Package index • parameters

Comprehensive Model Parameters

compare_parameters() compare_models(): Compare model parameters of multiple models

dominance_analysis(): Dominance Analysis

model_parameters() parameters(): Model Parameters

pool_parameters(): Pool Model Parameters

random_parameters(): Summary information from random effects

format(<parameters_model>) print(<parameters_model>) summary(<parameters_model>) print_html(<parameters_model>) print_md(<parameters_model>): Print model parameters

sort_parameters(): Sort parameters by coefficient values

standardize_parameters() standardize_posteriors(): Parameters standardization

standardize_info(): Get Standardization Information

Documentation of Specific Class Objects

model_parameters(<aov>): Parameters from ANOVAs

model_parameters(<befa>): Parameters from Bayesian Exploratory Factor Analysis

model_parameters(<default>): Parameters from (General) Linear Models

model_parameters(<zcpglm>): Parameters from Zero-Inflated Models

model_parameters(<cgam>): Parameters from Generalized Additive (Mixed) Models

model_parameters(<mlm>): Parameters from multinomial or cumulative link models

model_parameters(<glmmTMB>): Parameters from Mixed Models

model_parameters(<hclust>): Parameters from Cluster Models (k-means, ...)

model_parameters(<mira>): Parameters from multiply imputed repeated analyses

model_parameters(<lavaan>) model_parameters(<principal>): Parameters from PCA, FA, CFA, SEM

model_parameters(<data.frame>) model_parameters(<brmsfit>): Parameters from Bayesian Models

model_parameters(<BFBayesFactor>): Parameters from BayesFactor objects

model_parameters(<rma>): Parameters from Meta-Analysis

model_parameters(<htest>) model_parameters(<coeftest>): Parameters from hypothesis tests

model_parameters(<glht>): Parameters from Hypothesis Testing

model_parameters(<glimML>): Parameters from special models

model_parameters(<t1way>): Parameters from robust statistical objects in WRS2

Standard Errors, Confidence Intervals, Degrees of Freedom and p-values

standard_error(): Standard Errors

ci(<default>): Confidence Intervals (CI)

p_value(): p-values

degrees_of_freedom() dof(): Degrees of Freedom (DoF)

Approximation Methods

ci_kenward() dof_kenward() p_value_kenward() se_kenward(): Kenward-Roger approximation for SEs, CIs and p-values

ci_satterthwaite() dof_satterthwaite() p_value_satterthwaite() se_satterthwaite(): Satterthwaite approximation for SEs, CIs and p-values

ci_betwithin() dof_betwithin() p_value_betwithin(): Between-within approximation for SEs, CIs and p-values

ci_ml1() dof_ml1() p_value_ml1(): "m-l-1" approximation for SEs, CIs and p-values

Effect Existence and Significance

equivalence_test(<lm>) equivalence_test(<ggeffects>): Equivalence test

p_calibrate(): Calculate calibrated p-values.

p_direction(<lm>): Probability of Direction (pd)

p_function() consonance_function() confidence_curve(): p-value or consonance function

p_significance(<lm>): Practical Significance (ps)

Parameter Sampling

bootstrap_model(): Model bootstrapping

bootstrap_parameters(): Parameters bootstrapping

simulate_model(): Simulated draws from model coefficients

simulate_parameters(): Simulate Model Parameters

Feature Reduction

reduce_parameters() reduce_data(): Dimensionality reduction (DR) / Features Reduction

select_parameters(): Automated selection of model parameters

Data Reduction

Cluster Analysis

cluster_analysis(): Cluster Analysis

cluster_centers(): Find the cluster centers in your data

cluster_discrimination(): Compute a linear discriminant analysis on classified cluster groups

cluster_meta(): Metaclustering

cluster_performance(): Performance of clustering models

n_clusters() n_clusters_elbow() n_clusters_gap() n_clusters_silhouette() n_clusters_dbscan() n_clusters_hclust(): Find number of clusters in your data

predict(<parameters_clusters>): Predict method for parameters_clusters objects

Factors and Principal Components

convert_efa_to_cfa() efa_to_cfa(): Conversion between EFA results and CFA structure

factor_analysis() principal_components() rotated_data() predict(<parameters_efa>) print(<parameters_efa>) sort(<parameters_efa>) closest_component(): Principal Component Analysis (PCA) and Factor Analysis (FA)

get_scores(): Get Scores from Principal Component Analysis (PCA)

n_factors() n_components(): Number of components/factors to retain in PCA/FA

reduce_parameters() reduce_data(): Dimensionality reduction (DR) / Features Reduction

reshape_loadings(): Reshape loadings between wide/long formats

Table and Value Formatting

display(<parameters_model>) display(<parameters_sem>) display(<parameters_efa_summary>) display(<parameters_efa>) display(<equivalence_test_lm>) print_table(): Print tables in different output formats

format_order(): Order (first, second, ...) formatting

format_parameters(): Parameter names formatting

format_p_adjust(): Format the name of the p-value adjustment methods

format_df_adjust(): Format the name of the degrees-of-freedom adjustment methods

format(<compare_parameters>) print(<compare_parameters>) print_html(<compare_parameters>) print_md(<compare_parameters>): Print comparisons of model parameters

parameters_type(): Type of model parameters

reexports equivalence_test ci n_parameters p_direction p_significance standardize_names supported_models print_html print_md display describe_distribution demean rescale_weights visualisation_recipe kurtosis skewness: Objects exported from other packages

Functions exported from other packages

reexports equivalence_test ci n_parameters p_direction p_significance standardize_names supported_models print_html print_md display describe_distribution demean rescale_weights visualisation_recipe kurtosis skewness: Objects exported from other packages

Example Data Sets

qol_cancer: Sample data set

fish: Sample data set