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ggmcmc

ggmcmc is a tool for assessing and diagnosing convergence of Markov Chain Monte Carlo simulations, as well as for graphically display results from full MCMC analysis. The package also facilitates the graphical interpretation of models by providing flexible functions to plot the results against observed variables.

To install or update, run:

install.packages("ggmcmc", dependencies=TRUE)

Find out an example at http://xavier-fim.net/packages/ggmcmc and track development at http://github.com/xfim/ggmcmc.

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Install

install.packages('ggmcmc')

Monthly Downloads

3,063

Version

1.1

License

GPL-2

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Repository

https://github.com/xfim/ggmcmc

Homepage

http://xavier-fim.net/packages/ggmcmc

Maintainer

Xavier i Marín

Last Published

June 28th, 2016

Functions in ggmcmc (1.1)

Calculate Credible Intervals (wide and narrow).

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

ggs_caterpillar

Caterpillar plot with thick and thin CI

ggs_autocorrelation

Plot an autocorrelation matrix

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

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

Auxiliary function that extracts information from a single chain.

Calculate binwidths by parameter, based on the total number of bins.

ggs_compare_partial

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

Auxiliary function that sorts Parameter names taking into account numeric values

Histograms of the paramters.

Dotplot of the Geweke diagnostic, the standard Z-score

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

Create a plot matrix of posterior simulations

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

Running means of the chains

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

Spectral Density Estimate at Zero Frequency.

Simulations of the parameters of a hierarchical model using the radon example in Gelman & Hill (2007).

Simulations of the posterior predictive distribution of a simple linear regression with fake data.

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

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

Generate a factor with unequal number of repetitions.

Density plots of the chains

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

Values for the observed outcome of a binary logistic regression with fake data.

ggs_crosscorrelation

Plot the Cross-correlation between-chains

Values for the observed outcome of a simple linear regression with fake data.

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

Traceplot of the chains

Dotplot of Potential Scale Reduction Factor (Rhat)

Separation plot for models with binary response variables