[R-sig-ME] MCMCglmm error-in-variables (total least squares) model?
Jarrod Hadfield
j.hadfield at ed.ac.uk
Sun Jan 3 17:16:12 CET 2016
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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>>>
>>>
>>>
>>
>>
>> --
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>> Scotland, with registration number SC005336.
>>
>>
>>
>
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