Package: ggmcmc 1.5.1.1

ggmcmc: Tools for Analyzing MCMC Simulations from Bayesian Inference

Tools 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, and functions to work with hierarchical/multilevel batches of parameters (Fernández-i-Marín, 2016 <doi:10.18637/jss.v070.i09>).

Authors:Xavier Fernández i Marín [aut, cre]

ggmcmc_1.5.1.1.tar.gz
ggmcmc_1.5.1.1.zip(r-4.5)ggmcmc_1.5.1.1.zip(r-4.4)ggmcmc_1.5.1.1.zip(r-4.3)
ggmcmc_1.5.1.1.tgz(r-4.4-any)ggmcmc_1.5.1.1.tgz(r-4.3-any)
ggmcmc_1.5.1.1.tar.gz(r-4.5-noble)ggmcmc_1.5.1.1.tar.gz(r-4.4-noble)
ggmcmc_1.5.1.1.tgz(r-4.4-emscripten)ggmcmc_1.5.1.1.tgz(r-4.3-emscripten)
ggmcmc.pdf |ggmcmc.html✨
ggmcmc/json (API)
NEWS

# Install 'ggmcmc' in R:
install.packages('ggmcmc', repos = c('https://xfim.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Bug tracker:https://github.com/xfim/ggmcmc/issues

Datasets:
  • radon - Simulations of the parameters of a hierarchical model
  • s - Simulations of the parameters of a simple linear regression with fake data.
  • 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.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.
  • s.y.rep - Simulations of the posterior predictive distribution of a simple linear regression with fake data.
  • y - Values for the observed outcome of a simple linear regression with fake data.
  • 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.
  • y.binary - Values for the observed outcome of a binary logistic regression with fake data.

On CRAN:

bayesian-data-analysisggplot2graphicaljagsmcmcstan

11.90 score 111 stars 8 packages 1.4k scripts 2.4k downloads 7 mentions 28 exports 46 dependencies

Last updated 2 years agofrom:352a0f8139. Checks:OK: 1 WARNING: 6. Indexed: yes.

TargetResultDate
Doc / VignettesOKOct 25 2024
R-4.5-winWARNINGOct 25 2024
R-4.5-linuxWARNINGOct 25 2024
R-4.4-winWARNINGOct 25 2024
R-4.4-macWARNINGOct 25 2024
R-4.3-winWARNINGOct 25 2024
R-4.3-macWARNINGOct 25 2024

Exports:accalc_binciggmcmcggsggs_autocorrelationggs_caterpillarggs_compare_partialggs_crosscorrelationggs_densityggs_diagnosticsggs_effectiveggs_gewekeggs_grbggs_histogramggs_pairsggs_pcpggs_ppmeanggs_ppsdggs_Rhatggs_rocplotggs_runningggs_separationggs_traceplotgl_unqplabroc_calcsde0f

Dependencies:clicolorspacecpp11crayondplyrfansifarverforcatsgenericsGGallyggplot2ggstatsgluegtablehmsisobandlabelinglatticelifecyclemagrittrMASSMatrixmgcvmunsellnlmepatchworkpillarpkgconfigplyrprettyunitsprogresspurrrR6RColorBrewerRcpprlangscalesstringistringrtibbletidyrtidyselectutf8vctrsviridisLitewithr

ggmcmc: Analysis of MCMC Samples and Bayesian Inference

Rendered fromv70i09.Rnwusingknitr::knitron Oct 25 2024.

Last update: 2021-02-10
Started: 2016-05-11

Using the ggmcmc package

Xavier Fernández-i-Marín

Rendered fromusing_ggmcmc.Rmdusingknitr::rmarkdownon Oct 25 2024.

Last update: 2020-08-11
Started: 2016-03-14

Readme and manuals

Help Manual

Help pageTopics
Calculate the autocorrelation of a single chain, for a specified amount of lagsac
Simulated data for a binary logistic regression and its MCMC samplesbinary
Calculate binwidths by parameter, based on the total number of bins.calc_bin
Calculate Credible Intervals (wide and narrow).ci
Auxiliary function that sorts Parameter names taking into account numeric valuescustom.sort
Subset a ggs object to get only the parameters with a given regular expression.get_family
Wrapper function that creates a single pdf file with all plots that ggmcmc can produce.ggmcmc-package ggmcmc
Import MCMC samples into a ggs object than can be used by all ggs_* graphical functions.ggs
Plot an autocorrelation matrixggs_autocorrelation
Caterpillar plot with thick and thin CIggs_caterpillar
Auxiliary function that extracts information from a single chain.ggs_chain
Density plots comparing the distribution of the whole chain with only its last part.ggs_compare_partial
Plot the Cross-correlation between-chainsggs_crosscorrelation
Density plots of the chainsggs_density
Formal diagnostics of convergence and sampling qualityggs_diagnostics
Dotplot of the effective number of independent drawsggs_effective
Dotplot of the Geweke diagnostic, the standard Z-scoreggs_geweke
Gelman-Rubin-Brooks plot (Rhat shrinkage)ggs_grb
Histograms of the paramters.ggs_histogram
Create a plot matrix of posterior simulationsggs_pairs
Plot for model fit of binary response variables: percent correctly predictedggs_pcp
Posterior predictive plot comparing the outcome mean vs the distribution of the predicted posterior means.ggs_ppmean
Posterior predictive plot comparing the outcome standard deviation vs the distribution of the predicted posterior standard deviations.ggs_ppsd
Dotplot of Potential Scale Reduction Factor (Rhat)ggs_Rhat
Receiver-Operator Characteristic (ROC) plot for models with binary outcomesggs_rocplot
Running means of the chainsggs_running
Separation plot for models with binary response variablesggs_separation
Traceplot of the chainsggs_traceplot
Generate a factor with unequal number of repetitions.gl_unq
Simulated data for a continuous linear regression and its MCMC sampleslinear
Generate a data frame suitable for matching parameter names with their labelsplab
Simulations of the parameters of a hierarchical modelradon
Calculate the ROC curve for a set of observed outcomes and predicted probabilitiesroc_calc
Simulations of the parameters of a simple linear regression with fake data.s
Simulations of the parameters of a simple linear regression with fake data.s.binary
Simulations of the posterior predictive distribution of a simple linear regression with fake data.s.y.rep
Spectral Density Estimate at Zero Frequency.sde0f
Values for the observed outcome of a simple linear regression with fake data.y
Values for the observed outcome of a binary logistic regression with fake data.y.binary