[R-sig-ME] MCMCglmm error-in-variables (total least squares) model?
M.Fairbrother at bristol.ac.uk
Sat Jan 2 23:47:08 CET 2016
Dear Alberto (I believe),
To my knowledge, this is not possible in MCMCglmm (though Jarrod Hadfield,
the package author, may weigh in with another response).
A collaborator and I have been working on a paper that shows how to fit
such models in JAGS (and perhaps Stan), though thus far we've only been
able to fit such models correcting for measurement error in the predictors
at the lowest level. Multiple such predictors (including with different
measurement error variances) are no problem.
That paper, however, is probably still some months away from being finished
and presentable. In the meantime, I don't know of any good options for you.
If other subscribers to this list have any ideas, I'll be quite interested
Date: Tue, 29 Dec 2015 16:09:53 -0500
> From: Alberto Gallano <alberto.gc8 at gmail.com>
> To: r-sig-mixed-models at r-project.org
> Subject: [R-sig-ME] MCMCglmm error-in-variables (total least squares)
> I posted this question on Stack Overflow a week ago but received no
> This may be a more appropriate venue.
> I am fitting some Bayesian linear mixed models using the MCMCglmm package.
> My data includes predictors that are measured with error. I'd therefore
> like to build a model that takes this into account. My understanding is
> that a basic mixed effects model in MCMCglmm will minimize error only for
> the response variable (as in frequentist OLS regression). In other words,
> vertical errors will be minimized. Instead, I'd like to minimize errors
> orthogonal to the regression line/plane/hyperplane.
> 1. Is it possible to fit an error-in-variables (aka total least squares)
> model using MCMCglmm or would I have to use JAGS / STAN to do this?
> 2. Is it possible to do this with multiple predictors in the same model
> (I have some models with 3 or 4 predictors, each measured with error)?
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