Calculate the autocorrelation of a single chain, for a specified amount of lags
Dotplot of Potential Scale Reduction Factor (Rhat)
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 numeric values
Dotplot of the effective number of independent draws
Dotplot of the Geweke diagnostic, the standard Z-score
Formal diagnostics of convergence and sampling quality
Density plots of the chains
Simulated data for a binary logistic regression and its MCMC samples
Plot an autocorrelation matrix
Subset a ggs object to get only the parameters with a given regular expression.
Spectral Density Estimate at Zero Frequency.
Density plots comparing the distribution of the whole chain with only its last part.
Wrapper function that creates a single pdf file with all plots that ggmcmc can produce.
Caterpillar plot with thick and thin CI
Auxiliary function that extracts information from a single chain.
Separation plot for models with binary response variables
Import MCMC samples into a ggs object than can be used by all ggs_* graphical functions.
Values for the observed outcome of a simple linear regression with fake data.
Generate a factor with unequal number of repetitions.
Plot the Cross-correlation between-chains
Create a plot matrix of posterior simulations
Simulated data for a continuous linear regression and its MCMC samples
Simulations of the parameters of a simple linear regression with fake data.
Simulations of the posterior predictive distribution of a simple linear regression with fake data.
Posterior predictive plot comparing the outcome standard deviation vs the distribution of the predicted posterior standard deviations.
Posterior predictive plot comparing the outcome mean vs the distribution of the predicted posterior means.
Traceplot of the chains
Values for the observed outcome of a binary logistic regression with fake data.
Plot for model fit of binary response variables: percent correctly predicted
Simulations of the parameters of a simple linear regression with fake data.
Calculate the ROC curve for a set of observed outcomes and predicted probabilities
Histograms of the paramters.
Receiver-Operator Characteristic (ROC) plot for models with binary outcomes
Gelman-Rubin-Brooks plot (Rhat shrinkage)
Running means of the chains
Simulations of the parameters of a hierarchical model
Generate a data frame suitable for matching parameter names with their labels