Graphical tools for analyzing Markov Chain Monte Carlo simulations from Bayesian inference.

Documentation for package ‘ggmcmc’ version 0.5.1

Help Pages

ggmcmc-package	Wrapper function that creates a single pdf file with all plots that ggmcmc can produce.
ac	Calculate the autocorrelation of a single chain, for a specified amount of lags
calc.bin	Calculate binwidths by parameter, based on the total number of bins
get_family	Subset a ggs object to get only the parameters with a given regular expression.
ggmcmc	Wrapper function that creates a single pdf file with all plots that ggmcmc can produce.
ggs	Import MCMC samples into a ggs object than can be used by all ggs_* graphical functions.
ggs_autocorrelation	Plot an autocorrelation matrix
ggs_caterpillar	Caterpillar plot with thick and thin CI
ggs_chain	Auxiliary function that extracts information from a single chain.
ggs_compare_partial	Density plots comparing the distribution of the whole chain with only its last part.
ggs_crosscorrelation	Plot the Cross-correlation between-chains
ggs_density	Density plots of the chains
ggs_geweke	Dotplot of the Geweke diagnostic, the standard Z-score
ggs_histogram	Histograms of the paramters.
ggs_ppmean	Posterior predictive plot comparing the outcome vs the posterior means.
ggs_ppsd	Posterior predictive plot comparing the outcome vs the posterior standard deviations.
ggs_Rhat	Dotplot of Potential Scale Reduction Factor (Rhat)
ggs_rocplot	Receiver-Operator Characteristic (ROC) plot for models with binary outcomes
ggs_running	Running means of the chains
ggs_separation	Separation plot for models with binary response variables
ggs_traceplot	Traceplot of the chains
gl.unq	Generate a factor with unequal number of repetitions
roc.calc	Calculate the ROC curve for a set of observed outcomes and predicted probabilities
S	Simulations of the parameters of a simple linear regression with fake data.
sde0f	Spectral Density Estimate at Zero Frequency