README.md
In majuvi/llperm: LikeLihood based Permutation of Regression Residuals (PRR) test
llperm
LikeLihood based Permutation of Regression Residuals (PRR) test.
The basic approach is described in the paper (https://svn.r-project.org/Rjournal/trunk/html/_site/archive/2010-1/RJournal_2010-1_Werft+Benner.pdf):
Werft, Wiebke, and Axel Benner. "glmperm: A Permutation of Regressor Residuals Test for Inference in Generalized Linear Models." R J. 2.1 (2010): 39.
The idea is to calculate a p-value for a variable of interest based on a permutation test. This approach is more robust than using likelihood based inference with a regression model.
We extend the original implementation in three ways:
1. A categorical variable of interest with multiple levels
2. Can be applied to any log-likelihood based model, including over-dispersed and zero-inflated models.
3. Fit both count/zero components in zero-inflated models, then test either or both components separately.
Installation
From the public github repository:
devtools::install_git("https://github.com/majuvi/llperm")
From the internal RIVM repository:
devtools::install_git("https://gitl01-int-p.rivm.nl/viljanem/llperm")
If you cannot install the package, it is possible to just download the .zip and source all files:
sapply(file.path("llperm", "R", list.files(file.path("llperm", "R"), pattern="*.R")),source,.GlobalEnv)
Usage
See the 'vignettes' folder. We have two vignettes:
1. 'introduction.Rmd' shows how to fit the models to a real data set (VEGA subset), and shows that glmperm can fix standard models.
2. 'introduction2.Rmd' shows how to simulate Zero-Inflated Negative Binomial Data, and demonstrates that the standard models do well.
Experiments for the paper can be found in
1. 'llperm_experiments.Rmd': shows how to run all experiments in a local machine, save the results and then visualize the tables & figures.
2. 'llperm_experiments_LSF.Rmd': shows how to run the same experiments in a high-performance LSF cluster called 'biogrid' at RIVM, faster.
3. 'llperm_investigation.Rmd': some codes when we investigated why Beta Binomial / Negative Binomial differ and MASS had different results.
License
GPL-3. The package was inspired by the GPL licensed packages glmperm, pscl, ZIBBSeqDiscovery and glm.fit in base R.
majuvi/llperm documentation built on May 2, 2022, 5:20 p.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
llperm
LikeLihood based Permutation of Regression Residuals (PRR) test.
The basic approach is described in the paper (https://svn.r-project.org/Rjournal/trunk/html/_site/archive/2010-1/RJournal_2010-1_Werft+Benner.pdf):
Werft, Wiebke, and Axel Benner. "glmperm: A Permutation of Regressor Residuals Test for Inference in Generalized Linear Models." R J. 2.1 (2010): 39.
The idea is to calculate a p-value for a variable of interest based on a permutation test. This approach is more robust than using likelihood based inference with a regression model.
We extend the original implementation in three ways: 1. A categorical variable of interest with multiple levels 2. Can be applied to any log-likelihood based model, including over-dispersed and zero-inflated models. 3. Fit both count/zero components in zero-inflated models, then test either or both components separately.
Installation
From the public github repository:
devtools::install_git("https://github.com/majuvi/llperm")
From the internal RIVM repository:
devtools::install_git("https://gitl01-int-p.rivm.nl/viljanem/llperm")
If you cannot install the package, it is possible to just download the .zip and source all files:
sapply(file.path("llperm", "R", list.files(file.path("llperm", "R"), pattern="*.R")),source,.GlobalEnv)
Usage
See the 'vignettes' folder. We have two vignettes: 1. 'introduction.Rmd' shows how to fit the models to a real data set (VEGA subset), and shows that glmperm can fix standard models. 2. 'introduction2.Rmd' shows how to simulate Zero-Inflated Negative Binomial Data, and demonstrates that the standard models do well.
Experiments for the paper can be found in 1. 'llperm_experiments.Rmd': shows how to run all experiments in a local machine, save the results and then visualize the tables & figures. 2. 'llperm_experiments_LSF.Rmd': shows how to run the same experiments in a high-performance LSF cluster called 'biogrid' at RIVM, faster. 3. 'llperm_investigation.Rmd': some codes when we investigated why Beta Binomial / Negative Binomial differ and MASS had different results.
License
GPL-3. The package was inspired by the GPL licensed packages glmperm, pscl, ZIBBSeqDiscovery and glm.fit in base R.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.