddof : {None, int}, optional
New in version 1.5.
If not None normalization is by (N - ddof), where N is the number of observations; this overrides the value implied by bias. The default value is None.
See also
- numpy.corrcoef
- Equivalent function in top-level NumPy module.
- cov
- Estimate the covariance matrix.