Extracting, Computing and Exploring the Parameters of Statistical Models using R

The recent growth of data science is partly fueled by the ever-growing amount of data and the joint important developments in statistical modeling, with new and powerful models and frameworks becoming accessible to users. Although there exist some generic functions to obtain model summaries and parameters, many package-specific modeling functions do not provide such methods to allow users to access such valuable information.


Summary
The recent growth of data science is partly fueled by the ever-growing amount of data and the joint important developments in statistical modeling, with new and powerful models and frameworks becoming accessible to users. Although there exist some generic functions to obtain model summaries and parameters, many package-specific modeling functions do not provide such methods to allow users to access such valuable information.

Aims of the Package
parameters is an R-package (R Core Team, 2020) that fills this important gap. Its primary goal is to provide utilities for processing the parameters of various statistical models. Beyond computing p-values, standard errors, confidence intervals (CI), Bayesian indices and other measures for a wide variety of models, this package implements features like parameters bootstrapping and engineering (such as variables reduction and/or selection), as well as tools for data reduction like functions to perform cluster, factor or principal component analysis.
Another important goal of the parameters package is to facilitate and streamline the process of reporting results of statistical models, which includes the easy and intuitive calculation of standardized estimates in addition to robust standard errors and p-values. parameters therefor offers a simple and unified syntax to process a large variety of (model) objects from many different packages.
parameters is part of the easystats ecosystem, a collaborative project created to facilitate the usage of R for statistical analyses.
• parameters easily allows to compute standardized estimates, robust estimation, smallsample-size corrections for degrees of freedom (like Satterthwaite or Kenward-Roger), bootstrapping or simulating parameters, and feature reduction. Furthermore, parameters provides functions to test for the presence or absence of an effect (equivalence testing, see Lakens, Scheel, & Isager, 2018). • For most functions, easy-to-use plot()-methods exist to quickly create nice looking plots (powered by the see package (Lüdecke et al., 2020a)). • parameters is a very lightweight package. Its main functionality only relies on the insight, the bayestestR, and the effectsize packages (Ben-Shachar, Makowski, & Lüdecke, 2020;Lüdecke, Waggoner, & Makowski, 2019;Makowski, Ben-Shachar, & Lüdecke, 2019) to access and process information contained in models, and these packages in turn only depend on R core packages. However, additional features that do not belong to the core functions of parameters require the installation of other packages, such as sandwich (Zeileis, 2006) for robust estimation, psych (Revelle, 2019) for factor analysis or PCA or cAIC4 (Saefken, Ruegamer, Kneib, & Greven, 2018) for parameter selection for mixed models.

Examples of Features
As stated above, parameters creates summary tables of many different statistical models. The workflow is simple: fit a model and pass it to the model_parameters() function (or its shortcut, parameters()) to obtain information about the model's parameters.
In the following, we show some brief examples. However, a comprehensive overview including in-depth examples are accessible via the dedicated website (https://easystats.github.io/ parameters/).

Summary of Model Parameters
model_parameters() allows you to extract the parameters and their characteristics from various models in a consistent way.

library(parameters)
model <-lm(Sepal.Length~Species, data = iris) parameters(model) Extraction of robust indices is possible for many models, in particular models supported by the sandwich (Zeileis, 2006) and clubSandwich (Pustejovsky, 2020) packages. For linear mixed models, parameters() also allows to specify the method for approximating degrees of freedom, which may improve the accurracy for calculated standard errors or pvalues.