model_info: Access information from model objects

In insight: Easy Access to Model Information for Various Model Objects

Access information from model objects

Description

Retrieve information from model objects.

Usage

model_info(x, ...)

## Default S3 method:
model_info(x, verbose = TRUE, ...)

## S3 method for class 'brmsfit'
model_info(x, response = NULL, ...)

Arguments

x	A fitted model.
...	Currently not used.
verbose	Toggle off warnings.
response	If x is a multivariate response model, model_info() returns a list of information for each response variable. Set response to the number of a specific response variable, or provide the name of the response variable in response, to return the information for only one response.

Details

model_info() returns a list with information about the model for many different model objects. Following information is returned, where all values starting with is_ are logicals.

Common families and distributions:

• is_bernoulli: special case of binomial models: family is Bernoulli

• is_beta: family is beta

• is_betabinomial: family is beta-binomial

• is_binomial: family is binomial (but not negative binomial)

• is_categorical: family is categorical link

• is_censored: model is a censored model (has a censored response, including survival models)

• is_count: model is a count model (i.e. family is either poisson or negative binomial)

• is_cumulative: family is ordinal or cumulative link

• is_dirichlet: family is dirichlet

• is_exponential: family is exponential (e.g. Gamma or Weibull)

• is_linear: family is gaussian

• is_logit: model has logit link

• is_multinomial: family is multinomial or categorical link

• is_negbin: family is negative binomial

• is_orderedbeta: family is ordered beta

• is_ordinal: family is ordinal or cumulative link

• is_poisson: family is poisson

• is_probit: model has probit link

• is_tweedie: family is tweedie

Special model types:

• is_anova: model is an Anova object

• is_bayesian: model is a Bayesian model

• is_dispersion: model has dispersion component (not only dispersion parameter)

• is_gam: model is a generalized additive model

• is_meta: model is a meta-analysis object

• is_mixed: model is a mixed effects model (with random effects)

• is_mixture: model is a finite mixture model (currently only recognized for package brms).

• is_multivariate: model is a multivariate response model (currently only works for brmsfit and vglm/vgam objects)

• is_hurdle: model has zero-inflation component and is a hurdle-model (truncated family distribution)

• is_rtchoice: model is a brms decision-making (sequential sampling) model, which models outcomes that consists of two components (reaction times and choice).

• is_survival: model is a survival model

• is_trial: model response contains additional information about the trials

• is_truncated: model is a truncated model (has a truncated response)

• is_wiener: model is a brms decision-making (sequential sampling) model with Wiener process (also called drift diffusion model)

• is_zero_inflated: model has zero-inflation component

Hypotheses tests:

• is_binomtest: model is an an object of class htest, returned by binom.test()

• is_chi2test: model is an an object of class htest, returned by chisq.test()

• is_correlation: model is an an object of class htest, returned by cor.test()

• is_ftest: model is an an object of class htest, and test-statistic is an F-statistic.

• is_levenetest: model is an an object of class anova, returned by car::leveneTest().

• is_onewaytest: model is an an object of class htest, returned by oneway.test()

• is_proptest: model is an an object of class htest, returned by prop.test()

• is_ranktest: model is an an object of class htest, returned by cor.test() (if Spearman's rank correlation), wilcox.text() or kruskal.test().

• is_ttest: model is an an object of class htest, returned by t.test()

• is_variancetest: model is an an object of class htest, returned by bartlett.test(), shapiro.test() or car::leveneTest().

• is_xtab: model is an an object of class htest or BFBayesFactor, and test-statistic stems from a contingency table (i.e. chisq.test() or BayesFactor::contingencyTableBF()).

Other model information:

• link_function: the link-function

• family: name of the distributional family of the model. For some exceptions (like some htest objects), can also be the name of the test.

• n_obs: number of observations

• n_grouplevels: for mixed models, returns names and numbers of random effect groups

Value

A list with information about the model, like family, link-function etc. (see 'Details').

Examples

ldose <- rep(0:5, 2)
numdead <- c(1, 4, 9, 13, 18, 20, 0, 2, 6, 10, 12, 16)
sex <- factor(rep(c("M", "F"), c(6, 6)))
SF <- cbind(numdead, numalive = 20 - numdead)
dat <- data.frame(ldose, sex, SF, stringsAsFactors = FALSE)
m <- glm(SF ~ sex * ldose, family = binomial)

# logistic regression
model_info(m)

# t-test
m <- t.test(1:10, y = c(7:20))
model_info(m)

insight documentation built on Aug. 19, 2025, 1:12 a.m.