[R] Linear model with hidden variables

[Ingmar Visser]

Richard,
I'm not sure I fully understand your question but the flexmix package does
mixtures of linear models which may be what you want.
Hth, Ingmar


> From: "Richard A. O'Keefe" <ok at cs.otago.ac.nz>
> Date: Wed, 4 Oct 2006 18:36:55 +1300 (NZDT)
> To: <r-help at stat.math.ethz.ch>
> Subject: [R] Linear model with hidden variables
> 
> 
> I have some data on a moving vehicle where, amongst other things,
> it looks as though it would be informative to fit a model with the
> following structure:
> 
>     Z = B.Y + errorz
>     Y = C.X + errorz
> 
> The X variables are observed predictor variables;
> 6 of the variables look promising (on the basis of what they mean).
> 
> The Z variables are observed response variables;
> there are 4 of them.
> 
> There is a priori reason to believe, and scatterplots to suggest,
> that the Z variables are really essentially two-dimensional, so
> 
> the Y variables are "hidden" intermediate variables.
> There should be 2 of them.  There are actually two physical candidates
> for what they might be, but they happen not to have been measured.
> 
> There are 800 cases.  (More precisely, there are 14 periods, each with
> about 800 samples, and I am interested in fitting a separate model in
> each period.)
> 
> A simple least squares fit for this model would minimise the sum of
> error squares for the Zs.  Oh, I do mean there to be constant terms.
> 
> I suppose I could go back to first principles and work it all out,
> but has anyone ever done something like this in R?  There seems to
> be every imaginable variation on lm and some that I find unimaginable,
> so presumably a means to do this is already around somewhere.
> 
> ______________________________________________
> R-help at stat.math.ethz.ch mailing list
> 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.