Fine with me :-) This would probably simplify the docs a bit too.


On Wed, Jun 4, 2014 at 12:56 PM, broessli <notifications@github.com> wrote:

> In patsy/mgcv_cubic_splines.py:
>
> > +     - ``cc(x, k=None, cons=None)`` for cyclic cubic regression spline
> > +
> > +    Each of these stateful transforms generate a cubic spline basis for ``x``
> > +    (with the option of absorbing centering or more general parameters
> > +    constraints), allowing non-linear fits. The usual usage is something like::
> > +
> > +      y ~ 1 + cr(x, k=5, cons=True)
> > +
> > +    to fit ``y`` as a smooth function of ``x``, with a 5-dimensional basis
> > +    used to represent the smooth term, and centering constraint absorbed in
> > +    the resulting design matrix.
> > +
> > +    :arg k: Either the dimension of the spline basis or the list of knots used
> > +     to represent the smooth term. If only the dimension is provided, equally
> > +     spaced quantiles of the input data are used as knots and will be remembered
> > +     and re-used for prediction from the fitted model.
>
> In the same spirit we could drop cs since this is again too *mgcv*
> specific ?
>
> —
> Reply to this email directly or view it on GitHub
> <https://github.com/pydata/patsy/pull/43/files#r13381913>.
>



-- 
Nathaniel J. Smith
Postdoctoral researcher - Informatics - University of Edinburgh
http://vorpus.org