Returns a list with the names of all variables, including response value and random effects.

  effects = c("all", "fixed", "random"),
  component = c("all", "conditional", "zi", "zero_inflated", "dispersion",
    "instruments", "smooth_terms"),
  flatten = FALSE,
  verbose = TRUE



A fitted model.


Should variables for fixed effects, random effects or both be returned? Only applies to mixed models. May be abbreviated.


Should all predictor variables, predictor variables for the conditional model, the zero-inflated part of the model, the dispersion term or the instrumental variables be returned? Applies to models with zero-inflated and/or dispersion formula, or to models with instrumental variable (so called fixed-effects regressions). May be abbreviated. Note that the conditional component is also called count or mean component, depending on the model.


Logical, if TRUE, the values are returned as character vector, not as list. Duplicated values are removed.


Toggle warnings.


A list with (depending on the model) following elements (character vectors):

  • response, the name of the response variable

  • conditional, the names of the predictor variables from the conditional model (as opposed to the zero-inflated part of a model)

  • cluster, the names of cluster or grouping variables

  • dispersion, the name of the dispersion terms

  • instruments, the names of instrumental variables

  • random, the names of the random effects (grouping factors)

  • zero_inflated, the names of the predictor variables from the zero-inflated part of the model

  • zero_inflated_random, the names of the random effects (grouping factors)


The difference to find_terms() is that find_variables() returns each variable name only once, while find_terms() may return a variable multiple times in case of transformations or when arithmetic expressions were used in the formula.


if (require("lme4")) {
  # some data preparation...
  cbpp$trials <- cbpp$size - cbpp$incidence
  sleepstudy$mygrp <- sample(1:5, size = 180, replace = TRUE)
  sleepstudy$mysubgrp <- NA
  for (i in 1:5) {
    filter_group <- sleepstudy$mygrp == i
    sleepstudy$mysubgrp[filter_group] <-
      sample(1:30, size = sum(filter_group), replace = TRUE)

  m1 <- glmer(
    cbind(incidence, size - incidence) ~ period + (1 | herd),
    data = cbpp,
    family = binomial

  m2 <- lmer(
    Reaction ~ Days + (1 | mygrp / mysubgrp) + (1 | Subject),
    data = sleepstudy
  find_variables(m2, flatten = TRUE)
#> boundary (singular) fit: see ?isSingular
#> [1] "Reaction" "Days"     "mysubgrp" "mygrp"    "Subject"