Class material: webpage
Week 8: Estimating interactions (the B matrix)
This week, we discuss issues related to the estimation of the B matrix in the context of using it to represent species interactions in a community dynamics models.
* univariate discrete time Gompertz model
* multivariate discrete time Gompertz model
* including covariates
* spurious density dependence resulting from ignoring observation error
* uncertainty in B elements resulting from estimating observation variance
* different methods for estimating confidence intervals: bootstrapping, hessian approximation, profile likelihood
* diagnostics
Lab 8
The main lab is to go through case study 7 in the MARSS User Guide and the corresponding code.
Lecture 8
You can find the pdf of lecture on the class webpage.
Week 8: Estimating interactions (the B matrix)
This week, we discuss issues related to the estimation of the B matrix in the context of using it to represent species interactions in a community dynamics models.
* univariate discrete time Gompertz model
* multivariate discrete time Gompertz model
* including covariates
* spurious density dependence resulting from ignoring observation error
* uncertainty in B elements resulting from estimating observation variance
* different methods for estimating confidence intervals: bootstrapping, hessian approximation, profile likelihood
* diagnostics
Lab 8
The main lab is to go through case study 7 in the MARSS User Guide and the corresponding code.
Lecture 8
You can find the pdf of lecture on the class webpage.
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