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## **baker**: Bayesian Analysis Kit for Etiology Research

> An R Package for Fitting Bayesian [Nested Partially Latent Class
> Models](https://doi.org/10.1093/biostatistics/kxw037)

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**Maintainer**: Zhenke Wu, <zhenkewu@umich.edu>

**Source Code**: Please click [here](https://github.com/zhenkewu/baker)
for source code on GitHub.

**Issues**: Please click
[here](https://github.com/zhenkewu/baker/issues) to report reproducible
issues.

**Vignette**: Please click
[here](https://zhenkewu.com/assets/html/baker_vignette.html) to read the
latest long-version vignette; a short version can be found
[here](https://zhenkewu.com/baker/articles/baker_demo.html).

**Package website:** Please click [here](https://zhenkewu.com/baker/)
for a website generated by `pkgdown`, which contains html format of the
package manual (“Reference”).

**References**: If you are using **baker** for population and individual
estimation from case-control data, please cite the following papers:

|  | Citation |
|----|----|
| partially Latent Class Models (pLCM) | Wu, Z., Deloria-Knoll, M., Hammitt, L. L., Zeger, S. L. and the Pneumonia Etiology Research for Child Health Core Team (2016), Partially latent class models for case–control studies of childhood pneumonia aetiology. *J. R. Stat. Soc. C*, 65: 97–114. [![](https://img.shields.io/badge/doi-10.1111/rssc.12101-yellow.svg)](https://doi.org/10.1111/rssc.12101) |
| nested pLCM | Wu, Z., Deloria-Knoll, M., Zeger, S.L.; Nested partially latent class models for dependent binary data; estimating disease etiology. *Biostatistics* 2017; 18 (2): 200-213. [![](https://img.shields.io/badge/doi-10.1093/biostatistics/kxw037-yellow.svg)](https://doi.org/10.1093/biostatistics/kxw037) |
| nested pLCM regression | Wu, Z., Chen, I (2021). Probabilistic Cause-of-disease Assignment using Case-control Diagnostic Tests: A Hierarchical Bayesian Approach. *Statistics in Medicine* 40(4):823-841. [![](https://img.shields.io/badge/doi-10.1002/sim.8804-yellow.svg)](https://doi.org/10.1002/sim.8804) |
| Application | Maria Deloria Knoll, Wei Fu, Qiyuan Shi, Christine Prosperi, Zhenke Wu, Laura L. Hammitt, Daniel R. Feikin, Henry C. Baggett, Stephen R.C. Howie, J. Anthony G. Scott, David R. Murdoch, Shabir A. Madhi, Donald M. Thea, W. Abdullah Brooks, Karen L. Kotloff, Mengying Li, Daniel E. Park, Wenyi Lin, Orin S. Levine, Katherine L. O’Brien, Scott L. Zeger; Bayesian Estimation of Pneumonia Etiology: Epidemiologic Considerations and Applications to the Pneumonia Etiology Research for Child Health Study, *Clinical Infectious Diseases*, Volume 64, Issue suppl_3, 15 June 2017, Pages S213–S227 [![](https://img.shields.io/badge/doi-10.1093/cid/cix144-yellow.svg)](https://doi.org/10.1093/cid/cix144) |
| Primary PERCH Analysis | The PERCH Study Group (2019). Aetiology of severe hospitalized pneumonia in HIV-uninfected children from Africa and Asia: the Pneumonia Aetiology Research for Child Health (PERCH) Case-Control Study. *The Lancet* 394(10200): 757-779. [![](https://img.shields.io/badge/doi-10.1016/S0140--6736(19)30721--4-yellow.svg)](https://doi.org/10.1016/S0140-6736(19)30721-4) |
| Software paper | Chen I, Shi Q, Zeger SL, Wu Z (2022+) `baker`: An `R` package for Nested Partially-Latent Class Models. [![](https://img.shields.io/badge/arxiv-preprint-red.svg)](https://arxiv.org/abs/2202.11778) [![](https://img.shields.io/badge/vignette-workflow-blue.svg)](https://zhenkewu.com/assets/html/baker_vignette.html) |

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:

|  | Notable References using `baker` (model and/or software) |
|----|----|
| 1 | Kubale et al. (2023). Etiology of acute lower respiratory illness hospitalizations among infants in four countries. *Open Forum Infectious Diseases*, ofad580. [![](https://img.shields.io/badge/doi-10.1093/ofid/ofad580-green.svg)](https://doi.org/10.1093/ofid/ofad580) |
| 2 | Saha SK et al. (2018). Causes and incidence of community-acquired serious infections among young children in south Asia (ANISA): an observational cohort study. *The Lancet* 392(10142):145-159. [![](https://img.shields.io/badge/doi-10.1016/S0140--6736(18)31127--9-green.svg)](https://doi.org/10.1016/S0140-6736(18)31127-9) |

## Table of content

<div id="id-section1">

- [1. Installation](#id-section1)
- [2. Vignettes](#id-section2)
- [3. Graphical User Interface (GUI)](#id-section3)
- [4. Analytic Goal](#id-section4)
- [5. Comparison to Other Existing Solutions](#id-section5)
- [6. Details](#id-section6)
- [7. Platform](#id-section7)
- [8. Connect `R` to `JAGS` on Unix systems or OSX](#id-section8)
- [9. Submit Jobs to Computing Cluster via a shell script](#id-section9)
- [10. Connect `R` to `JAGS` on Windows](#id-section10)
- [11. Example Datasets](#id-section11)

<div id="id-section1">

## Installation

``` r
# install.packages("devtools",repos="https://cloud.r-project.org")
devtools::install_github("zhenkewu/baker")
```

Note:

- run `install.packages("pbkrtest")` for `R(>=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 message `ERROR: loading failed for 'i386'`
  (Thanks Chrissy!).

<div id="id-section2">

## Vignettes

``` r
devtools::install_github("zhenkewu/baker", build_vignettes=TRUE) # will take extra time to run a few examples.
browseVignettes("baker")
```

<div id="id-section3">

## Graphical User Interface (GUI)

``` r
# 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](https://stackoverflow.com/questions/44143110/visualizing-r-function-dependencies)
of the function dependencies within the package:

``` r
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:

![](man/figures/nplcm_functional_dependence.png)

<div id="id-section4">

## Analytic Goal

- 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`.

<div id="id-section5">

## Comparison to Other Existing Solutions

- 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.

<div id="id-section6">

## Details

1.  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.
2.  `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.
3.  Scientists in [Pneumonia Etiology Research for Child
    Health](https://publichealth.jhu.edu/ivac) (PERCH) study usually
    refer to the etiology distribution as “*population etiology pie*”
    and “*individual etiology pie*” for their compositional nature,
    hence the name of the package.

<div id="id-section7">

## Platform

- 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 the `gitter` button at the top of this
  README file.

<div id="id-section8">

## Connect `R` to `JAGS`

#### Mac OSX (10.11+)

1.  Use [Just Another Gibbs Sampler
    (JAGS)](https://mcmc-jags.sourceforge.io/)
2.  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](https://sourceforge.net/projects/mcmc-jags/files/JAGS/4.x/Mac%20OS%20X/)
3.  Install `R`; Download from [here](https://cran.r-project.org/)
4.  Fire up `R`, run `R` command `install.packages("rjags")`
5.  Run `R` command `library(rjags)` in R console; If the installations
    are successful, you’ll see some notes like this:

``` r
>library(rjags)
Loading required package: coda
Linked to JAGS 4.x.0
Loaded modules: basemod,bugs
```

- Run `R` command `library(baker)`. If the package `ks` cannot be loaded
  due to failure of loading package `rgl`, first install X11 by going
  [here](https://www.xquartz.org/releases/XQuartz-2.7.11.html), followed
  by

``` r
install.packages("http://download.r-forge.r-project.org/src/contrib/rgl_0.95.1504.tar.gz",repo=NULL,type="source")
```

#### Unix (Build from source without administrative privilege)

Here we use [JHPCE](https://jhpce.jhu.edu/) as an example. [The complete
installation
guide](https://sourceforge.net/projects/mcmc-jags/files/Manuals/4.x/)
offers extra information.

1.  Download source code for [JAGS
    4.2.0](https://sourceforge.net/projects/mcmc-jags/files/JAGS/4.x/Source/JAGS-4.2.0.tar.gz/download);
    The workflow would be similar for later versions of `JAGS`.

2.  Suppose you’ve downloaded it in `~/local/jags/4.2.0`. Follow the
    bash commands below:

    ``` bash
    # 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")
    ```

3.  Also check out the
    [INSTALLATION](https://CRAN.R-project.org/package=rjags/INSTALL)
    file for `rjags` package.

<div id="id-section9">

## Submitting Jobs to Computing Cluster via a shell script

Again, I use JHPCE as an example.

``` bash
#!/bin/bash
#$ -M zhenkewu@gmail.com
#$ -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
```

<div id="id-section10">

#### Windows

- JAGS 4.2.0 (also applicable to later versions)

1.  Install `R`; Download from [here](https://cran.r-project.org/)
2.  Install [JAGS
    4.2.0](https://sourceforge.net/projects/mcmc-jags/files/JAGS/4.x/Windows/);
    Add the path to JAGS 4.2.0 into the environmental variable
    (essential for R to find the jags program). See
    [this](https://superuser.com/questions/949560/how-do-i-set-system-environment-variables-in-windows-10)
    for setting environmental variables;

- alternatives are `brew install -v jags` for OSX,
  `sudo apt-get install jags` for Ubuntu/Debian

3.  Fire up `R`, run `R` command `install.packages("rjags")`
4.  Install [`Rtools`](https://cran.r-project.org/bin/windows/Rtools/)
    (for building and installing R packages from source); Add the path
    to `Rtools` (e.g., `C:\Rtools\`) into your environmental variables
    so that R knows where to find it.

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<div id="id-section11">

#### Example data sets

We provide two simulated data sets in the package:

`data(data_nplcm_noreg)`

`data(data_nplcm_reg_nest)`

</div>

</div>

</div>

</div>

</div>

</div>

</div>

</div>

</div>

</div>

</div>

</div>
