Embedding R in Windows applications, and executing R remotely

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Implementation and Simulation of Ecological Models in R: an Interactive Approach Thomas Petzoldt ∗ February 9, 2004 In recent years, the R system has evolved to a mature environment for statistical data analysis, the development of new statistical techniques, and, together with an increasing number of books, papers and online documents, an impressing basis for learning, teaching and understanding statistical techniques. Moreover, due to the advantages of the S language and the OpenSource availability of the R software and its libraries, this system is also suitable to implement simulation models, which are common in ecology. However, there remains one problem. When implementing models of different type, e.g. differential equations or individual-based models, the result may be a lot of different simulation programs and the interactive ease of a command driven R-system gets lost. The need to edit parameter files or the sourcecode directly is a serious disadvantage, particularly when sharing such models. As a first step towards an intercheangable but easy to use format I propose a standardized liststructure to allow users to run, modify, implement, discuss and share ecological models. Each simulation model can be implemented as a list (simecol simulation model object) with an intentionally simple structure. The list has very few mandatory components, namely equations, which contains the model equations, rules or arbitrary program code, params with the constant model parameters and init which is used to define the initial state as vector, matrix, list or via a special initialization function. For dynamic models the vector times is used to define simulation interval and time steps and inputs for variable data (e.g. time dependend environmental forcings). The list component solver defines the solver to be used to simulate the model. It can be either iteration for discrete event models, an ODE-solver (e.g. lsoda) for ODE models or a user-provided algorithm. Simulation results are stored within the simecol object for later use, e.g. plotting or statistical analysis. In this way, a simecol object contains the entire dataset, that is needed to run a basic simulation simply by entering the model object via source() or data() from the harddisk or the internet and then to run and plot the model via plot(simulate(simecol.object)). As an additional advantage R-helpfiles and package vignettes can be used as usable and standardized methods to overcome the documentation dilemma. Interface functions are provided to get or set model parameters, time steps and initial values, but it is also possible to modify the components of simecol objects directly, e.g. the model equations. Compared to a strictly object oriented approach, this might seem dangerous and may lead to an inconsistent state, but on the other hand the list-based approach makes implementation and share of new models extremely simple. Using this, several applications with different types of ecological models will be demonstrated to show the interactive potential of this approach. ∗ Institute of Hydrobiology, Dresden University of Technology, 01062 Dresden, petzoldt@rcs.urz.tu-dresden.de 1
useR! – at an investment bank? Bernhard Pfaff ∗ Frankfurt, January 29, 2004 Abstract This paper describes the demands placed on the statistical and/or econometric software utilised by an investment bank. It outlines the reasons and considerations for choosing R instead of alternative programs/programming environments as the statistical software environment at Dresdner Kleinwort Wasserstein (henceforth referred to as DrKW). The features of a typical workflow process as well as examples of financial econometric models and their implementation are also discussed. The experiences and insights described in this paper have been gained from day-to-day usage of R since mid-2002. Introduction Since the beginning of the 90s not only academic interest in econometric modelling of financial markets has increased but also in todays financial industry the role and necessity of quantitative research econ Pecularities Econometric Modelling Conclusion ∗ The views expressed in the paper are the author’s own and do not necessarily reflect those of Dresdner Kleinwort Wasserstein or any associated legal entity. This note was prepared for: “useR! 2004 – The R User Conference”, May 20-22, 2004, Vienna, Austria. Adress correspondence to: Bernhard Pfaff, Dresdner Kleinwort Wasserstein, Corporates & Markets, Capital Markets, Global Research, Theodor–Heuss–Allee 44–46, 60301 Frankfurt am Main, Germany, email: bernhard.pfaff@drkw.com
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Embedding R in Windows applications
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