insight: find_predictors – R documentation

Become an expert in R — Interactive courses, Cheat Sheets, certificates and more!

find_predictors

Find names of model predictors

Description

Returns the names of the predictor variables for the different parts of a model (like fixed or random effects, zero-inflated component, ...). Unlike find_parameters, the names from find_predictors() match the original variable names from the data that was used to fit the model.

Usage

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

Arguments

`x`	A fitted model.
`effects`	Should variables for fixed effects, random effects or both be returned? Only applies to mixed models. May be abbreviated.
`component`	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.
`flatten`	Logical, if `TRUE`, the values are returned as character vector, not as list. Duplicated values are removed.
`verbose`	Toggle warnings.

Value

A list of character vectors that represent the name(s) of the predictor variables. Depending on the combination of the arguments effects and component, the returned list has following elements:

conditional, the "fixed effects" terms from the model
random, the "random effects" terms from the model
zero_inflated, the "fixed effects" terms from the zero-inflation component of the model
zero_inflated_random, the "random effects" terms from the zero-inflation component of the model
dispersion, the dispersion terms
instruments, for fixed-effects regressions like ivreg, felm or plm, the instrumental variables
correlation, for models with correlation-component like gls, the variables used to describe the correlation structure

Examples

data(mtcars)
m <- lm(mpg ~ wt + cyl + vs, data = mtcars)
find_predictors(m)

insight

Easy Access to Model Information for Various Model Objects

v0.14.0

GPL-3

Authors

Daniel Lüdecke [aut, cre] (<https://orcid.org/0000-0002-8895-3206>, @strengejacke), Dominique Makowski [aut, ctb] (<https://orcid.org/0000-0001-5375-9967>, @Dom_Makowski), Indrajeet Patil [aut, ctb] (<https://orcid.org/0000-0003-1995-6531>, @patilindrajeets), Philip Waggoner [aut, ctb] (<https://orcid.org/0000-0002-7825-7573>), Mattan S. Ben-Shachar [aut, ctb] (<https://orcid.org/0000-0002-4287-4801>), Brenton M. Wiernik [aut] (<https://orcid.org/0000-0001-9560-6336>, @bmwiernik)

Initial release