JointAI v1.0.0

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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'.

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JointAI: Joint Analysis and Imputation of Incomplete Data

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The package JointAI provides functionality to perform joint analysis and imputation of a range of model types in the Bayesian framework. Implemented are (generalized) linear regression models and extensions thereof, models for (un-/ordered) categorical data, as well as multi-level (mixed) versions of these model types.

Moreover, survival models and joint models for longitudinal and survival data are available. It is also possible to fit multiple models of mixed types simultaneously. Missing values in (if present) will be imputed automatically.

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.

Besides the main modelling functions, JointAI also provides a number of functions to summarize and visualize results and incomplete data.

Installation

JointAI can be installed from CRAN:

install.packages('JointAI')

Alternatively, you can install JointAI from GitHub:

# install.packages("remotes")
remotes::install_github("NErler/JointAI")

Main functions

JointAI provides the following main functions:

lm_imp()                 # linear regression
glm_imp()                # generalized linear regression
clm_imp()                # cumulative logit model
mlogit_imp()             # multinomial logit model
lognorm_imp()            # log-normal regression
betareg_imp()            # beta regression
lme_imp() / lmer_imp()   # linear mixed model
glme_imp() / glmer_imp() # generalized linear mixed model
clmm_imp()               # cumulative logit mixed model
mlogitmm_imp()           # multinomial logit model
lognormmm_imp()          # log-normal regression
betamm_imp()             # beta regression
survreg_imp()            # parametric (Weibull) survival model
coxph_imp()              # proportional hazards survival model
JM_imp()                 # joint model for longitudinal and survival data

The functions use specification similar to that of well known standard functions like lm() and glm() from base R, nlme::lme() (from the package nlme) , lme4::lmer() or lme4::glmer() (from the package lme4) and survival::survreg() and survival::coxph() (from the package survival).

Functions summary(), coef(), traceplot() and densplot() provide a summary of the posterior distribution and its visualization.

GR_crit() and MC_error() implement the Gelman-Rubin diagnostic for convergence and the Monte Carlo error of the MCMC sample, respectively.

JointAI also provides functions for exploration of the distribution of the data and missing values, export of imputed values and prediction.

Minimal Example

Visualize the observed data and missing data pattern

library(JointAI)

plot_all(NHANES[c(1, 5:6, 8:12)], fill = '#D10E3B', border = '#460E1B', ncol = 4, breaks = 30)

md_pattern(NHANES, color = c('#460E1B', '#D10E3B'))

Fit a linear regression model with incomplete covariates

lm1 <- lm_imp(SBP ~ gender + age + WC + alc + educ + bili,
              data = NHANES, n.iter = 500, progress.bar = 'none', seed = 2020)

Visualize the MCMC sample

traceplot(lm1, col = c('#d4af37', '#460E1B', '#D10E3B'), ncol = 4)

densplot(lm1, col = c('#d4af37', '#460E1B', '#D10E3B'), ncol = 4, lwd = 2)

Summarize the Result

summary(lm1)
#> 
#> Bayesian linear model fitted with JointAI
#> 
#> Call:
#> lm_imp(formula = SBP ~ gender + age + WC + alc + educ + bili, 
#>     data = NHANES, n.iter = 500, seed = 2020, progress.bar = "none")
#> 
#> 
#> Posterior summary:
#>                Mean     SD    2.5%   97.5% tail-prob. GR-crit MCE/SD
#> (Intercept)  87.984 9.0412  70.110 107.092    0.00000    1.00 0.0258
#> genderfemale -3.501 2.2488  -8.039   1.059    0.10400    1.00 0.0258
#> age           0.333 0.0713   0.199   0.471    0.00000    1.01 0.0275
#> WC            0.226 0.0757   0.072   0.373    0.00267    1.00 0.0258
#> alc>=1        6.509 2.3290   1.899  10.859    0.01067    1.00 0.0270
#> educhigh     -2.780 2.1237  -6.886   1.248    0.19733    1.00 0.0258
#> bili         -5.173 4.8315 -14.599   4.109    0.28800    1.01 0.0303
#> 
#> Posterior summary of residual std. deviation:
#>           Mean    SD 2.5% 97.5% GR-crit MCE/SD
#> sigma_SBP 13.6 0.739 12.3  15.1    1.01 0.0289
#> 
#> 
#> MCMC settings:
#> Iterations = 101:600
#> Sample size per chain = 500 
#> Thinning interval = 1 
#> Number of chains = 3 
#> 
#> Number of observations: 186
coef(lm1)
#> $SBP
#>  (Intercept) genderfemale          age           WC       alc>=1     educhigh 
#>   87.9839157   -3.5010429    0.3329532    0.2262894    6.5093606   -2.7800225 
#>         bili    sigma_SBP 
#>   -5.1730414   13.5670206

confint(lm1)
#> $SBP
#>                      2.5%       97.5%
#> (Intercept)   70.11037933 107.0920122
#> genderfemale  -8.03905105   1.0594821
#> age            0.19919441   0.4705334
#> WC             0.07201019   0.3734877
#> alc>=1         1.89897665  10.8594963
#> educhigh      -6.88561508   1.2481772
#> bili         -14.59898407   4.1089909
#> sigma_SBP     12.25273343  15.1162472

Functions in JointAI

Name Description
PBC PBC data
add_samples Continue sampling from an object of class JointAI
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
Surv Create a Survival Object
NHANES National Health and Nutrition Examination Survey (NHANES) Data
bs Generate a Basis Matrix for Natural Cubic Splines
all_vars Version of all.vars() that can handle lists of formulas
densplot Plot the posterior density from object of class JointAI
get_MIdat Extract multiple imputed datasets from an object of class JointAI
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
model_imp Joint Analysis and Imputation of incomplete data
md_pattern Missing data pattern
plot_imp_distr Plot the distribution of observed and imputed values
predDF Create a new data frame for prediction
remove_lhs Remove the left hand side of a (list of) formula(s)
predict.JointAI Predict values from an object of class JointAI
plot.JointAI Plot an object object inheriting from class 'JointAI'
plot_all Visualize the distribution of all variables in the dataset
summary.JointAI Summarize the results from an object of class JointAI
longDF Longitudinal example dataset
clean_survname Convert a survival outcome to a model name
set_refcat Specify reference categories for all categorical covariates in the model
default_hyperpars Get the default values for hyper-parameters
residuals.JointAI Extract residuals 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
traceplot Create traceplots for a MCMC sample
wideDF Cross-sectional example dataset
parameters Parameter names of an JointAI object
ns Generate a Basis Matrix for Natural Cubic Splines
sharedParams Parameters used by several functions in JointAI
simLong Simulated Longitudinal Data in Long and Wide Format
No Results!

Vignettes of JointAI

Name
AfterFitting.Rmd
MCMCsettings.Rmd
ModelSpecification.Rmd
SelectingParameters.Rmd
No Results!

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Details

URL https://nerler.github.io/JointAI/
License GPL (>= 2)
BugReports https://github.com/nerler/JointAI/issues/
LazyData TRUE
RoxygenNote 7.1.1
SystemRequirements JAGS (http://mcmc-jags.sourceforge.net/)
VignetteBuilder knitr
Encoding UTF-8
RdMacros mathjaxr
Language en-GB
NeedsCompilation no
Packaged 2020-08-30 19:38:55 UTC; erler
Repository CRAN
Date/Publication 2020-08-31 06:40:09 UTC

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