Package: JointAI 1.1.0

JointAI: Joint Analysis and Imputation of Incomplete Data

Joint analysis and imputation of incomplete data in the Bayesian framework, using (generalized) linear (mixed) models and extensions there of, survival models, or joint models for longitudinal and survival data, as described in Erler, Rizopoulos and Lesaffre (2021) <doi:10.18637/jss.v100.i20>. Incomplete covariates, if present, are automatically imputed. The package performs some preprocessing of the data and creates a 'JAGS' model, which will then automatically be passed to 'JAGS' <https://mcmc-jags.sourceforge.io/> with the help of the package 'rjags'.

Authors:Nicole S. Erler [aut, cre]

JointAI_1.1.0.tar.gz
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manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
JointAI/json (API)

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

Bug tracker:https://github.com/nerler/jointai/issues

Pkgdown/docs site:https://nerler.github.io

Uses libs:
  • jags– Just Another Gibbs Sampler for Bayesian MCMC
  • c++– GNU Standard C++ Library v3
Datasets:
  • longDF - Longitudinal example dataset
  • NHANES - National Health and Nutrition Examination Survey (NHANES) Data
  • PBC - PBC data
  • simLong - Simulated Longitudinal Data in Long and Wide Format
  • simWide - Simulated Longitudinal Data in Long and Wide Format
  • wideDF - Cross-sectional example dataset

On CRAN:

Conda:

bayesiangeneralized-linear-modelsglmglmmimputationimputationsjagsjoint-analysislinear-mixed-modelslinear-regression-modelsmcmc-samplemcmc-samplingmissing-datamissing-valuessurvivalcpp

8.18 score 29 stars 1 packages 62 scripts 673 downloads 2 mentions 43 exports 42 dependencies

Last updated from:ab46e5a5cd. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64OK199
source / vignettesOK250
linux-release-x86_64OK250
macos-release-arm64OK189
macos-oldrel-arm64OK276
windows-develOK160
windows-releaseOK203
windows-oldrelOK189
wasm-releaseOK134

Exports:add_samplesauto_corrauto_corr_plotbetamm_impbetareg_impbsclean_survnameclm_impclmm_impcoxph_impcross_corrcross_corr_plotdefault_hyperparsdensplotextract_stateget_MIdatget_missinfoglm_impglme_impglmer_impGR_critJM_implist_modelslm_implme_implmer_implognorm_implognormmm_impMC_errormd_patternmlogit_impmlogitmm_impnsparametersplot_allplot_imp_distrpredDFrd_vcovset_refcatsum_durationSurvsurvreg_imptraceplot

Dependencies:briocallrclicodacodetoolscrayondescdiffobjdigestellipseevaluatefftwtoolsfsfutureglobalsgluejsonlitelatticelifecyclelistenvmagrittrMASSmathjaxrMatrixmcmcseotelparallellypkgbuildpkgloadpraiseprocessxpsR6RcppRcppArmadillorjagsrlangrprojrootsurvivaltestthatwaldowithr

After Fitting
Visualizing the posterior sample | Trace plot | Density plot | Model Summary | Tail probability | Evaluation criteria | Gelman-Rubin criterion for convergence | Monte Carlo Error | Subset of output | Subset of parameters | Examples | Subset of MCMC samples | Predicted values | Prediction to visualize non-linear effects | Export of imputed values
Rendered fromAfterFitting.Rmdusingknitr::rmarkdown

Last update: 2026-02-22
Started: 2018-12-03

Model Specification
Analysis model type | Model family and link functions | Model formula | Interactions | Interaction with multiple variables | Higher level interactions | Non-linear functional forms | What happens inside JointAI? | Functions with restricted support | Functions that are not available in R | Nested functions | Multi-level structure & longitudinal covariates | Random effects | Longitudinal covariates | Why do we need models for completely observed covariates? | Covariate model types | Specification of covariate model types | Order of the sequence of imputation models | Auxiliary variables | Functions of variables as auxiliary variables | Categorical covariates: coding and reference categories | Coding | Reference categories | Setting reference categories for all variables | Setting reference categories for individual variables | Hyper-parameters | Scaling | Shrinkage
Rendered fromModelSpecification.Rmdusingknitr::rmarkdown

Last update: 2026-02-22
Started: 2018-08-10

Parameter Selection
Monitoring parameters | Parameters of the analysis model | Imputed values & parameters of the imputation models | Side note: Getting information about of the imputation models | Side note: How to extract imputed datasets | Random effects | Other parameters | Subsets of Parameters for Plots, Summaries, ... | Select a subset of the variables to display | Random subset of subject-specific values
Rendered fromSelectingParameters.Rmdusingknitr::rmarkdown

Last update: 2026-02-22
Started: 2018-08-02

MCMC Settings
MCMC related parameters in JointAI | Chains and Iterations | Number of chains | Adaptive phase | Sampling iterations | Side note: How to evaluate convergence? | Side note: How to check precision? | Thinning | Examples | Default settings | Insufficient adaptation phase | Parameters to follow | What are nodes? | Specifying which nodes should be monitored | Initial values | User-specified initial values | Initial values in a list of lists | Initial values as a function | For which nodes can initial values be specified? | Parallel sampling | Other arguments
Rendered fromMCMCsettings.Rmdusingknitr::rmarkdown

Last update: 2026-01-05
Started: 2018-12-03

Readme and manuals

Help Manual

Help pageTopics
Continue sampling from an object of class JointAIadd_samples
Autocorrelation of MCMC samplesauto_corr auto_corr_plot
Convert a survival outcome to a model nameclean_survname
Cross-correlation of MCMC samplescross_corr cross_corr_plot
Get the default values for hyper-parametersdefault_hyperpars
Plot the posterior density from object of class JointAIdensplot densplot.JointAI
Return the current state of a 'JointAI' modelextract_state
Extract multiple imputed datasets from an object of class JointAIget_MIdat
Obtain a summary of the missing values involved in an object of class JointAIget_missinfo
Gelman-Rubin criterion for convergenceGR_crit
Convert a survival outcome to a model nameinternal_clean_survname
JointAI: Joint Analysis and Imputation of Incomplete DataJointAI-package JointAI
Fitted object of class 'JointAI'JointAIObject
List model detailslist_models
Longitudinal example datasetlongDF
Calculate and plot the Monte Carlo errorMC_error plot.MCElist
Missing data patternmd_pattern
Joint Analysis and Imputation of incomplete databetamm_imp betareg_imp clmm_imp clm_imp coxph_imp glmer_imp glme_imp glm_imp JM_imp lmer_imp lme_imp lm_imp lognormmm_imp lognorm_imp mlogitmm_imp mlogit_imp model_imp survreg_imp
National Health and Nutrition Examination Survey (NHANES) DataNHANES
Parameter names of an JointAI objectparameters
PBC dataPBC
Visualize the distribution of all variables in the datasetplot_all
Plot the distribution of observed and imputed valuesplot_imp_distr
Plot an object object inheriting from class 'JointAI'plot.JointAI
Predict values from an object of class JointAIpredict.JointAI
Summarize the results from an object of class JointAIcoef.JointAI confint.JointAI print.Dmat print.JointAI print.summary.JointAI summary.JointAI
Extract the random effects variance covariance matrixrd_vcov
Extract residuals from an object of class JointAIresiduals.JointAI
Specify reference categories for all categorical covariates in the modelset_refcat
Parameters used by several functions in JointAIsharedParams
Simulated Longitudinal Data in Long and Wide FormatsimLong simWide
Calculate the sum of the computational duration of a JointAI objectsum_duration
Create traceplots for a MCMC sampletraceplot traceplot.JointAI
Cross-sectional example datasetwideDF