[R] regression with uncertainty in both variables

[Stuart Luppescu]

On 15-Apr-99 Matthew Wiener wrote:
> Hi, all.
> 
> I'm trying to use some linear regression models in which both the
> dependent and independent variables are measured with some error.  To
> make things worse, while the errors in the dependent variable are uniform,
> the errors in the independent (or explanatory, or "x") variables can be
> heteroskedastic.  I've been looking at the book _Measurement Error Models_
> by Fuller (1987).  I'm wondering whether anybody knows any other
> references on the subject, and whether anyone has written S or R code that
> handles these kinds of problems.  (As far as I can tell, the usual lm and
> glm functions don't; if I'm wrong, that's great.)

This really has nothing to do with R (I don't think, anyway -- could it be done
with lme?), but we do this kind of thing all the time using the HLM program
(Bryk, Raudenbush and Congden). At level 1, the outcome is the measure divided
by the standard error, and a series of dummies, one for each type of measure,
divided by the standard error. The level 1 variance is fixed at 1.0. These
1/s.e.,  come down to level 2, where they can be considered ``true score''
estimates of the measures, as outcomes, or as predictors, if you use  the
latent variable regression capability of HLM. I believe this capability is in
the latest version of the HLM program (maybe not -- we use a pre-release
version here), and the procedure should be detailed in the 2nd edition of Bryk
and Raudenbush, _Hierarchical Linear Models_, to be published (by Sage again?)
this summer or fall.

______________________________________________________________________
Stuart Luppescu         -=-=-  University of Chicago
ºÍʸ ¤ÈÃÒÆàÈþ¤ÎÉã(EUC)  -=-=-  s-luppescu at uchicago.edu
http://www.consortium-chicago.org/people/sl/sl.html
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>> Sent on 15-Apr-99 at 09:22:12 with xfmail
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