Provides joint analysis and imputation of (generalized) linear regression models, (generalized) linear mixed models and parametric survival models with incomplete (covariate) data in the Bayesian framework. The package performs some preprocessing of the data and creates a 'JAGS' model, which will then automatically be passed to 'JAGS' < http://mcmc-jags.sourceforge.net> with the help of the package 'rjags'. It also provides summary and plotting functions for the output.
The package JointAI provides joint analysis and imputation of linear regression models, generalized linear regression models or linear mixed models with incomplete (covariate) data in the Bayesian framework.
JointAI also provides summary and plotting functions for the output.
You can install JointAI from GitHub with:
Currently, there are three main functions that perform linear, generalized linear or linear mixed regression:
glm_imp() use specification similar to their complete
lme_imp() uses similar
lme() from the package
densityplot() provide a summary
of the posterior distribution and its visualization.
mc_error() provide the Gelman-Rubin diagnostic for
convergence and the Monte Carlo error of the MCMC sample, respectively.
monitor_paramsis now checked to avoid problems when only part of the main parameters is selected
md.pattern()now uses ggplot, which scales better than the previous version
lme_imp()now ask about overwriting a model file
analysis_main = Tstays selected when other parameters are followed as well
includeadded to select if original data are included and id variable
.idis added to the dataset
subsetargument uses same logit as
lme_imp()now take argument
truncin order to truncate the distribution of incomplete variables
summary()now omits auxiliary variables from the output
imp_par_listis now returned from JointAI models
cat_varsis no longer returned from
lme_imp(), because it is contained in
traceplot()optional with ggplot
densplot()option to combine chains before plotting
list_impmodelsto print information on the imputation models and hyperparameters
parameters()added to display the parameters to be/that were monitored
set_refcat()added to guide specification of reference categories
md_pattern(): does not generate duplicate plot any more
get_MIdat(): imputed values are now filled in in the correct order
get_MIdat(): variables imputed with
lognormare now included when extracting an imputed dataset
get_MIdat(): imputed values of transformed variables are now included in imputed datasets
md.pattern(): adaptation to new version of
md.pattern()from the mice package
betaimputation methods implemented