[R-sig-ME] MCMCglmm error-in-variables (total least squares) model?

Alberto Gallano alberto.gc8 at gmail.com
Sun Jan 3 16:35:02 CET 2016


Hi Jarrod,

I don't know the measurement error in the predictors in advance, so I guess
it would need to be estimated simultaneously. I'm not 100% sure what you
mean by 'multiple observations for each predictor variable'. I have data on
132 species and have multiple observations (7 to 80) for each species. I'm
using a species level random effect and a phylogenetic covariance matrix
(using ginverse) to account for phylogenetic autocorrelation, and I'm also
using van de Pol and Wright's (2009) method for partitioning slopes into
between- and within-species (i'm interested in the between species slope).
My understanding is that neither of these things fits a model in which
orthogonal residuals are minimized.

best,
Alberto

On Sun, Jan 3, 2016 at 5:24 AM, Jarrod Hadfield <j.hadfield at ed.ac.uk> wrote:

> Hi Alberto,
>
> Do you know the measurement error in the predictors in advance or do you
> have multiple observations for each predictor variable and wish to estimate
> the error simultaneously?
>
> Cheers,
>
> Jarrod
>
>
>
>
>
> Quoting Malcolm Fairbrother <M.Fairbrother at bristol.ac.uk> on Sat, 2 Jan
> 2016 14:47:08 -0800:
>
> 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
>> too!
>> - Malcolm
>>
>>
>>
>>
>>
>> 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)
>>>         model?
>>>
>>> I posted this question on Stack Overflow a week ago but received no
>>> answers:
>>>
>>>
>>>
>>> http://stackoverflow.com/questions/34446618/bayesian-error-in-variables-total-least-squares-model-in-r-using-mcmcglmm
>>>
>>> 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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>>
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>>
>>
>>
>
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