Tools for Analyzing MCMC Simulations from Bayesian Inference
Calculate the autocorrelation of a single chain, for a specified amoun...
Calculate binwidths by parameter, based on the total number of bins.
Calculate Credible Intervals (wide and narrow).
Auxiliary function that sorts Parameter names taking into account nume...
Subset a ggs object to get only the parameters with a given regular ex...
Wrapper function that creates a single pdf file with all plots that gg...
Import MCMC samples into a ggs object than can be used by all ggs_* gr...
Plot an autocorrelation matrix
Caterpillar plot with thick and thin CI
Auxiliary function that extracts information from a single chain.
Density plots comparing the distribution of the whole chain with only ...
Plot the Cross-correlation between-chains
Density plots of the chains
Formal diagnostics of convergence and sampling quality
Dotplot of the effective number of independent draws
Dotplot of the Geweke diagnostic, the standard Z-score
Gelman-Rubin-Brooks plot (Rhat shrinkage)
Histograms of the paramters.
Create a plot matrix of posterior simulations
Plot for model fit of binary response variables: percent correctly pre...
Posterior predictive plot comparing the outcome mean vs the distributi...
Posterior predictive plot comparing the outcome standard deviation vs ...
Dotplot of Potential Scale Reduction Factor (Rhat)
Receiver-Operator Characteristic (ROC) plot for models with binary out...
Running means of the chains
Separation plot for models with binary response variables
Traceplot of the chains
Generate a factor with unequal number of repetitions.
Generate a data frame suitable for matching parameter names with their...
Calculate the ROC curve for a set of observed outcomes and predicted p...
Spectral Density Estimate at Zero Frequency.
Tools for assessing and diagnosing convergence of Markov Chain Monte Carlo simulations, as well as for graphically display results from full MCMC analysis. The package also facilitates the graphical interpretation of models by providing flexible functions to plot the results against observed variables, and functions to work with hierarchical/multilevel batches of parameters (Fernández-i-Marín, 2016 <doi:10.18637/jss.v070.i09>).
Useful links