# Tools for Analyzing MCMC Simulations from Bayesian Inference

## Documentation for package ‘ggmcmc’ version 1.5.1.2

- DESCRIPTION file
- User guides, package vignettes and other documentation
- Package NEWS
- Package README

## 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.