Download e-book for iPad: Bayesian Analysis Made Simple : An Excel GUI for WinBUGS by Phil Woodward

By Phil Woodward

ISBN-10: 1439839557

ISBN-13: 9781439839553

From basic NLMs to complicated GLMMs, this booklet describes tips on how to use the GUI for WinBUGS - BugsXLA - an Excel add-in written through the writer that permits a number Bayesian versions to be simply detailed. With case reports all through, the textual content indicates tips to repeatedly observe even the extra complicated facets of version specification, reminiscent of GLMMs, outlier strong types, random results Emax versions, auto-regressive blunders, and Read more...

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4 M odel Statements The linear predictor consists of factors and covariates. In the classical terminology, factors can be considered ‘Fixed’ or ‘Random’ effects, and the covariates can be considered ‘Independent’ or ‘Random Coefficients’. The ‘Variable Selection’ (VS) factors and covariates will be discussed later. Models are defined by using model-statement operators in a similar fashion to statistical packages such as SAS, S-PLUS, R, or Genstat. The following are legal operators for defining the model terms involving factors: + addition of another term − subtraction of a term : interaction between two terms / nesting of second term inside first, for example, A/B = A + A:B BugsXLA Overview and Reference Manual 33 * addition of second term plus interactions with first, for example, A*B = A + B + A:B @ only expand terms to order specified, for example, A*B*C@2 = A*B*C − A:B:C Brackets, () or [], can be used to affect order of operator action.

Once WinBUGS completes the specified analysis, BugsXLA reads the output files created and imports the results back into Excel. 8 BugsXLA’s Role The WinBUGS package contains many more features than have been illustrated in this section, and anyone wishing to become proficient in its use will need to become familiar with the manual that is built into the program (‘Help: User Manual’ from the menu bar, or Spiegelhalter et al. (2003)), as well as working through some of the examples (‘Help: Examples Vol I and II’).

If covariates have been obtained to hopefully explain some of the within unit variation, then it is recommended that random coefficient terms are used to model their effects. It is often possible to simplify the explicit correlation structure needed for the errors by including such random effects. If the error distribution is Poisson, then only the AR1{Unit [/Time]} model statement is permitted. This fits the Poisson transition model of Zeger and Qaqish (1988), which is analogous to a first-order autocorrelation structure.

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Bayesian Analysis Made Simple : An Excel GUI for WinBUGS by Phil Woodward


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