[SciPy-dev] Stats module (was Problem with stats.distributions.randint)

southey at ux1.cso.uiuc.edu southey at ux1.cso.uiuc.edu
Mon Jan 27 14:54:09 CST 2003


General linear model is probably the most important since things like
regression and analysis of (co)variance can be fitted in this framework.
The current option is rather limited.

There needs to be some general consensus on how this would work and the
resulting output. I see three parts:
a) A model stage that defines the terms in the model - mainly to provide
the design matrix X.
b) The engine that sets the system of equations and solves it. The easy
approach is find inverse of X'X and solve b=inv(X'X)X'Y.  Alternatively
use a sweep function.
c) The output that contains the requested outputs - provides
standard errors, reduction in sum of squares, contrasts, estimates,

The following build upon general linear models:
1) Mixed models
2) Generalized linear models - fits distributions like binomial and
3) Non-linear models


On Mon, 27 Jan 2003, Travis Oliphant wrote:

> > I would be interested in this aspect as well because I am interested in
> > becoming involved with the stats module.
> >
> > If not can we set one up?
> >
> Absolutely,  If you have ideas please list them.  Later today, I will
> think up my own list.
> -Travis O.
> ===================
> Travis Oliphant
> Assistant Professor
> 459 CB
> Electrical and Computer Engineering
> Brigham Young University
> Provo, UT 84602
> Tel: (801) 422-3108
> oliphant.travis at ieee.org
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