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parameters

:warning: For Bayesian models, we changed the default the CI width! Please make an informed decision and set it explicitly (ci = 0.89, ci = 0.95, or anything else that you decide) :warning:

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

Run the following to install the stable release of parameters from CRAN:

install.packages("parameters")

Or this one to install the latest development version from R-universe…

install.packages("parameters", repos = "https://easystats.r-universe.dev")

…or from GitHub:

install.packages("remotes")
remotes::install_github("easystats/parameters")

Documentation

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

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

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)
#> 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

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 | [0.89, 3.11] |   3.56 | < .001
#> Petal Length |        0.28 | 0.06 | [0.16, 0.40] |   4.75 | < .001
#> 
#> # Random Effects
#> 
#> Parameter               | Coefficient |   SE |       95% CI
#> -----------------------------------------------------------
#> SD (Intercept: Species) |        0.89 | 0.46 | [0.33, 2.43]
#> SD (Residual)           |        0.32 | 0.02 | [0.28, 0.35]

# model parameters with CI, df and p-values based on Kenward-Roger approximation
model_parameters(model, ci_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 |   SE |       95% CI
#> -----------------------------------------------------------
#> SD (Intercept: Species) |        0.89 | 0.46 | [0.33, 2.43]
#> SD (Residual)           |        0.32 | 0.02 | [0.28, 0.35]

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

library(poorman)

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

Citation

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

citation("parameters")

To cite package 'parameters' in publications use:

  Lüdecke 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
  <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 Lüdecke and Mattan S. Ben-Shachar and Indrajeet Patil and Dominique Makowski},
    year = {2020},
    pages = {2445},
  }

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.

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Version

Install

install.packages('parameters')

Monthly Downloads

85,879

Version

0.18.1

License

GPL-3

Maintainer

Daniel Lüdecke

Last Published

May 29th, 2022

Functions in parameters (0.18.1)

check_clusterstructure

Check suitability of data for clustering
bootstrap_model

Model bootstrapping
check_heterogeneity

Check model predictor for heterogeneity bias
check_kmo

Kaiser, Meyer, Olkin (KMO) Measure of Sampling Adequacy (MSA) for Factor Analysis
compare_parameters

Compare model parameters of multiple models
convert_efa_to_cfa

Conversion between EFA results and CFA structure
.n_factors_bentler

Bentler and Yuan's Procedure
cluster_centers

Find the cluster centers in your data
bootstrap_parameters

Parameters bootstrapping
cluster_discrimination

Compute a linear discriminant analysis on classified cluster groups
.n_factors_cng

Cattell-Nelson-Gorsuch CNG Indices
cluster_meta

Metaclustering
.filter_component

for models with zero-inflation component, return required component of model-summary
ci.default

Confidence Intervals (CI)
check_sphericity_bartlett

Bartlett's Test of Sphericity
.n_factors_bartlett

Bartlett, Anderson and Lawley Procedures
check_factorstructure

Check suitability of data for Factor Analysis (FA)
degrees_of_freedom

Degrees of Freedom (DoF)
ci_robust

Robust confidence intervals. Superseded by the vcov* arguments in ci()
cluster_analysis

Cluster Analysis
cluster_performance

Performance of clustering models
model_parameters.cgam

Parameters from Generalized Additive (Mixed) Models
model_parameters.befa

Parameters from Bayesian Exploratory Factor Analysis
display.parameters_model

Print tables in different output formats
format_df_adjust

Format the name of the degrees-of-freedom adjustment methods
model_parameters.BFBayesFactor

Parameters from BayesFactor objects
fish

Sample data set
equivalence_test.lm

Equivalence test
get_scores

Get Scores from Principal Component Analysis (PCA)
.n_factors_sescree

Standard Error Scree and Coefficient of Determination Procedures
format_parameters

Parameter names formatting
format_order

Order (first, second, ...) formatting
format_p_adjust

Format the name of the p-value adjustment methods
.data_frame

help-functions
model_parameters.PMCMR

Parameters from Hypothesis Testing
model_parameters.default

Parameters from (General) Linear Models
.n_factors_mreg

Multiple Regression Procedure
model_parameters.mira

Parameters from multiply imputed repeated analyses
model_parameters.DirichletRegModel

Parameters from multinomial or cumulative link models
.n_factors_scree

Non Graphical Cattell's Scree Test
model_parameters.cpglmm

Parameters from Mixed Models
model_parameters.lavaan

Parameters from CFA/SEM models
model_parameters.PCA

Parameters from Structural Models (PCA, EFA, ...)
model_parameters.zcpglm

Parameters from Zero-Inflated Models
model_parameters.rma

Parameters from Meta-Analysis
model_parameters

Model Parameters
n_clusters

Find number of clusters in your data
p_value

p-values
p_value.DirichletRegModel

p-values for Models with Special Components
factor_analysis

Principal Component Analysis (PCA) and Factor Analysis (FA)
print.parameters_model

Print model parameters
p_value.zcpglm

p-values for Models with Zero-Inflation
parameters_type

Type of model parameters
p_value.poissonmfx

p-values for Marginal Effects Models
ci_satterthwaite

Satterthwaite approximation for SEs, CIs and p-values
model_parameters.averaging

Parameters from special models
.factor_to_dummy

Safe transformation from factor/character to numeric
model_parameters.aov

Parameters from ANOVAs
model_parameters.htest

Parameters from hypothesis tests
standard_error

Standard Errors
sort_parameters

Sort parameters by coefficient values
model_parameters.dbscan

Parameters from Cluster Models (k-means, ...)
p_value.BFBayesFactor

p-values for Bayesian Models
n_factors

Number of components/factors to retain in PCA/FA
model_parameters.data.frame

Parameters from Bayesian Models
reduce_parameters

Dimensionality reduction (DR) / Features Reduction
predict.parameters_clusters

Predict method for parameters_clusters objects
select_parameters

Automated selection of model parameters
reshape_loadings

Reshape loadings between wide/long formats
pool_parameters

Pool Model Parameters
standardize_parameters

Parameters standardization
ci_ml1

"m-l-1" approximation for SEs, CIs and p-values
standardize_info

Get Standardization Information
standard_error_robust

Robust standard errors. Superseded by the vcov* arguments in standard_error()
reexports

Objects exported from other packages
p_value_robust

Robust p-values. Superseded by the vcov* arguments in p_value()
qol_cancer

Sample data set
random_parameters

Summary information from random effects
ci_betwithin

Between-within approximation for SEs, CIs and p-values
model_parameters.t1way

Parameters from robust statistical objects in WRS2
ci_kenward

Kenward-Roger approximation for SEs, CIs and p-values
simulate_parameters.glmmTMB

Simulate Model Parameters
simulate_model

Simulated draws from model coefficients