Diagnostic Plots
Several diagnostic plots and tables are created for the Bayesian network model to ensure convergence along with providing credibility intervals. By default these plots are not generated but can be by setting the option R diagnostic = TRUE .
Density and trace plots are used to help determine if the Markov chains are stable and the symmetry of the data. Trace plots should appear flat, this ensures that simulations are coming from a stable Markov chain. Because of the stochastic nature of the simulation, continuous variables should look like flat 'fuzzy caterpillars'. Density show the distribution of the simulated values and should appear relatively symmetrical around the mean.
Autocorrelation plots are a way to evaluate the randomness of the data, comparing simulated values at a specific iteration to previous iterations (lags). Autocorrelation plots show the correlation coefficients, a value between 1 and -1. values around 1 and -1 show correlation while values around 0 show no correlation.
Gelman Plots are another source for determining convergence. Gelman plots are created for each CNV state probability (theta). The black line should converge onto the horizontal line stationary at 1.
