In easystats/parameters: Processing of Model Parameters

knitr::opts_chunk$set(
  collapse = TRUE,
  dpi = 300,
  fig.path = "man/figures/",
  comment = "#>",
  warning = FALSE,
  message = FALSE,
  out.width = "100%"
)

set.seed(333)
library(parameters)

parameters

Describe and understand your model's parameters!

parameters' primary goal is to provide utilities for processing the parameters of various statistical models (see here for a list of supported models). Beyond computing p-values, CIs, Bayesian indices and other measures for a wide variety of models, this package implements features like bootstrapping of parameters and models, feature reduction (feature extraction and variable selection), or 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 or 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.

Installation

Type | Source | Command ---|---|--- Release | CRAN | install.packages("parameters") Development | r - universe | install.packages("parameters", repos = "https://easystats.r-universe.dev") Development | GitHub | remotes::install_github("easystats/parameters")

Tip

Instead of library(parameters), use library(easystats). This will make all features of the easystats-ecosystem available.

To stay updated, use easystats::install_latest().

Documentation

Click on the buttons above to access the package documentation and the easystats blog, and check-out these vignettes:

• Summary of Model Parameters
• Parameter and Model Standardization
• Robust Estimation of Standard Errors, Confidence Intervals and p-values
• Model Parameters and Missing Data
• Feature reduction (PCA, cMDS, ICA...)
• Structural models (EFA, CFA, SEM...)
• Parameters selection
• A Practical Guide for Panel Data Analysis
• Plotting functions

Contributing and Support

In case you want to file an issue or contribute in another way to the package, please follow this guide. For questions about the functionality, you may either contact us via email or also file an issue.

Features

Model's parameters description

knitr::include_graphics("man/figures/figure1.png")

The model_parameters() function (that can be accessed via the parameters() shortcut) allows you to extract the parameters and their characteristics from various models in a consistent way. It can be considered as a lightweight alternative to broom::tidy(), with some notable differences:

• The column names of the returned data frame are specific to their content. For instance, the column containing the statistic is named following the statistic name, i.e., t, z, etc., instead of a generic name such as statistic (however, you can get standardized (generic) column names using standardize_names()).
• It is able to compute or extract indices not available by default, such as p-values, CIs, etc.
• It includes feature engineering capabilities, including parameters bootstrapping.

Classical Regression Models

model <- lm(Sepal.Width ~ Petal.Length * Species + Petal.Width, data = iris)

# regular model parameters
model_parameters(model)

# standardized parameters
model_parameters(model, standardize = "refit")

# heteroscedasticity-consitent SE and CI
model_parameters(model, vcov = "HC3")

Mixed Models

library(lme4)
model <- lmer(Sepal.Width ~ Petal.Length + (1 | Species), data = iris)

# model parameters with CI, df and p-values based on Wald approximation
model_parameters(model)

# model parameters with CI, df and p-values based on Kenward-Roger approximation
model_parameters(model, ci_method = "kenward", effects = "fixed")

Structural Models

Besides many types of regression models and packages, it also works for other types of models, such as structural models (EFA, CFA, SEM...).

library(psych)

model <- psych::fa(attitude, nfactors = 3)
model_parameters(model)

Variable and parameters selection

knitr::include_graphics("man/figures/figure2.png")

select_parameters() can help you quickly select and retain the most relevant predictors using methods tailored for the model type.

lm(disp ~ ., data = mtcars) |>
  select_parameters() |>
  model_parameters()

Citation

In order to cite this package, please use the following command:

citation("parameters")

Code of Conduct

Please note that the parameters project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.

easystats/parameters documentation built on Aug. 24, 2024, 4:41 p.m.