[R-sig-ME] Variance components analysis using a GLMM, how to insert a variance-covariance matrix in the model ?

[ONKELINX, Thierry]

Dear Celia,

Do you have just one phi per year? Note that you need multiple observations per random effect level to fit a mixed model.

How do you want to incorporate Rcov into the model. Writing the model as a mathematical expression would clarify things.

Best regards,

ir. Thierry Onkelinx
Instituut voor natuur- en bosonderzoek / Research Institute for Nature and Forest
team Biometrie & Kwaliteitszorg / team Biometrics & Quality Assurance
Kliniekstraat 25
1070 Anderlecht
Belgium
+ 32 2 525 02 51
+ 32 54 43 61 85
Thierry.Onkelinx op inbo.be
www.inbo.be

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~ Sir Ronald Aylmer Fisher

The plural of anecdote is not data.
~ Roger Brinner

The combination of some data and an aching desire for an answer does not ensure that a reasonable answer can be extracted from a given body of data.
~ John Tukey


-----Oorspronkelijk bericht-----
Van: R-sig-mixed-models [mailto:r-sig-mixed-models-bounces op r-project.org] Namens REZOUKI CELIA p1314815
Verzonden: woensdag 28 januari 2015 10:17
Aan: r-sig-mixed-models op r-project.org
Onderwerp: [R-sig-ME] Variance components analysis using a GLMM, how to insert a variance-covariance matrix in the model ?

Dear list,

We are analysing the survival rates of a mammalian species from a capture-mark-recapture protocol. As a biologist, the usual way to proceed is to analyse capture histories (raw data) with a specific software named MARK (http://www.phidot.org/software/mark/) to run capture-mark-recapture analyses.

Our problem is to get an estimation of a random effect of time using linear mixed models, not from the observed data, but from a coefficient vector (let's call it 'phi') representing annual estimates of the survival rates, and the empirical variance/covariance matrix (Rcov) obtained from MARK.

We would like to use the output of the analyse (phis and Rcov) from MARK in a linear mixed-model in R to extract both a variance components and eventually, to model linear effects of different covariates such as time. The response variable being a proportion, it would be best to use a binomial family and hence, a generalized version of the mixed models.

The model would look like:
- response variable: logit(phi_t), the annual survival estimated from MARK
- fixed effects : temporal trends (year entered as a covariable)
- random effects : variance in survival around the temporal trend
- Rcov, the empirical variance/covariance matrix from MARK is known and should be entered into the GLMM.

It is unclear to us whether such an analysis is doable in R or not. The closest we found would be to use mcmcglmm but we would need confirmation and somes hint to start.

In case you want to help, you can get a vector of estimated survival rates along with the empirical variance/covariance matrix returned by MARK from a subsample of our data here:

load(url("http://mammal-research.org/data/example.RData"))

Any help would be greatly appreciated.

Célia


--

Célia Rezouki
PhD student

UMR CNRS 5558 - LBBE
Biométrie et Biologie Évolutive
UCB Lyon 1 - Bât. Grégor Mendel
43 bd du 11 novembre 1918
69622 VILLEURBANNE cedex
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