parameters

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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).

Installation

Run the following:

install.packages("parameters")
library("parameters")

Documentation

Documentation Blog Features

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

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:

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 |  df |      p
#> ------------------------------------------------------------------------------------------------
#> (Intercept)                         |        2.89 | 0.36 | [ 2.18,  3.60] |  8.01 | 143 | < .001
#> Petal.Length                        |        0.26 | 0.25 | [-0.22,  0.75] |  1.07 | 143 | 0.287 
#> Species [versicolor]                |       -1.66 | 0.53 | [-2.71, -0.62] | -3.14 | 143 | 0.002 
#> Species [virginica]                 |       -1.92 | 0.59 | [-3.08, -0.76] | -3.28 | 143 | 0.001 
#> Petal.Width                         |        0.62 | 0.14 | [ 0.34,  0.89] |  4.41 | 143 | < .001
#> Petal.Length * Species [versicolor] |       -0.09 | 0.26 | [-0.61,  0.42] | -0.36 | 143 | 0.721 
#> Petal.Length * Species [virginica]  |       -0.13 | 0.26 | [-0.64,  0.38] | -0.50 | 143 | 0.618

# standardized parameters
model_parameters(model, standardize = "refit")
#> Parameter                           | Coefficient |   SE |         95% CI |     t |  df |      p
#> ------------------------------------------------------------------------------------------------
#> (Intercept)                         |        3.59 | 1.30 | [ 1.01,  6.17] |  2.75 | 143 | 0.007 
#> Petal.Length                        |        1.07 | 1.00 | [-0.91,  3.04] |  1.07 | 143 | 0.287 
#> Species [versicolor]                |       -4.62 | 1.31 | [-7.21, -2.03] | -3.53 | 143 | < .001
#> Species [virginica]                 |       -5.51 | 1.38 | [-8.23, -2.79] | -4.00 | 143 | < .001
#> Petal.Width                         |        1.08 | 0.24 | [ 0.59,  1.56] |  4.41 | 143 | < .001
#> Petal.Length * Species [versicolor] |       -0.38 | 1.06 | [-2.48,  1.72] | -0.36 | 143 | 0.721 
#> Petal.Length * Species [virginica]  |       -0.52 | 1.04 | [-2.58,  1.54] | -0.50 | 143 | 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)
#> Parameter    | Coefficient |   SE |       95% CI |    t |  df |      p
#> ----------------------------------------------------------------------
#> (Intercept)  |        2.00 | 0.56 | [0.90, 3.10] | 3.56 | 146 | < .001
#> Petal.Length |        0.28 | 0.06 | [0.17, 0.40] | 4.75 | 146 | < .001

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

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

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

library(dplyr)

lm(disp ~ ., data = mtcars) %>% 
  select_parameters() %>% 
  model_parameters()
#> Parameter   | Coefficient |     SE |            95% CI |     t | df |      p
#> ----------------------------------------------------------------------------
#> (Intercept) |      141.70 | 125.67 | [-116.62, 400.02] |  1.13 | 26 | 0.270 
#> cyl         |       13.14 |   7.90 | [  -3.10,  29.38] |  1.66 | 26 | 0.108 
#> hp          |        0.63 |   0.20 | [   0.22,   1.03] |  3.18 | 26 | 0.004 
#> wt          |       80.45 |  12.22 | [  55.33, 105.57] |  6.58 | 26 | < .001
#> qsec        |      -14.68 |   6.14 | [ -27.31,  -2.05] | -2.39 | 26 | 0.024 
#> carb        |      -28.75 |   5.60 | [ -40.28, -17.23] | -5.13 | 26 | < .001

Miscellaneous

This packages also contains a lot of other useful functions:

Describe a Distribution

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 citation:

Corresponding BibTeX entry:

@Article{,
  title = {Describe and understand your model's parameters},
  author = {Daniel Lüdecke and Mattan S. Ben-Shachar and Dominique Makowski},
  journal = {CRAN},
  year = {2019},
  note = {R package},
  url = {https://github.com/easystats/parameters},
}