Phil Steitz wrote:
No, just X. see the references here:
http://apache.markmail.org/message/3aybm5emimg5da42
I think R uses QR as described above. Comments or suggestions for other
default implementations are most welcome. We should aim to provide a
default implementation that is reasonably fast and provides good
numerics across a broad range of design matrices.
Ok - noted. I'll take a look at numerics issue during the week.
We do need to decide what the API is, so even if it takes a while to
implement things, or the initial implementations are naive, we should
decide what statistics we are going to provide and how we are going to
provide them. Same for the specification of models (i.e., "input data")
Yes - agreed, but meant to say that before we start adding these methods
to the interfaces, we should decide the whole list of statistics and
input data - and that can be done on a wiki page, where people can
add/comment.
Perhaps it would help if we had overloaded newData methods that accept
different input strategies, but ultimately they will produce a n x m
double array. That way we can provide users with choice.
I was thinking the same thing.
Ok
The bit that is troubling me is the
omega matrix required by GLS cluttering the OLS interface. Other types
of models (e.g. weighted) will require other data. Could be we need
separate interfaces for the different types of regression, but maybe it
is better to dispense with the abstract interface altogether. The
reason we have interface / implementation separation is to allow
alternative implementations to be plugged in. Given the 2.0 approach to
support IOC, what may make more sense is to just encapsulate the core
model estimators (things like R's lm, gls), make them pluggable via
setters or constructors and get rid of the abstract interface. Any
thoughts on this?
I see your point. What made me fall on the side of a unified interface
was that OLS could be seen as special case of GLS. But yes the
covariance muddles the OLS case. I still think an interface defining
the common statistics available from the different types of regression
might be useful. We would just not add the data input to the interface,
which would instead be implementation specific.
I'm all for pluggable/IOC approaches, but I fail to see how this would
get rid of the interface.
Cheers
---------------------------------------------------------------------
To unsubscribe, e-mail: [EMAIL PROTECTED]
For additional commands, e-mail: [EMAIL PROTECTED]
