Re: [R] generalized least squares with empirical error covariance matrix

From: Roy Mendelssohn <Roy.Mendelssohn_at_noaa.gov>
Date: Wed, 09 May 2007 13:16:11 -0700

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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Roy Mendelssohn
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https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code. Received on Wed 09 May 2007 - 20:30:35 GMT

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