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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. 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' <http://mcmc-jags.sourceforge.net/> with the help of the package 'rjags'.

Functions (39)

GR_crit

Gelman-Rubin criterion for convergence

JointAI

JointAI: Joint Analysis and Imputation of Incomplete Data

JointAIObject

Fitted object of class 'JointAI'

MC_error

Calculate and plot the Monte Carlo error

NHANES

National Health and Nutrition Examination Survey (NHANES) Data

PBC

PBC data

Surv

Create a Survival Object

add_samples

Continue sampling from an object of class JointAI

all_vars

Version of all.vars() that can handle lists of formulas

Generate a Basis Matrix for Natural Cubic Splines

clean_survname

Convert a survival outcome to a model name

default_hyperpars

Get the default values for hyper-parameters

densplot

Plot the posterior density from object of class JointAI

extract_state

Return the current state of a 'JointAI' model

get_MIdat

Extract multiple imputed datasets from an object of class JointAI

get_Mlist

Re-create the full Mlist from a "JointAI" object

get_family

Identify the family from the covariate model type

get_missinfo

Obtain a summary of the missing values involved in an object of class JointAI

get_modeltype

Identify the general model type from the covariate model type

list_models

List model details

longDF

Longitudinal example dataset

md_pattern

Missing data pattern

model_imp

Joint Analysis and Imputation of incomplete data

Generate a Basis Matrix for Natural Cubic Splines

parameters

Parameter names of an JointAI object

plot.JointAI

Plot an object object inheriting from class 'JointAI'

plot_all

Visualize the distribution of all variables in the dataset

plot_imp_distr

Plot the distribution of observed and imputed values

predDF

Create a new data frame for prediction

predict.JointAI

Predict values from an object of class JointAI

rd_vcov

Extract the random effects variance covariance matrix Returns the posterior mean of the variance-covariance matrix/matrices of the random effects in a fitted JointAI object.

remove_lhs

Remove the left hand side of a (list of) formula(s)

residuals.JointAI

Extract residuals from an object of class JointAI

set_refcat

Specify reference categories for all categorical covariates in the model

sharedParams

Parameters used by several functions in JointAI

simLong

Simulated Longitudinal Data in Long and Wide Format

summary.JointAI

Summarize the results from an object of class JointAI