An R Package for Fitting Bayesian Nested Partially Latent Class Models
Maintainer: Zhenke Wu, [email protected]
Source Code: Please click here for source code on GitHub.
Issues: Please click here to report reproducible issues.
Vignette: Please click here to read the latest long-version vignette; a short version can be found here.
Package website: Please click here
for a website generated by pkgdown
, which contains html format of the
package manual (βRefenceβ).
References: If you are using baker for population and individual estimation from case-control data, please cite the following papers:
There are a number of scientific papers on global health and infectious diseases that have used the model and some the software (in its earlier versions). Some notable examples are listed below:
- 1. Installation
- 2. Vignettes
- 3. Graphical User Interface (GUI)
- 4. Analytic Goal
- 5. Comparison to Other Existing Solutions
- 6. Details
- 7. Platform
- 8. Connect
R
toJAGS
on Unix systems or OSX - 9. Submit Jobs to Computing Cluster via a shell script
- 10. Connect
R
toJAGS
on Windows - 11. Example Datasets
# install.packages("devtools",repos="https://cloud.r-project.org")
devtools::install_github("zhenkewu/baker")
Note:
- run
install.packages("pbkrtest")
forR(>=3.2.3)
if this package is reported as missing. - Windows User: use
devtools::install_github("zhenkewu/baker",INSTALL_opts=c("--no-multiarch"))
instead if you see an error messageERROR: loading failed for 'i386'
(Thanks Chrissy!).
devtools::install_github("zhenkewu/baker", build_vignettes=TRUE) # will take extra time to run a few examples.
browseVignettes("baker")
# install.packages("devtools",repos="http://watson.nci.nih.gov/cran_mirror/")
devtools::install_github("zhenkewu/baker")
shiny::runApp(system.file("shiny", package = "baker"))
For developers interested in low-level details, here is a pretty awesome visualization of the function dependencies within the package:
library(DependenciesGraphs) # if not installed, try this-- devtools::install_github("datastorm-open/DependenciesGraphs")
library(QualtricsTools) # devtools::install_github("emmamorgan-tufts/QualtricsTools")
dep <- funDependencies('package:baker','nplcm')
plot(dep)
You will get a dynamic figure. A snapshot is below:
- To study disease etiology from case-control data from multiple sources
that have measurement errors. If you are interested in estimating the
population etiology pie (fraction), and the probability of each cause
for individual case, try
baker
.
- Acknowledges various levels of measurement errors and combines multiple sources of data for optimal disease diagnosis.
- Main function:
nplcm()
that fits the model with or without covariates.
- Implements hierarchical Bayesian models to infer disease etiology for multivariate binary data. The package builds in functionalities for data cleaning, exploratory data analyses, model specification, model estimation, visualization and model diagnostics and comparisons, catalyzing vital effective communications between analysts and practicing clinicians.
baker
has implemented models for dependent measurements given disease status, regression analyses of etiology, multiple imperfect measurements, different priors for true positive rates among cases with differential measurement characteristics, and multiple-pathogen etiology.- Scientists in Pneumonia Etiology Research for Child Health (PERCH) study usually refer to the etiology distribution as βpopulation etiology pieβ and βindividual etiology pieβ for their compositional nature, hence the name of the package.
- The
baker
package is compatible with OSX, Linux and Windows systems, each requiring a slightly different setup as described below. If you need to speed up the installation and analysis, please contact the maintainer or chat by clicking thegitter
button at the top of this README file.
- Use Just Another Gibbs Sampler (JAGS)
- Install JAGS 4.3.2 (or 4.2.0 - currently it is slightly slower for 4.3.2, which was updated to be compatible with R 4.3.x); Download here
- Install
R
; Download from here - Fire up
R
, runR
commandinstall.packages("rjags")
- Run
R
commandlibrary(rjags)
in R console; If the installations are successful, youβll see some notes like this:
>library(rjags)
Loading required package: coda
Linked to JAGS 4.x.0
Loaded modules: basemod,bugs
- Run
R
commandlibrary(baker)
. If the packageks
cannot be loaded due to failure of loading packagergl
, first install X11 by going here, followed by
install.packages("http://download.r-forge.r-project.org/src/contrib/rgl_0.95.1504.tar.gz",repo=NULL,type="source")
Here we use JHPCE as an example. The complete installation guide offers extra information.
-
Download source code for JAGS 4.2.0; The workflow would be similar for later versions of
JAGS
. -
Suppose youβve downloaded it in
~/local/jags/4.2.0
. Follow the bash commands below:# change to the directory with the newly downloaded source files: cd ~/local/jags/4.2.0 # create a new folder named "usr" mkdir usr # decompress files: tar zxvf JAGS-4.2.0.tar.gz # change to the directory with newly decompressed files: cd ~/local/jags/4.2.0/JAGS-4.2.0 # specify new JAGS home: export JAGS_HOME=$HOME/local/jags/4.2.0/usr export PATH=$JAGS_HOME/bin:$PATH # link to BLAS and LAPACK: # Here I have used "/usr/lib64/atlas/" and "/usr/lib64/" on JHPCE that give me # access to libblas.so.3 and liblapack.so.3. Please modify to paths on your system. LDFLAGS="-L/usr/lib64/atlas/ -L/usr/lib64/" ./configure --prefix=$JAGS_HOME --libdir=$JAGS_HOME/lib64 # if you have 8 cores: make -j8 make install # prepare to install R package, rjags: export PKG_CONFIG_PATH=$HOME/local/jags/4.2.0/usr/lib64/pkgconfig module load R R> install.packages("rjags") # or if the above fails, try: R>install.packages("rjags", configure.args="--enable-rpath")
-
Also check out the INSTALLATION file for
rjags
package.
Again, I use JHPCE as an example.
#!/bin/bash
#$ -M [email protected]
#$ -N baker_regression_perch
#$ -o /users/zhwu/baker_regression/data_analysis/baker_regression_test.txt
#$ -e /users/zhwu/baker_regression/data_analysis/baker_regression_test.txt
export JAGS_HOME=$HOME/local/jags/4.2.0/usr
export PATH=$JAGS_HOME/bin:$PATH
export LD_LIBRARY_PATH=$JAGS_HOME/lib64
cd /users/zhwu/baker_regression/data_analysis
#$ -cwd
echo "**** Job starts ****"
date
echo "**** JHPCE info ****"
echo "User: ${USER}"
echo "Job id: ${JOB_ID}"
echo "Job name: ${JOB_NAME}"
echo "Hostname: ${HOSTNAME}"
Rscript real_regression_data_jhpce.R
echo "**** Job ends ****"
date
- JAGS 4.2.0 (also applicable to later versions)
- Install
R
; Download from here - Install JAGS 4.2.0; Add the path to JAGS 4.2.0 into the environmental variable (essential for R to find the jags program). See this for setting environmental variables;
- alternatives are
brew install -v jags
for OSX,sudo apt-get install jags
for Ubuntu/Debian
- Fire up
R
, runR
commandinstall.packages("rjags")
- Install
Rtools
(for building and installing R packages from source); Add the path toRtools
(e.g.,C:\Rtools\
) into your environmental variables so that R knows where to find it.