Access information from model objects — model

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.

Value

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

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

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)
#> $is_binomial
#> [1] TRUE
#> 
#> $is_bernoulli
#> [1] FALSE
#> 
#> $is_count
#> [1] FALSE
#> 
#> $is_poisson
#> [1] FALSE
#> 
#> $is_negbin
#> [1] FALSE
#> 
#> $is_beta
#> [1] FALSE
#> 
#> $is_betabinomial
#> [1] FALSE
#> 
#> $is_orderedbeta
#> [1] FALSE
#> 
#> $is_dirichlet
#> [1] FALSE
#> 
#> $is_exponential
#> [1] FALSE
#> 
#> $is_logit
#> [1] TRUE
#> 
#> $is_probit
#> [1] FALSE
#> 
#> $is_censored
#> [1] FALSE
#> 
#> $is_truncated
#> [1] FALSE
#> 
#> $is_survival
#> [1] FALSE
#> 
#> $is_linear
#> [1] FALSE
#> 
#> $is_tweedie
#> [1] FALSE
#> 
#> $is_zeroinf
#> [1] FALSE
#> 
#> $is_zero_inflated
#> [1] FALSE
#> 
#> $is_dispersion
#> [1] FALSE
#> 
#> $is_hurdle
#> [1] FALSE
#> 
#> $is_ordinal
#> [1] FALSE
#> 
#> $is_cumulative
#> [1] FALSE
#> 
#> $is_multinomial
#> [1] FALSE
#> 
#> $is_categorical
#> [1] FALSE
#> 
#> $is_mixed
#> [1] FALSE
#> 
#> $is_multivariate
#> [1] FALSE
#> 
#> $is_trial
#> [1] FALSE
#> 
#> $is_bayesian
#> [1] FALSE
#> 
#> $is_gam
#> [1] FALSE
#> 
#> $is_anova
#> [1] FALSE
#> 
#> $is_timeseries
#> [1] FALSE
#> 
#> $is_ttest
#> [1] FALSE
#> 
#> $is_correlation
#> [1] FALSE
#> 
#> $is_onewaytest
#> [1] FALSE
#> 
#> $is_chi2test
#> [1] FALSE
#> 
#> $is_ranktest
#> [1] FALSE
#> 
#> $is_levenetest
#> [1] FALSE
#> 
#> $is_variancetest
#> [1] FALSE
#> 
#> $is_xtab
#> [1] FALSE
#> 
#> $is_proptest
#> [1] FALSE
#> 
#> $is_binomtest
#> [1] FALSE
#> 
#> $is_ftest
#> [1] FALSE
#> 
#> $is_meta
#> [1] FALSE
#> 
#> $is_wiener
#> [1] FALSE
#> 
#> $is_rtchoice
#> [1] FALSE
#> 
#> $is_mixture
#> [1] FALSE
#> 
#> $link_function
#> [1] "logit"
#> 
#> $family
#> [1] "binomial"
#> 
#> $n_obs
#> [1] 12
#> 
#> $n_grouplevels
#> NULL
#> 

# t-test
m <- t.test(1:10, y = c(7:20))
model_info(m)
#> $is_binomial
#> [1] FALSE
#> 
#> $is_bernoulli
#> [1] FALSE
#> 
#> $is_count
#> [1] FALSE
#> 
#> $is_poisson
#> [1] FALSE
#> 
#> $is_negbin
#> [1] FALSE
#> 
#> $is_beta
#> [1] FALSE
#> 
#> $is_betabinomial
#> [1] FALSE
#> 
#> $is_orderedbeta
#> [1] FALSE
#> 
#> $is_dirichlet
#> [1] FALSE
#> 
#> $is_exponential
#> [1] FALSE
#> 
#> $is_logit
#> [1] FALSE
#> 
#> $is_probit
#> [1] FALSE
#> 
#> $is_censored
#> [1] FALSE
#> 
#> $is_truncated
#> [1] FALSE
#> 
#> $is_survival
#> [1] FALSE
#> 
#> $is_linear
#> [1] TRUE
#> 
#> $is_tweedie
#> [1] FALSE
#> 
#> $is_zeroinf
#> [1] FALSE
#> 
#> $is_zero_inflated
#> [1] FALSE
#> 
#> $is_dispersion
#> [1] FALSE
#> 
#> $is_hurdle
#> [1] FALSE
#> 
#> $is_ordinal
#> [1] FALSE
#> 
#> $is_cumulative
#> [1] FALSE
#> 
#> $is_multinomial
#> [1] FALSE
#> 
#> $is_categorical
#> [1] FALSE
#> 
#> $is_mixed
#> [1] FALSE
#> 
#> $is_multivariate
#> [1] FALSE
#> 
#> $is_trial
#> [1] FALSE
#> 
#> $is_bayesian
#> [1] FALSE
#> 
#> $is_gam
#> [1] FALSE
#> 
#> $is_anova
#> [1] FALSE
#> 
#> $is_timeseries
#> [1] FALSE
#> 
#> $is_ttest
#> [1] TRUE
#> 
#> $is_correlation
#> [1] FALSE
#> 
#> $is_onewaytest
#> [1] FALSE
#> 
#> $is_chi2test
#> [1] FALSE
#> 
#> $is_ranktest
#> [1] FALSE
#> 
#> $is_levenetest
#> [1] FALSE
#> 
#> $is_variancetest
#> [1] FALSE
#> 
#> $is_xtab
#> [1] FALSE
#> 
#> $is_proptest
#> [1] FALSE
#> 
#> $is_binomtest
#> [1] FALSE
#> 
#> $is_ftest
#> [1] FALSE
#> 
#> $is_meta
#> [1] FALSE
#> 
#> $is_wiener
#> [1] FALSE
#> 
#> $is_rtchoice
#> [1] FALSE
#> 
#> $is_mixture
#> [1] FALSE
#> 
#> $link_function
#> [1] "identity"
#> 
#> $family
#> [1] "gaussian"
#> 
#> $n_obs
#> NULL
#> 
#> $n_grouplevels
#> NULL
#>