ggmcmc

Calculate the autocorrelation of a single chain, for a specified amount of lags

Simulated data for a binary logistic regression and its MCMC samples

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

Subset a ggs object to get only the parameters with a given regular expression.

Wrapper function that creates a single pdf file with all plots that ggmcmc can produce.

Import MCMC samples into a ggs object than can be used by all ggs_* graphical functions.

Dotplot of Potential Scale Reduction Factor (Rhat)

Plot an autocorrelation matrix

Caterpillar plot with thick and thin CI

Auxiliary function that extracts information from a single chain.

Density plots comparing the distribution of the whole chain with only its last part.

Plot the Cross-correlation between-chains

Density plots of the chains

Formal diagnostics of convergence and sampling quality

Dotplot of the effective number of independent draws

Dotplot of the Geweke diagnostic, the standard Z-score

Gelman-Rubin-Brooks plot (Rhat shrinkage)

Histograms of the paramters.

Create a plot matrix of posterior simulations

Plot for model fit of binary response variables: percent correctly predicted

Posterior predictive plot comparing the outcome mean vs the distribution of the predicted posterior means.

Posterior predictive plot comparing the outcome standard deviation vs the distribution of the predicted posterior standard deviations.

Receiver-Operator Characteristic (ROC) plot for models with binary outcomes

Running means of the chains

Separation plot for models with binary response variables

Traceplot of the chains

Generate a factor with unequal number of repetitions.

Simulated data for a continuous linear regression and its MCMC samples

Generate a data frame suitable for matching parameter names with their labels

Simulations of the parameters of a hierarchical model

Calculate the ROC curve for a set of observed outcomes and predicted probabilities

Simulations of the parameters of a simple linear regression with fake data.

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.

Spectral Density Estimate at Zero Frequency.

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.