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Find all model terms

Source: R/find_terms.R
find_terms.Rd

Returns a list with the names of all terms, including response value and random effects, "as is". This means, on-the-fly tranformations or arithmetic expressions like log(), I(), as.factor() etc. are preserved.

Usage

find_terms(x, ...)

# Default S3 method
find_terms(x, flatten = FALSE, as_term_labels = FALSE, verbose = TRUE, ...)

Arguments

x

A fitted model.

...

Currently not used.

flatten

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

as_term_labels

Logical, if TRUE, extracts model formula and tries to access the "term.labels" attribute. This should better mimic the terms() behaviour even for those models that do not have such a method, but may be insufficient, e.g. for mixed models.

verbose

Toggle warnings.

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

Returns NULL if no terms could be found (for instance, due to problems in accessing the formula).

Note

The difference to find_variables() is that find_terms() may return a variable multiple times in case of multiple transformations (see examples below), while find_variables() returns each variable name only once.

Examples

data(sleepstudy, package = "lme4")
m <- suppressWarnings(lme4::lmer(
  log(Reaction) ~ Days + I(Days^2) + (1 + Days + exp(Days) | Subject),
  data = sleepstudy
))

find_terms(m)
#> $response
#> [1] "log(Reaction)"
#> 
#> $conditional
#> [1] "Days"      "I(Days^2)"
#> 
#> $random
#> [1] "Days"      "exp(Days)" "Subject"  
#> 

# sometimes, it is necessary to retrieve terms from "term.labels" attribute
m <- lm(mpg ~ hp * (am + cyl), data = mtcars)
find_terms(m, as_term_labels = TRUE)
#> $conditional
#> [1] "hp"     "am"     "cyl"    "hp:am"  "hp:cyl"
#>