Partial association, the dependency between variables after adjusting for a set of covariates, is an important statistical notion for scientific research. However, if the variables of interest are ordered categorical data, the development of statistical methods and software for assessing their partial association is limited. Following the framework established by Liu et al. (2021), we develop an R package PAsso for assessing Partial Associations between ordinal variables. The package provides various functions that allow users to perform a wide spectrum of assessments, including quantification, visualization, and hypothesis testing. In this paper, we discuss the implementation of PAsso in detail and demonstrate its utility through an analysis of the 2016 American National Election Study.
PAsso, sure, MASS, stats, pcaPP, copBasic, rms, ordinal, VGAM, GGally, ggplot2, plotly
Econometrics, Psychometrics, Distributions, Multivariate, SocialSciences, Environmetrics, Robust, Survival, TeachingStatistics, ChemPhys, ExtremeValue, NumericalMathematics, Phylogenetics, ReproducibleResearch, WebTechnologies
Text and figures are licensed under Creative Commons Attribution CC BY 4.0. The figures that have been reused from other sources don't fall under this license and can be recognized by a note in their caption: "Figure from ...".
For attribution, please cite this work as
Li, et al., "PAsso: an R Package for Assessing Partial Association between Ordinal Variables", The R Journal, 2021
BibTeX citation
@article{RJ-2021-088,
author = {Li, Shaobo and Zhu, Xiaorui and Chen, Yuejie and Liu, Dungang},
title = {PAsso: an R Package for Assessing Partial Association between Ordinal Variables},
journal = {The R Journal},
year = {2021},
note = {https://doi.org/10.32614/RJ-2021-088},
doi = {10.32614/RJ-2021-088},
volume = {13},
issue = {2},
issn = {2073-4859},
pages = {239-252}
}