diff options
Diffstat (limited to 'numpy/lib/polynomial.py')
| -rw-r--r-- | numpy/lib/polynomial.py | 30 |
1 files changed, 19 insertions, 11 deletions
diff --git a/numpy/lib/polynomial.py b/numpy/lib/polynomial.py index 81af185eb..e3defdca2 100644 --- a/numpy/lib/polynomial.py +++ b/numpy/lib/polynomial.py @@ -463,9 +463,14 @@ def polyfit(x, y, deg, rcond=None, full=False, w=None, cov=False): w : array_like, shape (M,), optional Weights to apply to the y-coordinates of the sample points. For gaussian uncertainties, use 1/sigma (not 1/sigma**2). - cov : bool, optional - Return the estimate and the covariance matrix of the estimate - If full is True, then cov is not returned. + cov : bool or str, optional + If given and not `False`, return not just the estimate but also its + covariance matrix. By default, the covariance are scaled by + chi2/sqrt(N-dof), i.e., the weights are presumed to be unreliable + except in a relative sense and everything is scaled such that the + reduced chi2 is unity. This scaling is omitted if ``cov='unscaled'``, + as is relevant for the case that the weights are 1/sigma**2, with + sigma known to be a reliable estimate of the uncertainty. Returns ------- @@ -633,14 +638,17 @@ def polyfit(x, y, deg, rcond=None, full=False, w=None, cov=False): elif cov: Vbase = inv(dot(lhs.T, lhs)) Vbase /= NX.outer(scale, scale) - # Some literature ignores the extra -2.0 factor in the denominator, but - # it is included here because the covariance of Multivariate Student-T - # (which is implied by a Bayesian uncertainty analysis) includes it. - # Plus, it gives a slightly more conservative estimate of uncertainty. - if len(x) <= order + 2: - raise ValueError("the number of data points must exceed order + 2 " - "for Bayesian estimate the covariance matrix") - fac = resids / (len(x) - order - 2.0) + if cov == "unscaled": + fac = 1 + else: + if len(x) <= order: + raise ValueError("the number of data points must exceed order " + "to scale the covariance matrix") + # note, this used to be: fac = resids / (len(x) - order - 2.0) + # it was deciced that the "- 2" (originally justified by "Bayesian + # uncertainty analysis") is not was the user expects + # (see gh-11196 and gh-11197) + fac = resids / (len(x) - order) if y.ndim == 1: return c, Vbase * fac else: |