ordPens

ordPens: An R package for Selection, Smoothing and Principal Components Analysis for Ordinal Variables - Published in JOSS (2021)

https://github.com/ahoshiyar/ordpens

Scientific Fields

Repository

Ordinal PCA, Selection and/or Smoothing of Ordinal Predictors

README.Rmd

---
output: github_document
---



```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.path = "man/figures/README-",
  out.width = "100%"
)
```

# `ordPens`: Selection and/or Smoothing and Principal Components Analysis for Ordinal Variables




We provide selection, and/or smoothing/fusion of ordinally scaled independent variables using a group lasso or generalized ridge penalty. In addition, nonlinear principal components analysis for ordinal variables is offered, using a second-order difference penalty. 

Also, ANOVA with ordered factors is provided by the function `ordAOV`; testing for differentially expressed genes can be done using `ordGene`. For details cf. Gertheiss (2014) and Sweeney et al. (2015), respectively.

For smoothing, selection and fusion, details may be found in Tutz and Gertheiss (2014, 2016). 
All functions are documented in detail in `vignette("ordPens", package = "ordPens")`. For smoothing only, the package also builds a bridge to `mgcv::gam()`, see Gertheiss et al. (2022) for further information.

For the function implementing nonlinear principal components analysis, `ordPCA`, details can be found in Hoshiyar et al. (2021) and `vignette("ordPCA", package = "ordPens")`.

Version 1.1.0 is a minor release with new functions:

* Functions `ordSelect`, `ordFusion` updated/extended to cumulative
    logit model models.
*  Function `ordCV` added, provides cross-validation for penalized
    regression models with ordinal predictors.  
*   Function `StabilityCumu` added, provides stability selection for
    penalized cumulative logit models.


Version 1.0.0 is a major release with new functions:

* `ordPCA` applies nonlinear principal components analysis for ordinal variables.
Also, performance evaluation and selection of an optimal penalty parameter provided.  
* `ordFusion` fits dummy coefficients of ordinally scaled independent variables with a fused lasso penalty for fusion and selection.
* A new type of spline basis for ordered factors `s(..., bs = "ordinal")`is provided, such that smooth terms in the `mgcv::gam()` formula can be used as an alternative and extension to `ordSmooth()`. Additionally, generic functions for prediction and plotting are provided.  
 
## Installation & getting started

For standard use, install `ordPens` from [CRAN](https://cran.r-project.org/package=ordPens):
```{r CRAN-install, eval=FALSE}
install.packages("ordPens")
```

The development version of the package may be installed from GitHub:
```{r git-install, eval=FALSE} 
devtools::install_git("https://github.com/ahoshiyar/ordPens", build_vignettes = TRUE)
```

For a detailed overview about the functionalities and given examples type:
```{r vignettes, eval=FALSE} 
library(ordPens)
vignette("ordPens", package = "ordPens")
vignette("ordPCA", package = "ordPens")
```

## Issues

If you encounter any bugs or have any specific feature requests, please [file an issue](https://github.com/ahoshiyar/ordPens/issues).

## Contributions & Code of conduct 

Contributions are very welcome. Interested contributors should consult the
[contribution
guidelines](https://github.com/ahoshiyar/ordPens/blob/master/Contributing.md)
prior to submitting a pull request.
  
Please note that the `ordPens` package is released with a [Contributor Code of Conduct](https://www.contributor-covenant.org/version/2/0/code_of_conduct/). By contributing to this project, you agree to abide by its terms.

## References

* Gertheiss, J. (2014). ANOVA for factors with ordered levels. *Journal of Agricultural, Biological and Environmental Statistics 19*, 258-277.

* Gertheiss, J., F. Scheipl, T. Lauer, and H. Ehrhardt (2022). Statistical inference for ordinal predictors in generalized linear and additive models with application to bronchopulmonary dysplasia. *BMC research notes 15*, 112.  

* Hoshiyar, A., H.A.L. Kiers, and J. Gertheiss (2021). Penalized non-linear principal components analysis for ordinal variables with an application to international classification of functioning core sets. *British Journal of Mathematical and Statistical Psychology 76*, 353-371.

* Hoshiyar, A., Gertheiss, L.H., and Gertheiss, J. (2023). Regularization and model selection for item-on-items regression with applications to food products' survey data. Preprint, available from https://arxiv.org/abs/2309.16373.

* Sweeney, E., C. Crainiceanu, and J. Gertheiss (2015). Testing differentially expressed genes in dose-response studies and with ordinal phenotypes. *Statistical Applications in Genetics and Molecular Biology 15*, 213-235.

* Tutz, G. and J. Gertheiss (2014). Rating scales as predictors – the old question of scale level and some answers. *Psychometrica 79*, 357-376.

* Tutz, G. and J. Gertheiss (2016). Regularized regression for categorical data. *Statistical Modelling 16*, 161-200.

JOSS Publication

ordPens: An R package for Selection, Smoothing and Principal Components Analysis for Ordinal Variables

Published

December 06, 2021

DOI

10.21105/joss.03828

Volume 6, Issue 68, Page 3828

Authors

Aisouda Hoshiyar
School of Economics and Social Sciences, Helmut Schmidt University, Hamburg, Germany

Editor

Øystein Sørensen

Tags

smoothing ordinal ANOVA Nonlinear Principal Components Analysis fusion lasso

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