find_predictors: Find names of model predictors

In insight: Easy Access to Model Information for Various Model Objects

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, ...)

## Default S3 method:
find_predictors(
  x,
  effects = c("fixed", "random", "all"),
  component = c("all", "conditional", "zi", "zero_inflated", "dispersion", "instruments",
    "correlation", "smooth_terms"),
  flatten = FALSE,
  verbose = TRUE,
  ...
)

## S3 method for class 'afex_aov'
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.
...	Currently not used.
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

Model components

Possible values for the component argument depend on the model class. Following are valid options:

• "all": returns all model components, applies to all models, but will only have an effect for models with more than just the conditional model component.

• "conditional": only returns the conditional component, i.e. "fixed effects" terms from the model. Will only have an effect for models with more than just the conditional model component.

• "smooth_terms": returns smooth terms, only applies to GAMs (or similar models that may contain smooth terms).

• "zero_inflated" (or "zi"): returns the zero-inflation component.

• "dispersion": returns the dispersion model component. This is common for models with zero-inflation or that can model the dispersion parameter.

• "instruments": for instrumental-variable or some fixed effects regression, returns the instruments.

• "location": returns location parameters such as conditional, zero_inflated, smooth_terms, or instruments (everything that are fixed or random effects - depending on the effects argument - but no auxiliary parameters).

• "distributional" (or "auxiliary"): components like sigma, dispersion, beta or precision (and other auxiliary parameters) are returned.

Examples

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

insight documentation built on Nov. 26, 2023, 5:08 p.m.