[R] generalized least squares with empirical error covariance matrix

Roy Mendelssohn Roy.Mendelssohn at noaa.gov
Wed May 9 22:16:11 CEST 2007

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.

"The contents of this message do not reflect any position of the U.S.  
Government or NOAA."
Roy Mendelssohn
Supervisory Operations Research Analyst
Environmental Research Division	
Southwest Fisheries Science Center
1352 Lighthouse Avenue
Pacific Grove, CA 93950-2097

e-mail: Roy.Mendelssohn at noaa.gov (Note new e-mail address)
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