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Phil Steitz commented on MATH607:

Thanks, I forgot to mention that important point. Initially, we took the "take what we are
given" approach, but that proved confusing and errorprone for users (forcing them to add
unitary columns to input data). I think it is best to expect no unitary columns in the design
matrix and have the user explicitly specify "noIntercept" to estimate a model without an intercept
term. This is how the MultipleLinearRegression impls now work. (See the javadoc for newSampleData
in AbstractMultipleLinearRegression). In the updating impls, this can work the same way,
allowing users to omit initial "1"s from added rows. I guess this will have to be a constructor
parameter to work correctly in the updating impls.
Another thing I forgot to mention is careful specification and validation of array shape constraints
on input data (i.e., when things have to be rectangular and/or of length = previously determined
nVars. I liked the lack of a setter for the number of explanatory variables, but that means
the first addData becomes definitional.
One final suggestion  maybe the row version of addData should be addObservation and the matrix
version should be addObservations.
> Current Multiple Regression Object does calculations with all data incore. There are
non incore techniques which would be useful with large datasets.
> 
>
> Key: MATH607
> URL: https://issues.apache.org/jira/browse/MATH607
> Project: Commons Math
> Issue Type: New Feature
> Affects Versions: 3.0
> Environment: Java
> Reporter: greg sterijevski
> Labels: Gentleman's, QR, Regression, Updating, decomposition, lemma
> Fix For: 3.0
>
> Attachments: updating_reg_ifaces
>
> Original Estimate: 840h
> Remaining Estimate: 840h
>
> The current multiple regression class does a QR decomposition on the complete data set.
This necessitates the loading incore of the complete dataset. For large datasets, or large
datasets and a requirement to do datamining or stepwise regression this is not practical.
There are techniques which form the normal equations on the fly, as well as ones which form
the QR decomposition on an update basis. I am proposing, first, the specification of an "UpdatingLinearRegression"
interface which defines basic functionality all such techniques must fulfill.
> Related to this 'updating' regression, the results of running a regression on some subset
of the data should be encapsulated in an immutable object. This is to ensure that subsequent
additions of observations do not corrupt or render inconsistent parameter estimates. I am
calling this interface "RegressionResults".
> Once the community has reached a consensus on the interface, work on the concrete implementation
of these techniques will take place.
> Thanks,
> Greg

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