Revision 678 trunk/docs/manuals/admb-re/admbre.tex

admbre.tex (revision 678)
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Important points are emphasized with a star
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\begin{itemize}
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\item[$\bigstar$] like this.
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  \item[$\bigstar$] like this.
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\end{itemize}
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Please submit all comments and complaints by email to
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models. With \scAR, you can include random effects in your model. Examples of
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such models include:
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\begin{itemize}
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\item Generalized linear mixed models (logistic and Poisson regression).
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\item Nonlinear mixed models (growth curve models, pharmacokinetics).
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\item State space models (nonlinear Kalman filters).
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\item Frailty models in survival analysis.
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\item Nonparametric smoothing.
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\item Semiparametric modelling.
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\item Frailty models in survival analysis.
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\item Bayesian hierarchical models.
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\item General nonlinear random-effects models (fisheries catch-at-age
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models).
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  \item Generalized linear mixed models (logistic and Poisson regression).
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  \item Nonlinear mixed models (growth curve models, pharmacokinetics).
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  \item State space models (nonlinear Kalman filters).
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  \item Frailty models in survival analysis.
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  \item Nonparametric smoothing.
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  \item Semiparametric modelling.
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  \item Frailty models in survival analysis.
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  \item Bayesian hierarchical models.
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  \item General nonlinear random-effects models (fisheries catch-at-age models).
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\end{itemize}
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You formulate the likelihood function in a template file, using a language that
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resembles \cplus. The file is compiled into an executable program (on Linux or
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\subsection{The strengths of \scAR}
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\begin{itemize}
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\item \textit{Flexibility:} You can fit a large variety of models within a
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single framework.
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\item \textit{Convenience:} Computational details are transparent. Your only
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responsibility is to formulate the log-likelihood.
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\item \textit{Computational efficiency:} \scAR\ is up to 50 times faster
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than is \scWinBUGS.
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\item \textit{Robustness:} With exact derivatives, you can fit highly nonlinear
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models.
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\item \textit{Convergence diagnostic:} The gradient of the likelihood
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function provides a clear convergence diagnostic.
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  \item \textit{Flexibility:} You can fit a large variety of models within a
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  single framework.
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  \item \textit{Convenience:} Computational details are transparent. Your only
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  responsibility is to formulate the log-likelihood.
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  \item \textit{Computational efficiency:} \scAR\ is up to 50 times faster than
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  \scWinBUGS.
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  \item \textit{Robustness:} With exact derivatives, you can fit highly
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  nonlinear models.
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  \item \textit{Convergence diagnostic:} The gradient of the likelihood function
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  provides a clear convergence diagnostic.
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\end{itemize}
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\subsection{Program interface}
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\begin{itemize}
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\item\textit{Model formulation}: You fill in a \cplus-based template using your
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favorite text editor.
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  \item\textit{Model formulation}: You fill in a \cplus-based template using
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  your favorite text editor.
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\item \textit{Compilation}: You turn your model into an executable program using
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a  \cplus\ compiler (which you need to install separately).
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  \item \textit{Compilation}: You turn your model into an executable program
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  using a \cplus\ compiler (which you need to install separately).
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\item\textit{Platforms}: Windows and Linux
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  \item\textit{Platforms}: Windows and Linux.
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\end{itemize}
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\subsection{How to obtain \scAR}
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to data:
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\begin{itemize}
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\item Vector-matrix arithmetic and vectorized operations for common mathematical
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functions.
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\item Reading and writing vector and matrix objects to a file.
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\item Fitting the model is a stepwise manner (with ``phases''), where more and
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more parameters become active in the minimization.
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\item Calculating standard deviations of arbitrary functions of the model
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parameters by the ``delta method.''
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\item \scMCMC\ sampling around the posterior mode.
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  \item Vector-matrix arithmetic and vectorized operations for common
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  mathematical functions.
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  \item Reading and writing vector and matrix objects to a file.
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  \item Fitting the model is a stepwise manner (with ``phases''), where more and
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  more parameters become active in the minimization.
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  \item Calculating standard deviations of arbitrary functions of the model
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