**JointAI** is a software package
contributed to the
Comprehensive R Archive Network
(CRAN).
It provides functionality to fit

- generalized linear regressionmodels
- (ordinal) cumulative logit regression models
- generalized linear mixed models (for multi-level data)
- (ordinal) cumulative logit mixed models
- parametric (Weibull) survival models
- Cox proportional hazards survival models

on incomplete (covariate) data using the Bayesian framework.

As far as possible, the specification of the main functions is analogue to the
specification of their complete data versions from base **R** and the packages
**ordinal**,
**nlme** and
**survival**.

**JointAI** performs some preprocessing of the data and creates a
“JAGS” model, which will then automatically
be passed to
JAGS
with the help of the R package
**rjags**.

**JointAI** has its own
website,
it can be downloaded from
CRAN
or
GitHub, and it is
part of the
CRAN Task View: Missing Data.

### Special features

Some features include **parallel computation**, shrinkage of regression coefficiens
via **ridge penalties** and the option to use **user-specified hyperparameters**.

### Results

Results can not only be summarized and printed, but also visualized using traceplots or density plots.

### Prediction

It is also possible to obtain predicted values (and corresponding intervals)
from **JointAI** models.

### Evaluation of convergence and precision

Two criteria for evaluation of convergence and precision of the posterior estimate are available:

The **Gelman-Rubin criterion** (‘potential scale reduction factor’) for convergence^{1},
and the **Monte Carlo error**, specifically the ratio of the Monte Carlo error to the
parameter’s standard deviation.

### Extraction and visualization of imputed values

Imputed data can be extracted, as multipe imputed datasets for further
analyses in **R**,and directly exported to **SPSS**.
Once extracted, the distribution of the imputed values can be compared visually
to the distribution of the observed data.

### Visualization of incomplete data

To learn about the data at hand, and make better modelling choices, functions that calculate and plot the missing data pattern and the distribution of each of the variables in the dataset, are provided.

## Vignettes

The package comes with the following vignettes, which give a more detailed explanation
and demonstration of how to use **JointAI**:

### Minimal Example

A minimal example demonstrating the use of the functions `lm_imp()`

, `summary()`

,
`traceplot()`

and `densplot()`

.

### Visualizing Incomplete Data:

Demonstrations of the options in the functions `plot_all()`

(plotting histograms and barplots for all variables in the data) and
`md_pattern()`

(plotting or printing the missing data pattern).

### Model Specification:

Explanation and demonstration of all parameters that are required or optional to
specify the model structure in the main anlysis functions.
Among others, the additional functions `parameters()`

, `list_models()`

, `get_models()`

and
`set_refcat()`

are used.

### Parameter Selection:

Examples on how to select the parameters/variables/nodes to follow using the
argument `monitor_params`

and the parameters/variables/nodes displayed in the
functions `summary()`

, `traceplot()`

, `densplot()`

or when using `GR_crit()`

or
`MC_error()`

.

### MCMC Settings:

Examples demonstrating how to set the arguments controlling settings of the MCMC
sampling, i.e., `n.adapt`

, `n.iter`

, `n.chains`

, `thin`

, `inits`

.

### After Fitting:

Examples on the use of functions to be applied after the model has been fitted,
including `traceplot()`

, `densplot()`

, `summary()`

, `GR_crit()`

, `MC_error()`

,
`predict()`

, `predDF()`

and `get_MIdat()`

.

Gelman, A and Rubin, DB (1992) Inference from iterative simulation using multiple sequences, Statistical Science, 7, 457-511.

Brooks, SP. and Gelman, A. (1998) General methods for monitoring convergence of iterative simulations. Journal of Computational and Graphical Statistics, 7, 434-455. ↩︎