insight (version 0.6.0)

model_info: Access information from model objects

Description

Retrieve information from model objects.

Usage

model_info(x, ...)

Arguments

x

A fitted model.

...

Currently not used.

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.

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

• is_poisson: family is poisson

• is_negbin: family is negative binomial

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

• is_beta: family is beta

• is_betabinomial: family is beta-binomial

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

• is_logit: model has logit link

• is_progit: model has probit link

• is_linear: family is gaussian

• is_tweedie: family is tweedie

• is_ordinal: family is ordinal or cumulative link

• is_categorical: family is categorical link

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

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

• is_survival: model is a survival model

• is_zero_inflated: model has zero-inflation component

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

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

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

• is_trial: model response contains additional information about the trials

• is_bayesian: model is a Bayesian model

• is_anova: model is an Anova object

• link_function: the link-function

• family: the family-object

• n_obs: number of observations

• model_terms: a list with all model terms, including terms such as random effects or from zero-inflated model parts.

Examples

# NOT RUN {
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)

model_info(m)
# }
# NOT RUN {
library(glmmTMB)
data("Salamanders")
m <- glmmTMB(
  count ~ spp + cover + mined + (1 | site),
  ziformula = ~ spp + mined,
  dispformula = ~DOY,
  data = Salamanders,
  family = nbinom2
)
# }
# NOT RUN {
model_info(m)
# }