[R] generalized least squares with empirical error covariance matrix

[Roy Mendelssohn]

Look at "DLM".  it can do bayesian dynamic linear models, ie. the  
bayes equivalent of kalman filtering.

-Roy M.
On May 9, 2007, at 1:09 PM, Andrew Schuh wrote:

> I have a bayesian hierarchical normal regression model, in which the
> regression coefficients are nested, which I've wrapped into one
> regression framework, y = X %*% beta + e .  I would like to run data
> through the model in a filter style (kalman filterish), updating
> regression coefficients at each step new data can be gathered.  After
> the first filter step, I will need to be able to feed the a non- 
> diagonal
> posterior covariance in for the prior of the next step.  "gls" and  
> "glm"
> seem to be set up to handle structured error covariances, where  
> mine is
> more empirical, driven completely by the data.  Explicitly solving w/
> "solve" is really sensitive to small values in the covariance  
> matrix and
> I've only been able to get reliable results at the first step by using
> weighted regression w/ lm().  Am I missing an obvious function for
> linear regression w/ a correlated  prior on the errors for the  
> updating
> steps?  Thanks in advance for any advice.
>
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> and provide commented, minimal, self-contained, reproducible code.

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Roy Mendelssohn
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NOAA/NMFS
Environmental Research Division	
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