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

[Jarrod Hadfield]

Hi Alberto,

When you say you have multiple observations for each species, do you  
mean that you have multiple observations for the response and the  
predictors? Do you expect the response and/or the predictors to be  
correlated at the observation level (for example are they measured on  
the same individuals)? I presume the answer to both these questions is  
yes if you wish to use the van de Pol method?

Cheers,

Jarrod


Quoting Alberto Gallano <alberto.gc8 at gmail.com> on Sun, 3 Jan 2016  
10:35:02 -0500:

> 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)?
>>>>
>>>>
>>>         [[alternative HTML version deleted]]
>>>
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>>>
>>>
>>>
>>
>>
>> --
>> The University of Edinburgh is a charitable body, registered in
>> Scotland, with registration number SC005336.
>>
>>
>>
>


-- 
The University of Edinburgh is a charitable body, registered in
Scotland, with registration number SC005336.