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

Sun Jan 3 21:45:26 CET 2016

Hi Jarrod,

yes, that's right, I have multiple measurements for both response and
predictors and these are measured on the same individuals. The model i'm
fitting is very similar to the model called "model_repeat2" from Modern
Phylogenetic Comparative Methods:

http://www.mpcm-evolution.org/practice/online-practical-material-chapter-11/chapter-11-2-multiple-measurements-model-mcmcglmm

same random effects structure, same between/within structure for the fixed
effects, and i'm also using the inverse of the matrix of phylogenetic
correlation.

@Dimitri: I'm aware of the de Villemereuil et al. approach, which, If I
understand correctly, does a version of orthogonal regression (in JAGS).
I'm trying find out if this is possible in MCMCglmm.

best,
Alberto

On Sun, Jan 3, 2016 at 12:05 PM, Dimitri Skandalis <da.skandalis at gmail.com>
wrote:

> Hi Alberto,
>
> Have you looked at the book Modern Phylogenetic Comparative Methods? R
> code provided with Chapter 11 (2) deals with correlated measurements, and
> could be a good place to start.
>
> http://www.mpcm-evolution.org/practice/online-practical-material-chapter-11
>
> Also, de Villemereuil et al. have developed an approach to related models
> in BUGS/JAGS.
>
> http://bmcevolbiol.biomedcentral.com/articles/10.1186/1471-2148-12-102
>
> Dimitri
>
> On Sun, Jan 3, 2016 at 8:16 AM, Jarrod Hadfield <j.hadfield at ed.ac.uk>
> wrote:
>
>> 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]]
>>>>>
>>>>> _______________________________________________
>>>>> R-sig-mixed-models at r-project.org mailing list
>>>>> https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
>>>>>
>>>>>
>>>>>
>>>>>
>>>>
>>>> --
>>>> 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.
>>
>> _______________________________________________
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>>
>
>

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