Calculate the autocorrelation of a single chain, for a specified amount of lags
Auxiliary function that extracts information from a single chain.
Receiver-Operator Characteristic (ROC) plot for models with binary outcomes
Running means of the chains
Separation plot for models with binary response variables
Density plots comparing the distribution of the whole chain with only its last part.
Spectral Density Estimate at Zero Frequency.
Simulations of the posterior predictive distribution of a simple linear regression with fake data.
Calculate Credible Intervals (wide and narrow).
Auxiliary function that sorts Parameter names taking into account numeric values
Subset a ggs object to get only the parameters with a given regular expression.
Create a plot matrix of posterior simulations
Posterior predictive plot comparing the outcome mean vs the distribution of the predicted posterior means.
Wrapper function that creates a single pdf file with all plots that ggmcmc can produce.
Dotplot of the Geweke diagnostic, the standard Z-score
Simulations of the parameters of a simple linear regression with fake data.
Simulations of the parameters of a simple linear regression with fake data.
Histograms of the paramters.
Traceplot of the chains
Generate a factor with unequal number of repetitions.
Values for the observed outcome of a simple linear regression with fake data.
Values for the observed outcome of a binary logistic regression with fake data.
Import MCMC samples into a ggs object than can be used by all ggs_* graphical functions.
Dotplot of Potential Scale Reduction Factor (Rhat)
Plot the Cross-correlation between-chains
Density plots of the chains
Simulations of the parameters of a hierarchical model using the radon example in Gelman & Hill (2007).
Calculate the ROC curve for a set of observed outcomes and predicted probabilities
Plot an autocorrelation matrix
Calculate binwidths by parameter, based on the total number of bins.
Caterpillar plot with thick and thin CI
Posterior predictive plot comparing the outcome standard deviation vs the distribution of the predicted posterior standard deviations.