README.md

In oslerinhealth-releases/baker: Bayesian Analysis Kit for Etiology Research

baker: Bayesian Analysis Kit for Etiology Research

An R Package for Fitting Bayesian Nested Partially Latent Class Models

Maintainer: Zhenke Wu, [e-mail]

Issues Please click here to report reproducible issues.

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

Citation Paper Link 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. doi:10.1111/rssc.12101 Link 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. doi: 10.1093/biostatistics/kxw037 Link 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 Link

Table of content

• 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 to JAGS/WinBUGS

Installation ------------ wzxhzdk:0 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!).

Vignettes --------- wzxhzdk:1

Graphical User Interface (GUI) ------------------------------ wzxhzdk:2 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: wzxhzdk:3 You will get a dynamic figure. A snapshot is below: 

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`.

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.

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](http://www.jhsph.edu/research/centers-and-institutes/ivac/projects/perch/) (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.

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.

Connect `R` to `JAGS`/`WinBUGS` ------------------------------- #### Mac OSX 10.11 El Capitan 1. Use [Just Another Gibbs Sampler (JAGS)](http://mcmc-jags.sourceforge.net/) 2. Install JAGS 4.2.0; 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); 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. #### Windows - JAGS 4.2.0 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](http://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 1. Fire up `R`, run `R` command `install.packages("rjags")` 2. Install [`Rtools`](https://cran.r-project.org/bin/windows/Rtools/) (for building and installing R pacakges from source); Add the path to `Rtools` (e.g. `C:\Rtools\`) into your environmental variables so that R knows where to find it. - [WinBUGS 1.4.3](http://www.mrc-bsu.cam.ac.uk/software/bugs/the-bugs-project-winbugs/) 1. Install the [patch](http://www.mrc-bsu.cam.ac.uk/software/bugs/the-bugs-project-winbugs/the-bugs-project-winbugs-patches/) 2. Install the WinBUGS 1.4.x [immortality key](http://www.mrc-bsu.cam.ac.uk/software/bugs/the-bugs-project-winbugs/)

oslerinhealth-releases/baker documentation built on Nov. 4, 2019, 11:11 p.m.