Get clean names of model terms

This function "cleans" names of model terms (or a character vector with such names) by removing patterns like log() or as.factor() etc.

Usage

clean_names(x, ...)

# S3 method for character
clean_names(x, include_names = FALSE, ...)

Arguments

x

A fitted model, or a character vector.

...

Currently not used.

include_names

Logical, if TRUE, returns a named vector where names are the original values of x.

Value

The "cleaned" variable names as character vector, i.e. pattern like s() for splines or log() are removed from the model terms.

Note

Typically, this method is intended to work on character vectors, in order to remove patterns that obscure the variable names. For convenience reasons it is also possible to call clean_names() also on a model object. If x is a regression model, this function is (almost) equal to calling find_variables(). The main difference is that clean_names() always returns a character vector, while find_variables() returns a list of character vectors, unless flatten = TRUE. See 'Examples'.

Examples

# example from ?stats::glm
counts <- c(18, 17, 15, 20, 10, 20, 25, 13, 12)
outcome <- as.numeric(gl(3, 1, 9))
treatment <- gl(3, 3)
m <- glm(counts ~ log(outcome) + as.factor(treatment), family = poisson())
clean_names(m)
#> [1] "counts"    "outcome"   "treatment"

# difference "clean_names()" and "find_variables()"
data(cbpp, package = "lme4")
m <- lme4::glmer(
  cbind(incidence, size - incidence) ~ period + (1 | herd),
  data = cbpp,
  family = binomial
)

clean_names(m)
#> [1] "incidence" "size"      "period"    "herd"     
find_variables(m)
#> $response
#> [1] "incidence" "size"     
#> 
#> $conditional
#> [1] "period"
#> 
#> $random
#> [1] "herd"
#> 
find_variables(m, flatten = TRUE)
#> [1] "incidence" "size"      "period"    "herd"