Joint Analysis and Imputation of Incomplete Data
Continue sampling from an object of class JointAI
Extract names of variables from several objects
Ensure object is a (list of) formula(s)
Replace a full with a block-diagonal variance covariance matrix
Check if a grouping variable varies within another grouping variable
Check for missing values in grouping variables
First validation for rd_vcov
Check / create the random effects variance-covariance matrix specifica...
Check for unnecessary grouping levels
Check that all variables in formulas are in the data
Choose default analysis model based on outcome and data level
Replace ":" with "_" in a string
Convert a survival outcome to a model name
Combine fixed and random effects formulas
Combine a fixed and random effects formula
Compare the structure of two data.frames
Convert variables
Cross-correlation of MCMC samples
Determine grouping level of data
Identify the family from the covariate model type
Get grouping levels
Get grouping information
Get an element of a list, return a default value if it does not exist
Extract multiple imputed datasets from an object of class JointAI
Obtain a summary of the missing values involved in an object of class ...
Re-create the full Mlist from a "JointAI" object
Identify the general model type from the covariate model type
Extract the number of random effects
Identify the data matrix containing a given response variable
Write a linear predictor
Create the scaling in a data element of a linear predictor
Wrap a data element of a linear predictor in scaling syntax
Prepare list of arguments for model_imp()
Extract terms by grouping variables from a formula
Extract the random effects variance covariance matrix
Set all elements of a difftime object to the same, largest meaningfu...
Remove grouping part from (random effects) formula
Remove grouping part from (random effects) formulas
Remove the left hand side of a (list of) formula(s)
Replace NaN values with NA
Extract residuals from an object of class JointAI
Resolve family object
Write the coefficient part of a linear predictor
Write the data element of a linear predictor
Create data.frame from variable term and data
Add line breaks to a linear predictor string
Autocorrelation of MCMC samples
B-Spline Basis for Polynomial Splines
Check classes of all variables used in the model
Run all data related checks
Check for duplicate grouping levels
Check whether fixed or formula contains a random effects specification
Convert a factor to an integer representation
Get the default values for hyper-parameters
Plot the posterior density from object of class JointAI
Converts a difftime object to a data.frame
Check for empty factor levels
Create a duration object
Expand rd_vcov using variable names in case "full" is used
Extract fixed effects formula from lme4-type formula
Extract grouping variables from a (list of) formula(s)
Extract the left hand side of a formula
Extract variable names from the left-hand side of a formula
Extract random effects formula from lme4-type formula
Return the current state of a 'JointAI' model
Gelman-Rubin criterion for convergence
Get info on main effects in a rd slope structure for a level and sub-m...
Get info on the interactions with random slopes for a given level and ...
Convert a survival outcome to a model name
JointAI: Joint Analysis and Imputation of Incomplete Data
Fitted object of class 'JointAI'
Paste analysis type with family information
List model details
Calculate and plot the Monte Carlo error
Missing data pattern
Merge call arguments with default formals
Joint Analysis and Imputation of incomplete data
Normalize formula arguments in arglist
Generate a Basis Matrix for Natural Cubic Splines
Parameter names of an JointAI object
PBC data
Visualize the distribution of all variables in the dataset
Plot the distribution of observed and imputed values
Plot an object object inheriting from class 'JointAI'
Create a new data frame for prediction
Predict values from an object of class JointAI
Specify reference categories for all categorical covariates in the mod...
Parameters used by several functions in JointAI
Split a list of formulas into fixed and random effects parts.
Calculate the sum of the computational duration of a JointAI object
Summarize the results from an object of class JointAI
Create a Survival Object
Create traceplots for a MCMC sample
Convert two-value vectors to factors
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'.