https://github.com/aaronpeikert/parameters
:bar_chart: Computation and processing of models' parameters
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:bar_chart: Computation and processing of models' parameters
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- Host: GitHub
- Owner: aaronpeikert
- License: gpl-3.0
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- Homepage: https://easystats.github.io/parameters/
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# parameters[](https://doi.org/10.21105/joss.02445) [](https://cran.r-project.org/package=parameters) [](https://cranlogs.r-pkg.org/) ***Describe and understand your models 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 [](https://cran.r-project.org/package=parameters) [](https://github.com/easystats/parameters/actions) Run the following to install the stable release of **parameters** from CRAN: ``` r install.packages("parameters") ``` Or this one to install the latest development version: ``` r install.packages("remotes") remotes::install_github("easystats/parameters") ``` ## Documentation [](https://easystats.github.io/parameters/) [](https://easystats.github.io/blog/posts/) [](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) - [Standardized Model Parameters](https://easystats.github.io/parameters/articles/model_parameters_standardized.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) ## 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/master/.github/CONTRIBUTING.md). For questions about the functionality, you may either contact us via email or also file an issue. # Features ## Models parameters description
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) #> Parameter | Coefficient | SE | 95% CI | t(143) | p #> ------------------------------------------------------------------------------------------- #> (Intercept) | 2.89 | 0.36 | [ 2.18, 3.60] | 8.01 | < .001 #> Petal Length | 0.26 | 0.25 | [-0.22, 0.75] | 1.07 | 0.287 #> Species [versicolor] | -1.66 | 0.53 | [-2.71, -0.62] | -3.14 | 0.002 #> Species [virginica] | -1.92 | 0.59 | [-3.08, -0.76] | -3.28 | 0.001 #> Petal Width | 0.62 | 0.14 | [ 0.34, 0.89] | 4.41 | < .001 #> Petal Length * Species [versicolor] | -0.09 | 0.26 | [-0.61, 0.42] | -0.36 | 0.721 #> Petal Length * Species [virginica] | -0.13 | 0.26 | [-0.64, 0.38] | -0.50 | 0.618 # standardized parameters model_parameters(model, standardize = "refit") #> Parameter | Coefficient | SE | 95% CI | t(143) | p #> ------------------------------------------------------------------------------------------- #> (Intercept) | 3.59 | 1.30 | [ 1.01, 6.17] | 2.75 | 0.007 #> Petal Length | 1.07 | 1.00 | [-0.91, 3.04] | 1.07 | 0.287 #> Species [versicolor] | -4.62 | 1.31 | [-7.21, -2.03] | -3.53 | < .001 #> Species [virginica] | -5.51 | 1.38 | [-8.23, -2.79] | -4.00 | < .001 #> Petal Width | 1.08 | 0.24 | [ 0.59, 1.56] | 4.41 | < .001 #> Petal Length * Species [versicolor] | -0.38 | 1.06 | [-2.48, 1.72] | -0.36 | 0.721 #> Petal Length * Species [virginica] | -0.52 | 1.04 | [-2.58, 1.54] | -0.50 | 0.618 ``` ### 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, effects = "all") #> # Fixed Effects #> #> Parameter | Coefficient | SE | 95% CI | t(146) | p #> ------------------------------------------------------------------ #> (Intercept) | 2.00 | 0.56 | [0.90, 3.10] | 3.56 | < .001 #> Petal Length | 0.28 | 0.06 | [0.17, 0.40] | 4.75 | < .001 #> #> # Random Effects #> #> Parameter | Coefficient #> ------------------------------------- #> SD (Intercept: Species) | 0.89 #> SD (Residual) | 0.56 # model parameters with CI, df and p-values based on Kenward-Roger approximation model_parameters(model, df_method = "kenward") #> # Fixed Effects #> #> Parameter | Coefficient | SE | 95% CI | t | df | p #> ------------------------------------------------------------------------- #> (Intercept) | 2.00 | 0.57 | [0.07, 3.93] | 3.53 | 2.67 | 0.046 #> Petal Length | 0.28 | 0.06 | [0.16, 0.40] | 4.58 | 140.98 | < .001 #> #> # Random Effects #> #> Parameter | Coefficient #> ------------------------------------- #> SD (Intercept: Species) | 0.89 #> SD (Residual) | 0.56 ``` ### 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 library(psych) model <- psych::fa(attitude, nfactors = 3) model_parameters(model) #> # Rotated loadings from Factor Analysis (oblimin-rotation) #> #> Variable | MR1 | MR2 | MR3 | Complexity | Uniqueness #> ------------------------------------------------------------ #> rating | 0.90 | -0.07 | -0.05 | 1.02 | 0.23 #> complaints | 0.97 | -0.06 | 0.04 | 1.01 | 0.10 #> privileges | 0.44 | 0.25 | -0.05 | 1.64 | 0.65 #> learning | 0.47 | 0.54 | -0.28 | 2.51 | 0.24 #> raises | 0.55 | 0.43 | 0.25 | 2.35 | 0.23 #> critical | 0.16 | 0.17 | 0.48 | 1.46 | 0.67 #> advance | -0.11 | 0.91 | 0.07 | 1.04 | 0.22 #> #> The 3 latent factors (oblimin rotation) accounted for 66.60% of the total variance of the original data (MR1 = 38.19%, MR2 = 22.69%, MR3 = 5.72%). ``` ## Variable and parameters selection
[`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 library(dplyr) lm(disp ~ ., data = mtcars) %>% select_parameters() %>% model_parameters() #> Parameter | Coefficient | SE | 95% CI | t(26) | p #> ----------------------------------------------------------------------- #> (Intercept) | 141.70 | 125.67 | [-116.62, 400.02] | 1.13 | 0.270 #> cyl | 13.14 | 7.90 | [ -3.10, 29.38] | 1.66 | 0.108 #> hp | 0.63 | 0.20 | [ 0.22, 1.03] | 3.18 | 0.004 #> wt | 80.45 | 12.22 | [ 55.33, 105.57] | 6.58 | < .001 #> qsec | -14.68 | 6.14 | [ -27.31, -2.05] | -2.39 | 0.024 #> carb | -28.75 | 5.60 | [ -40.28, -17.23] | -5.13 | < .001 ``` ## Miscellaneous This packages also contains a lot of [other useful functions](https://easystats.github.io/parameters/reference/index.html): ### Describe a Distribution ``` r data(iris) describe_distribution(iris) #> Variable | Mean | SD | IQR | Range | Skewness | Kurtosis | n | n_Missing #> ---------------------------------------------------------------------------------------- #> Sepal.Length | 5.84 | 0.83 | 1.30 | [4.30, 7.90] | 0.31 | -0.55 | 150 | 0 #> Sepal.Width | 3.06 | 0.44 | 0.52 | [2.00, 4.40] | 0.32 | 0.23 | 150 | 0 #> Petal.Length | 3.76 | 1.77 | 3.52 | [1.00, 6.90] | -0.27 | -1.40 | 150 | 0 #> Petal.Width | 1.20 | 0.76 | 1.50 | [0.10, 2.50] | -0.10 | -1.34 | 150 | 0 ``` ### Citation In order to cite this package, please use the following command: ``` r citation("parameters") Ldecke D, Ben-Shachar M, Patil I, Makowski D (2020). "Extracting, Computing and Exploring the Parameters of Statistical Models using R." _Journal of Open Source Software_, *5*(53), 2445. doi: 10.21105/joss.02445 (URL: https://doi.org/10.21105/joss.02445). A BibTeX entry for LaTeX users is @Article{, title = {Extracting, Computing and Exploring the Parameters of Statistical Models using {R}.}, volume = {5}, doi = {10.21105/joss.02445}, number = {53}, journal = {Journal of Open Source Software}, author = {Daniel Ldecke and Mattan S. Ben-Shachar and Indrajeet Patil and Dominique Makowski}, year = {2020}, pages = {2445}, } ```
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