Package index • insight

Comprehensive Information from Model Objects

model_info(): Access information from model objects

Find Information from Model Objects

Functions to access names or information from model objects. These functions have no get_*()-counterpart.

find_algorithm(): Find sampling algorithm and optimizers

find_formula() formula_ok(): Find model formula

find_interactions(): Find interaction terms from models

find_offset(): Find possible offset terms in a model

find_random_slopes(): Find names of random slopes

find_smooth(): Find smooth terms from a model object

find_terms(): Find all model terms

find_variables(): Find names of all variables

Get Data from Model Objects

Functions to get values or data from model objects. These functions have no find_*()-counterpart.

get_auxiliary() get_dispersion(): Get auxiliary parameters from models

get_data(): Get the data that was used to fit the model

get_datagrid(): Create a reference grid

get_df(): Extract degrees of freedom

get_intercept(): Get the value at the intercept

get_loglikelihood() loglikelihood() get_loglikelihood_adjustment(): Log-Likelihood and Log-Likelihood correction

get_modelmatrix(): Model Matrix

get_predicted(): Model predictions (robust) and their confidence intervals

get_predicted_ci(): Confidence intervals around predicted values

get_priors(): Get summary of priors used for a model

get_residuals(): Extract model residuals

get_sigma(): Get residual standard deviation from models

get_variance() get_variance_residual() get_variance_fixed() get_variance_random() get_variance_distribution() get_variance_dispersion() get_variance_intercept() get_variance_slope() get_correlation_slope_intercept() get_correlation_slopes(): Get variance components from random effects models

get_varcov(): Get variance-covariance matrix from models

get_deviance(): Model Deviance

get_family(): A robust alternative to stats::family

Find Information or Get Data from Model Objects

Functions to list model-specific objects or to extract values (or data) associated with model-specific objects.

find_parameters(): Find names of model parameters

get_parameters(): Get model parameters

find_predictors(): Find names of model predictors

get_predictors(): Get the data from model predictors

find_random(): Find names of random effects

get_random(): Get the data from random effects

find_response(): Find name of the response variable

get_response(): Get the values from the response variable

find_statistic(): Find statistic for model

get_statistic(): Get statistic associated with estimates

find_transformation(): Find possible transformation of model variables

get_transformation(): Return function of transformed response variables

find_weights(): Find names of model weights

get_weights(): Get the values from model weights

Access Further Information from Model Objects

Functions to access specific information from model objects.

get_call(): Get the model's function call

link_function(): Get link-function from model object

link_inverse(): Get link-inverse function from model object

model_name(): Name the model

n_grouplevels(): Count number of random effect levels in a mixed model

n_obs(): Get number of observations from a model

n_parameters(): Count number of parameters in a model

trim_ws() n_unique() safe_deparse() safe_deparse_symbol() has_single_value(): Small helper functions

Test Model Properties

Functions to test specific properties of model objects.

all_models_equal() all_models_same_class(): Checks if all objects are models of same class

has_intercept(): Checks if model has an intercept

is_converged(): Convergence test for mixed effects models

is_empty_object(): Check if object is empty

is_gam_model(): Checks if a model is a generalized additive model

is_mixed_model(): Checks if a model is a mixed effects model

is_model() is_regression_model(): Checks if an object is a regression model or statistical test object

is_model_supported() supported_models(): Checks if a regression model object is supported by the insight package

is_multivariate(): Checks if an object stems from a multivariate response model

is_nested_models(): Checks whether a list of models are nested models

is_nullmodel(): Checks if model is a null-model (intercept-only)

Value Formatting

Functions for formatting (summary) output for printing

color_if() colour_if(): Color-formatting for data columns based on condition

print_color() print_colour() color_text() colour_text() color_theme(): Coloured console output

format_bf(): Bayes Factor formatting

format_capitalize(): Capitalizes the first letter in a string

format_ci(): Confidence/Credible Interval (CI) Formatting

format_message() format_alert() format_warning() format_error(): Format messages and warnings

format_number(): Convert number to words

format_p(): p-values formatting

format_pd(): Probability of direction (pd) formatting

format_rope(): Percentage in ROPE formatting

format_string(): String Values Formatting

format_table(): Parameter table formatting

format_value() format_percent(): Numeric Values Formatting

text_remove_backticks(): Remove backticks from a string

Utilities

Functions that are not specifically related to access model information, but rather small utility functions (mostly for printing)

clean_names(): Get clean names of model terms

clean_parameters(): Get clean names of model parameters

display() print_md() print_html(): Generic export of data frames into formatted tables

download_model(): Download circus models

ellipsis_info(): Gather information about objects in ellipsis (dot dot dot)

apply_table_theme() export_table(): Data frame and Tables Pretty Formatting

null_model(): Compute intercept-only model for regression models

print_parameters(): Prepare summary statistics of model parameters for printing

standardize_column_order(): Standardize column order

standardize_names(): Standardize column names

is_empty_object(): Check if object is empty

object_has_names() object_has_rownames(): Check names and rownames

compact_character(): Remove empty strings from character

compact_list(): Remove empty elements from lists

check_if_installed(): Checking if needed package is installed

validate_argument(): Validate arguments against a given set of options

trim_ws() n_unique() safe_deparse() safe_deparse_symbol() has_single_value(): Small helper functions

Example Datasets

Used in examples or for testing

fish: Sample data set

Documentation of Specific Class Objects

find_parameters(<BGGM>) find_parameters(<brmsfit>): Find names of model parameters from Bayesian models

find_parameters(<emmGrid>): Find model parameters from estimated marginal means objects

find_parameters(<gamlss>) find_parameters(<gam>): Find names of model parameters from generalized additive models

find_parameters(<betamfx>): Find names of model parameters from marginal effects models

find_parameters(<glmmTMB>): Find names of model parameters from mixed models

find_parameters(<zeroinfl>): Find names of model parameters from zero-inflated models

find_parameters(<averaging>): Find model parameters from models with special components

get_datagrid(<emmGrid>): Extract a reference grid from objects created by emmeans and marginaleffects

get_parameters(<BGGM>) get_parameters(<BFBayesFactor>) get_parameters(<brmsfit>): Get model parameters from Bayesian models

get_parameters(<emmGrid>): Get model parameters from estimated marginal means objects

get_parameters(<gamm>): Get model parameters from generalized additive models

get_parameters(<betamfx>): Get model parameters from marginal effects models

get_parameters(<glmmTMB>): Get model parameters from mixed models

get_parameters(<zeroinfl>): Get model parameters from zero-inflated and hurdle models

get_parameters(<betareg>): Get model parameters from models with special components

get_parameters(<htest>): Get model parameters from htest-objects