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| bayz() function | Model-term features and options | Output use |
|---|---|---|
| Model formula | Interaction specifications | General summary |
| Residual variance (Ve) | Nested regressions | Extract estimates (estim, coef, fixef, ranef) |
| data frame | Extracts variances, hyper-pars, and functions (vhest) | |
| linking kernels, features (linkid) | Convergence diagnostics (conv) | |
| [MCMC chain] | Variance specification (V) | Trace and density plots (plot) |
| workdir in/output | Reduced rank (dim, dimp) | Warnings and errors |
| initial values (init) | trace and save options | |
| verbose settings | linkid on rr() term |
The complete interface for the bayz() main modelling function is:
bayz(modelformula, Ve, data, linkid, chain, workdir, init, verbose)
where - modelformula is an extended R-style model formala, see xxx - Ve sets the model for the residual variance, see xxx - data is a data frame with variables (responses, explanatory variables) to build the model, see xxx - linkid is an id in the data used to link to kernels and sets of features. - chain is a vector c(length, burn-in, skip) with total chain length to run, burn-in, and skip-interval for saving samples and computing convergence diagnostics. - workdir is a directory to save the MCMC samples and intermediate files, and to run the MCMC chain in. If not specified, the current working directory is used. - init is a list of initial values for the MCMC chain, see xxx - verbose is an integer with settings for the amount of information printed during the MCMC run, see xxx
Use of summary() on the bayz output object produces a summary of parameter estimates from the fitted model including convergence diagnostics and Highest Posterior Density (HPD) regions.
The summary() method only lists a limited number of the so-called “traced” parameters - these are model-parameters for which all MCMC samples are saved in the output, allowing to compute convergence, HPD regions, and to plot traces and densities (using plot()). The traced parameters by default include: all scalar variance parameters, estimated variance-covariances up to dimension 4x4, the model mean, scalar regression coefficients, coefficient estimates from fixed effects with up to 4 levels, and nested regressions with up to 4 levels.
Apart from using these functions, it is also quite straightforward to extract estimates directly from the bayz output object. All estimates are stored in the output in a list called