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

Extracting, Computing and Exploring the Parameters of Statistical Models using R - Published in JOSS (2020)

https://github.com/easystats/parameters

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:bar_chart: Computation and processing of models' parameters

README.Rmd

---
output: github_document
bibliography: paper/paper.bib
csl: paper/apa.csl
---

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

options(easystats_table_width = Inf)
set.seed(333)
library(parameters)
```

# parameters 

[![DOI](https://joss.theoj.org/papers/10.21105/joss.02445/status.svg)](https://doi.org/10.21105/joss.02445)
[![downloads](https://cranlogs.r-pkg.org/badges/parameters)](https://cran.r-project.org/package=parameters)
[![total](https://cranlogs.r-pkg.org/badges/grand-total/parameters)](https://cranlogs.r-pkg.org/)

***Describe and understand your model's parameters!***

**parameters**' primary goal is to provide utilities for processing the parameters of various statistical models (see [here](https://easystats.github.io/insight/) 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

[![CRAN](https://www.r-pkg.org/badges/version/parameters)](https://cran.r-project.org/package=parameters) [![parameters status badge](https://easystats.r-universe.dev/badges/parameters)](https://easystats.r-universe.dev) [![codecov](https://codecov.io/gh/easystats/parameters/branch/main/graph/badge.svg)](https://app.codecov.io/gh/easystats/parameters)

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

[![Documentation](https://img.shields.io/badge/documentation-parameters-orange.svg?colorB=E91E63)](https://easystats.github.io/parameters/)
[![Blog](https://img.shields.io/badge/blog-easystats-orange.svg?colorB=FF9800)](https://easystats.github.io/blog/posts/)
[![Features](https://img.shields.io/badge/features-parameters-orange.svg?colorB=2196F3)](https://easystats.github.io/parameters/reference/index.html)

Click on the buttons above to access the package [documentation](https://easystats.github.io/parameters/) and the [easystats blog](https://easystats.github.io/blog/posts/), and check-out these vignettes:

- [Summary of Model Parameters](https://easystats.github.io/parameters/articles/model_parameters.html)
- [Parameter and Model Standardization](https://easystats.github.io/parameters/articles/standardize_parameters_effsize.html)
- [Robust Estimation of Standard Errors, Confidence Intervals and p-values](https://easystats.github.io/parameters/articles/model_parameters_robust.html)
- [Model Parameters and Missing Data](https://easystats.github.io/parameters/articles/model_parameters_mice.html)
- [Feature reduction (PCA, cMDS, ICA...)](https://easystats.github.io/parameters/articles/parameters_reduction.html)
- [Structural models (EFA, CFA, SEM...)](https://easystats.github.io/parameters/articles/efa_cfa.html)
- [Parameters selection](https://easystats.github.io/parameters/articles/parameters_selection.html)
- [A Practical Guide for Panel Data Analysis](https://easystats.github.io/parameters/articles/demean.html)
- [Plotting functions](https://easystats.github.io/see/articles/parameters.html)

## Contributing and Support

In case you want to file an issue or contribute in another way to the package, please follow [this guide](https://github.com/easystats/parameters/blob/main/.github/CONTRIBUTING.md). For questions about the functionality, you may either contact us via email or also file an issue.


# Features

## Model's parameters description

```{r echo=FALSE, fig.align='center', dpi=96}
knitr::include_graphics("man/figures/figure1.png")
```

The [`model_parameters()`](https://easystats.github.io/parameters/articles/model_parameters.html) 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()`](https://github.com/tidymodels/broom), 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()`](https://easystats.github.io/insight/reference/standardize_names.html)).
- 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](https://easystats.github.io/parameters/reference/bootstrap_parameters.html).


### Classical Regression Models

```{r}
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

```{r}
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**](https://easystats.github.io/parameters/articles/efa_cfa.html) (EFA, CFA, SEM...).

```{r, warning=FALSE, message=FALSE}
library(psych)

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



## Variable and parameters selection

```{r echo=FALSE, fig.align='center', dpi=96}
knitr::include_graphics("man/figures/figure2.png")
```

[`select_parameters()`](https://easystats.github.io/parameters/articles/parameters_selection.html) can help you quickly select and retain the most relevant predictors using methods tailored for the model type.

```{r, warning=FALSE, message=FALSE}
lm(disp ~ ., data = mtcars) |>
  select_parameters() |>
  model_parameters()
```


## Statistical inference - how to quantify evidence
There is no standardized approach to drawing conclusions based on the available data and statistical models. A frequently chosen but also much criticized approach is to evaluate results based on their statistical significance [@amrhein_earth_2017].

A more sophisticated way would be to test whether estimated effects exceed the "smallest effect size of interest", to avoid even the smallest effects being considered relevant simply because they are statistically significant, but clinically or practically irrelevant [@lakens2020equivalence;@lakens_improving_2022]. A rather unconventional approach, which is nevertheless advocated by various authors, is to interpret results from classical regression models in terms of probabilities, similar to the usual approach in Bayesian statistics [@greenland_aid_2022;@rafi_semantic_2020;@schweder_confidence_2018;@schweder_frequentist_2003;@vos_frequentist_2022].

The _parameters_ package provides several options or functions to aid statistical inference. These are, for example:

- [`equivalence_test()`](https://easystats.github.io/parameters/reference/equivalence_test.lm.html), to compute the (conditional) equivalence test for frequentist models
- [`p_significance()`](https://easystats.github.io/parameters/reference/p_significance.lm.html), to compute the probability of *practical significance*, which can be conceptualized as a unidirectional equivalence test
- [`p_function()`](https://easystats.github.io/parameters/reference/p_function.html), or _consonance function_, to compute p-values and compatibility (confidence) intervals for statistical models
- the `pd` argument (setting `pd = TRUE`) in `model_parameters()` includes a column with the *probability of direction*, i.e. the probability that a parameter is strictly positive or negative. See [`bayestestR::p_direction()`](https://easystats.github.io/bayestestR/reference/p_direction.html) for details.
- the `s_value` argument (setting `s_value = TRUE`) in `model_parameters()` replaces the p-values with their related _S_-values [@@rafi_semantic_2020]
- finally, it is possible to generate distributions of model coefficients by generating bootstrap-samples (setting `bootstrap = TRUE`) or simulating draws from model coefficients using [`simulate_model()`](https://easystats.github.io/parameters/reference/simulate_model.html). These samples can then be treated as "posterior samples" and used in many functions from the **bayestestR** package.

Most of the above shown options or functions derive from methods originally implemented for Bayesian models [@makowski_indices_2019]. However, assuming that model assumptions are met (which means, the model fits well to the data, the correct model is chosen that reflects the data generating process (distributional model family) etc.), it seems appropriate to interpret results from classical frequentist models in a "Bayesian way" (more details: documentation in [`p_function()`](https://easystats.github.io/parameters/reference/p_function.html)).

## Citation

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

```{r, comment=""}
citation("parameters")
```

## Code of Conduct

Please note that the parameters project is released with a [Contributor Code of Conduct](https://www.contributor-covenant.org/version/2/1/code_of_conduct/). By contributing to this project, you agree to abide by its terms.

## References

Owner

JOSS Publication

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

Published

September 09, 2020

DOI

10.21105/joss.02445

Volume 5, Issue 53, Page 2445

Authors

Daniel Lüdecke
University Medical Center Hamburg-Eppendorf, Germany

Mattan S. Ben-Shachar
Ben-Gurion University of the Negev, Israel

Indrajeet Patil
Max Planck Institute for Human Development, Germany

Dominique Makowski
Nanyang Technological University, Singapore

Editor

Sebastian Benthall

Tags

easystats parameters regression linear models coefficients

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