Tools for Analyzing MCMC Simulations from Bayesian Inference

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Documentation for package ‘ggmcmc’ version 1.5.1.1

• DESCRIPTION file.
• User guides, package vignettes and other documentation.
• Package NEWS.

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
binary	Simulated data for a binary logistic regression and its MCMC samples
calc_bin	Calculate binwidths by parameter, based on the total number of bins.
ci	Calculate Credible Intervals (wide and narrow).
custom.sort	Auxiliary function that sorts Parameter names taking into account numeric values
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_diagnostics	Formal diagnostics of convergence and sampling quality
ggs_effective	Dotplot of the effective number of independent draws
ggs_geweke	Dotplot of the Geweke diagnostic, the standard Z-score
ggs_grb	Gelman-Rubin-Brooks plot (Rhat shrinkage)
ggs_histogram	Histograms of the paramters.
ggs_pairs	Create a plot matrix of posterior simulations
ggs_pcp	Plot for model fit of binary response variables: percent correctly predicted
ggs_ppmean	Posterior predictive plot comparing the outcome mean vs the distribution of the predicted posterior means.
ggs_ppsd	Posterior predictive plot comparing the outcome standard deviation vs the distribution of the predicted 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.
linear	Simulated data for a continuous linear regression and its MCMC samples
plab	Generate a data frame suitable for matching parameter names with their labels
radon	Simulations of the parameters of a hierarchical model
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.
s.binary	Simulations of the parameters of a simple linear regression with fake data.
s.y.rep	Simulations of the posterior predictive distribution of a simple linear regression with fake data.
sde0f	Spectral Density Estimate at Zero Frequency.
y	Values for the observed outcome of a simple linear regression with fake data.
y.binary	Values for the observed outcome of a binary logistic regression with fake data.