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

find_variables(x, effects = c("all", "fixed", "random"),
component = c("all", "conditional", "zi", "zero_inflated",
"dispersion", "instruments"), flatten = FALSE)

## Arguments

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

## Value

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)

• 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)

• dispersion, the name of the dispersion terms

• instruments, the names of instrumental variables

## Note

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.

## Examples

library(lme4)
data(cbpp)
data(sleepstudy)
# 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$mysubgrp[filter_group] <- sample(1:30, size = sum(filter_group), replace = TRUE) } m1 <- glmer( cbind(incidence, size - incidence) ~ period + (1 | herd), data = cbpp, family = binomial ) find_variables(m1)#>$response
#> [1] "incidence" "size"
#>
#> $conditional #> [1] "period" #> #>$random
#> [1] "herd"
#>
m2 <- lmer(
Reaction ~ Days + (1 | mygrp / mysubgrp) + (1 | Subject),
data = sleepstudy
)#> boundary (singular) fit: see ?isSingularfind_variables(m2)#> $response #> [1] "Reaction" #> #>$conditional
#> [1] "Days"
#>
#> \$random
#> [1] "mysubgrp" "mygrp"    "Subject"
#> find_variables(m2, flatten = TRUE)#> [1] "Reaction" "Days"     "mysubgrp" "mygrp"    "Subject"