• STATISTICS SEMINAR
  • Speaker: Garth Tarr, School of Mathematical and Physical Sciences, The University of Newcastle
  • Title: Data visualisation, interactive data analysis, statistical programming
  • Location: Room V102, Mathematics Building (Callaghan Campus) The University of Newcastle
  • Time and Date: 3:00 pm, Fri, 13th May 2016
  • Abstract:

    In recent years, the power of R has been unleashed through the Shiny package which enables users to interact with complex analyses without needing to know any R programming. A Shiny application is a web interface to an underlying R instance. It is remarkably easy to develop both simple and complex Shiny apps using R and importantly, it requires no special knowledge of HTML, CSS or JavaScript. This workshop outlines the basics of developing a Shiny app and showcases some more advanced examples. One of the advantages of moving to a web-based approach is that it enables richer interactivity in data visualisation. There is a large, and ever increasing, pool of R packages that allow researchers to go beyond static plots.

    This seminar will also introduce the htmlwidgets framework that joins the raw statistical power of R with beautiful visualisations powered by JavaScript. The networkD3 and edgebundleR packages will be highlighted as examples that enable interactive visualisations of networks. It can be a full time job keeping up with all the new features R has to offer statisticians – the aim of this workshop is to familiarise you with some of the latest and greatest tools available for data visualisation and interactive data analysis.

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  • STATISTICS SEMINAR
  • Speaker: Garth Tarr, School of Mathematical and Physical Sciences, The University of Newcastle
  • Title: Interactive and data adaptive model selection with mplot
  • Location: Room V103, Mathematics Building (Callaghan Campus) The University of Newcastle
  • Time and Date: 3:00 pm, Fri, 30th Oct 2015
  • Technical
  • Abstract:

    This talk introduces the approach to model selection based on the concept of model stability (Meinshausen and Bühlmann, 2010; Müller and Welsh 2010). We present the mplot R package which provides a collection of functions designed to help users visualise the stability of the variable selection process. A browser based graphical user interface is provided to facilitate interaction with the results.

    We have developed routines for modified versions of the simplified adaptive fence procedure (Jiang et al., 2009) and other graphical tools such as variable inclusion plots and model selection plots (Müller and Welsh, 2010; Murray et al., 2013). We also propose extensions to higher dimensional models using via bootstrapping lasso estimates and incorporate robustness to outliers via an initial screening process (Filzmoser et al., 2008).

    While the focus to date has been on linear and generalised linear models, we are currently working on expanding the set of methods available to encompass mixed models and robust regression models. We will give an overview of what has been achieved and discuss areas for future research.

    REFERENCES:

    Filzmoser P, Maronna RA and Werner M (2008). Outlier Identification in High Dimensions. Computational Statistics & Data Analysis 52(3), 1694–1711. DOI: 10.1016/j.csda.2007.05.018.
    Jiang J, Nguyen T & Rao JS (2009). A simplified adaptive fence procedure. Statistics & Probability Letters, 79(5), 625-629. DOI: 10.1016/j.spl.2008.10.014
    Meinshausen N, and Bühlmann P (2010). Stability Selection. Journal of the Royal Statistical Society: Series B (Statistical Methodology) 72(4), 417–73. DOI: 10.1111/j.1467-9868.2010.00740.x.
    Müller S & Welsh AH (2010). On Model Selection Curves. International Statistical Review, 78, 240-256. DOI: 10.1111/j.1751-5823.2010.00108.x
    Murray K, Heritier S and Müller S (2013). Graphical tools for model selection in generalized linear models. Statistics in Medicine, 32, 4438-4451. DOI: 10.1002/sim.5855
    Tarr G, Müller S and Welsh AH. mplot: Graphical model stability and model selection procedures. Preprint.

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