An Introduction to AD Model Builder PFRP
Instructors Anders Nielsen (Technical University of Denmark, DTU-Aqua) Johnoel Ancheta (Pelagic Fisheries Research Program, PFRP) Mark Maunder (Inter-American Tropical Tuna Commission, IATTC)
Introduce yourself Name Organization Main research
Questionnaire What do you know Remember to ask Participants about WinBUGs.
What is AD Model Builder Tool for developing nonlinear models Efficient estimation of model parameters C++ libraries Template
Simplifying the development of models Removes the need to manage the interface between the model parameters and function minimizer. The template makes it easy to input and output data from the model, set up the parameters to estimate, and set up objective function to optimize (minimize). Adding additional estimable parameters or converting fixed parameters into estimable parameters is a simple process. ADMB is very flexible as model code is in C++ Experienced C++ programmers to create their own libraries
Efficient and stable function minimizer Analytical derivatives –Adjoint code –Chain rule More efficient and stable than other packages that use finite difference approximation. Stepwise process to sequentially estimate the parameters Bounds on all estimated parameters that restrict the range of possible parameter values.
MCMC algorithm for Bayesian integration Starts at the mode of the posterior reduces the burn-in time. Jumping rules based on the variance- covariance estimates at the mode of the posterior distribution
Automated likelihood profiles Normal approximation of confidence intervals based on the Hessian matrix and derived quantities using the delta method Automatically calculate likelihood profiles for model parameters and derived quantities producing asymmetrical confidence intervals
Random effects parameters Random effects parameters implemented using Laplace’s approximation (and importance sampling) Automatic analytical second derivatives. Use for process error or meta analysis
Matrix algebra Matrix algebra with associated precompiled adjoint code for derivative calculations Can greatly reduce computation time and memory usage compared to loops
Other features non-linear programming solver numerical integration routine random number generation high dimensional and ragged arrays estimation of the variance-covariance matrix dynamic link libraries with other software products (e.g. s- plus, Excel, Visual Basic) safe mode compiling for bounds checking ability to make ADMB C++ libraries. Parallel processing
What its good for: Highly parameterize nonlinear models Thousands of parameters Combining many data sets or analyses General Models –Stock Synthesis (Rick Methot NMFS)
What its good for: Nonlinear models with large data sets Integrating GLMs into nonlinear models
What its good for: Numerous optimizations of the objective function Simulation analysis Likelihood profiles Bootstrap/cross validation Model testing/sensitivity analysis Management strategy evaluation Numerical integration/simulated likelihood
What its good for: Nonlinear mixed effects models Crossed random effects Nonlinear state-space models.
The ADMB project Make ADMB Free Make ADMB open source Develop ADMB Facilitate the use of ADMB Promote ADMB
Outline 1.Introduction 2.Installation 3.First example 4.Likelihood based inference 5.What happens internally 6.Parameter setup 7.Data input and outputting results 8.Simulation 9.Estimating uncertainty 10.Random effects 11.Summary