Retrieve information from model objects.

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

  • is_zeroinf: model has zero-inflation component

  • is_zero_inflated: alias for is_zeroinf

  • 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

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)
#> $is_binomial #> [1] TRUE #> #> $is_count #> [1] FALSE #> #> $is_poisson #> [1] FALSE #> #> $is_negbin #> [1] FALSE #> #> $is_beta #> [1] FALSE #> #> $is_betabinomial #> [1] FALSE #> #> $is_exponential #> [1] FALSE #> #> $is_logit #> [1] TRUE #> #> $is_probit #> [1] FALSE #> #> $is_censored #> [1] FALSE #> #> $is_linear #> [1] FALSE #> #> $is_tweedie #> [1] FALSE #> #> $is_zeroinf #> [1] FALSE #> #> $is_zero_inflated #> [1] FALSE #> #> $is_hurdle #> [1] FALSE #> #> $is_ordinal #> [1] FALSE #> #> $is_categorical #> [1] FALSE #> #> $is_mixed #> [1] FALSE #> #> $is_multivariate #> [1] FALSE #> #> $is_trial #> [1] FALSE #> #> $is_bayesian #> [1] FALSE #> #> $is_anova #> [1] FALSE #> #> $is_ttest #> [1] FALSE #> #> $is_correlation #> [1] FALSE #> #> $is_meta #> [1] FALSE #> #> $link_function #> [1] "logit" #> #> $family #> [1] "binomial" #> #> $n_obs #> [1] 12 #> #> $model_terms #> $model_terms$response #> [1] "SF" #> #> $model_terms$conditional #> [1] "sex" "ldose" #> #>
# NOT RUN { library(glmmTMB) data("Salamanders") m <- glmmTMB( count ~ spp + cover + mined + (1 | site), ziformula = ~ spp + mined, dispformula = ~DOY, data = Salamanders, family = nbinom2 ) # }
model_info(m)
#> $is_binomial #> [1] TRUE #> #> $is_count #> [1] FALSE #> #> $is_poisson #> [1] FALSE #> #> $is_negbin #> [1] FALSE #> #> $is_beta #> [1] FALSE #> #> $is_betabinomial #> [1] FALSE #> #> $is_exponential #> [1] FALSE #> #> $is_logit #> [1] TRUE #> #> $is_probit #> [1] FALSE #> #> $is_censored #> [1] FALSE #> #> $is_linear #> [1] FALSE #> #> $is_tweedie #> [1] FALSE #> #> $is_zeroinf #> [1] FALSE #> #> $is_zero_inflated #> [1] FALSE #> #> $is_hurdle #> [1] FALSE #> #> $is_ordinal #> [1] FALSE #> #> $is_categorical #> [1] FALSE #> #> $is_mixed #> [1] FALSE #> #> $is_multivariate #> [1] FALSE #> #> $is_trial #> [1] FALSE #> #> $is_bayesian #> [1] FALSE #> #> $is_anova #> [1] FALSE #> #> $is_ttest #> [1] FALSE #> #> $is_correlation #> [1] FALSE #> #> $is_meta #> [1] FALSE #> #> $link_function #> [1] "logit" #> #> $family #> [1] "binomial" #> #> $n_obs #> [1] 12 #> #> $model_terms #> $model_terms$response #> [1] "SF" #> #> $model_terms$conditional #> [1] "sex" "ldose" #> #>