ctdcal.fit_ctd.multivariate_fit¶
- ctdcal.fit_ctd.multivariate_fit(y, *args, coef_names=None, const_name='c0')[source]¶
Least-squares fit data using multiple dependent variables. Dependent variables must be provided in tuple pairs of (data, order) as positional arguments.
If coef_names are defined, coefficients will be returned as a dict. Otherwise, coefficients are return as an array in the order of the dependent variables, sorted by decreasing powers.
- Parameters
- Returns
coefs – Least-squares fit coefficients in decreasing powers
- Return type
array-like
Examples
Behavior when coef_names is None:
>>> z = [1, 4, 9] >>> x = [1, 3, 5] >>> y = [1, 2, 3] >>> multivariate_fit(z, (x, 2), (y, 1)) array([0.25, 0.375, 0.25, 0.125]) # [c1, c2, c3, c4]
where z = (c1 * x ** 2) + (c2 * x) + (c3 * y) + c4
Behavior when coef_names is given:
>>> z = [1, 4, 9] >>> x = [1, 3, 5] >>> y = [1, 2, 3] >>> multivariate_fit(z, (x, 2), (y, 1), coef_names=["a", "b"]) {"a2": 0.25, "a1": 0.375, "b1": 0.25, "c0": 0.125}
where z = (a2 * x ** 2) + (a1 * x) + (b1 * y) + c0