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Advanced Multivariate Modeling in R: A New Grid Computing Package

Explore multivariate models with a motivating example, enabling technology for GRID computing in R, and a demo showcasing its benefits for statistical modeling. Enhance your research with this innovative approach.

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Advanced Multivariate Modeling in R: A New Grid Computing Package

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  1. An Alternative Package for Estimating Multivariate Generalised Linear Mixed Models in RDamon Berridge, Robert Crouchley & Daniel Grose, Lancaster University, U.K.

  2. Outline • A motivating example • Multivariate models: some comparisons • Enabling technology • Demo • Conclusions

  3. A motivating example (BHPS) • Sample of males who were employed and earning a wage at some point over the period 1991-2003. • Gives a total of 5285 individuals with a sequence of responses that occurred somewhere in the 1991-2004 interval. • At the 1st sample point of the survey (1991) there were was 2316 individuals of whom 945 of these males had some form of training in the previous 12 months. • 106 had been promoted in the previous 12 months.

  4. What is the effect of training & promotion on wages? Suppose we want to disentangle the dependencies between: Promotion (P=1,0) in the last 12 months (latent variable P*) Training (T=1,0) in the last 12 months (latent variable T*) Current wages (W)

  5. A correlated random effects model

  6. Multivariate models: some comparisons

  7. Enabling technology for GRID computing All you need is: • An internet connection • The installation of our multiR or sabreR packages for R • A certificate to identify the client to the host - typically a GRID certificate

  8. Points to note • Users do not need to install or be familiar with grid-proxy tools or any other GRID-related software. • When statistical modelling, there is very little difference between using the Sabre library from within R on the desktop, and using the Sabre library from within R on the GRID.

  9. sabre R R R R R R R R R R R R R R R R R R R R R R R R R R R R R Demo • sabrer_grid_vs_local_demo.mov

  10. Conclusions • This approach makes all the GRID middleware invisible and thus removes the biggest barrier to take up. • This approachcan provide researchers with more sophisticated statistical modelling tools and help increase their understanding of complex processes and thus help them to undertake more effective research. • Researchers do not need to let their large-scale computational statistics problems be limited by the developments of the big statistics software houses like SAS and Stata.

  11. Sabre web page • For further information on sabreR, see the Sabre web site: • http://sabre.lancs.ac.uk/

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