[R-sig-ME] Variance components analysis using a GLMM, how to insert a variance-covariance matrix in the model ?
REZOUKI CELIA p1314815
celia.rezouki at etu.univ-lyon1.fr
Wed Jan 28 10:17:18 CET 2015
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
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:
Any help would be greatly appreciated.
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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