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)?
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