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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).

Installation

Run the following:

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

Documentation

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:

  • 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 |  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 | 140.99 | < .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 |  Min |  Max | Skewness | Kurtosis |   n | n_Missing
#> --------------------------------------------------------------------------------
#> Sepal.Length | 5.84 | 0.83 | 4.30 | 7.90 |     0.31 |    -0.55 | 150 |         0
#> Sepal.Width  | 3.06 | 0.44 | 2.00 | 4.40 |     0.32 |     0.23 | 150 |         0
#> Petal.Length | 3.76 | 1.77 | 1.00 | 6.90 |    -0.27 |    -1.40 | 150 |         0
#> Petal.Width  | 1.20 | 0.76 | 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 = {Dominique Makowski and Mattan S. Ben-Shachar and Daniel
  Lüdecke},
  journal = {CRAN},
  year = {2019},
  note = {R package},
  url = {https://github.com/easystats/parameters},
}

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Version

Install

install.packages('parameters')

Monthly Downloads

87,418

Version

0.6.0

License

GPL-3

Maintainer

Daniel Lüdecke

Last Published

March 12th, 2020

Functions in parameters (0.6.0)

check_factorstructure

Check suitability of data for Factor Analysis (FA)
check_sphericity

Bartlett's Test of Sphericity
ci.merMod

Confidence Intervals (CI)
check_clusterstructure

Check suitability of data for clustering
bootstrap_model

Model bootstrapping
check_kmo

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

Check if a distribution is unimodal or multimodal
cluster_analysis

Compute cluster analysis and return group indices
cluster_discrimination

Compute a linear discriminant analysis on classified cluster groups
bootstrap_parameters

Parameters bootstrapping
convert_data_to_numeric

Convert data to numeric
describe_distribution

Describe a distribution
.n_factors_sescree

Standard Error Scree and Coefficient of Determination Procedures
demean

Compute group-meaned and de-meaned variables
format_order

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

Non Graphical Cattell's Scree Test
data_partition

Partition data into a test and a training set
.data_frame

help-functions
model_parameters.aov

Parameters from ANOVAs
convert_efa_to_cfa

Conversion between EFA results and CFA structure
.find_most_common

Find most common occurence
format_p

p-values formatting
.flatten_list

Flatten a list
model_parameters.Mclust

Parameters from Mixture Models
model_parameters.BFBayesFactor

Parameters from BayesFactor objects
.factor_to_dummy

Safe transformation from factor/character to numeric
degrees_of_freedom

Degrees of Freedom (DoF)
.compact_character

remove empty string from character
.recode_to_zero

Recode a variable so its lowest value is beginning with zero
format_model

Model Name formatting
.factor_to_numeric

Safe transformation from factor/character to numeric
factor_analysis

Factor Analysis (FA)
format_parameters

Parameter names formatting
reduce_parameters

Dimensionality reduction (DR) / Features Reduction
format_pd

Probability of direction (pd) formatting
.filter_component

for models with zero-inflation component, return required component of model-summary
equivalence_test.lm

Equivalence test
smoothness

Quantify the smoothness of a vector
.n_factors_cng

Cattell-Nelson-Gorsuch CNG Indices
model_parameters.stanreg

Parameters from Bayesian Models
.n_factors_bartlett

Bartlett, Anderson and Lawley Procedures
.n_factors_mreg

Multiple Regression Procedure
.n_factors_bentler

Bentler and Yuan's Procedure
format_rope

Percentage in ROPE formatting
fish

Sample data set
ci_kenward

Kenward-Roger approximation for SEs, CIs and p-values
model_parameters.zeroinfl

Parameters from Zero-Inflated Models
.compact_list

remove NULL elements from lists
model_parameters.merMod

Parameters from Mixed Models
model_parameters

Model Parameters
model_parameters.lavaan

Parameters from CFA/SEM models
simulate_model

Simulated draws from model coefficients
model_parameters.kmeans

Parameters from Cluster Models (k-means, ...)
format_number

Convert number to words
model_parameters.PCA

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

Parameters from PCA/FA
format_algorithm

Model Algorithm formatting
format_bf

Bayes Factor formatting
get_scores

Get Scores from Principal Component Analysis (PCA)
model_parameters.rma

Parameters from Meta-Analysis
standardize_names

Standardize column names
parameters_type

Type of model parameters
model_parameters.default

Parameters from (General) Linear Models
model_parameters.gam

Parameters from Generalized Additive (Mixed) Models
ci_ml1

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

Satterthwaite approximation for SEs, CIs and p-values
n_clusters

Number of clusters to extract
model_parameters.htest

Parameters from Correlations and t-tests
p_value

p-values
n_parameters

Count number of parameters in a model
rescale_weights

Rescale design weights for multilevel analysis
model_parameters.mlm

Parameters from multinomial or cumulative link models
ci_betwithin

Between-within approximation for SEs, CIs and p-values
print

Print model parameters
n_factors

Number of components/factors to retain in PCA/FA
random_parameters

Summary information from random effects
reexports

Objects exported from other packages
principal_components

Principal Component Analysis (PCA)
standard_error_robust

Robust estimation
parameters_table

Parameter table formatting
ci_wald

Wald-test approximation for CIs and p-values
standard_error

Standard Errors
reshape_loadings

Reshape loadings between wide/long formats
select_parameters

Automated selection of model parameters
skewness

Compute Skewness and Kurtosis
simulate_parameters

Simulate Model Parameters