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Find names of model predictors

Source: R/find_predictors.R
find_predictors.Rd

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 = "fixed",
  component = "all",
  flatten = FALSE,
  verbose = TRUE,
  ...
)

Arguments

x

A fitted model.

...

Currently not used.

effects

Should variables for fixed effects ("fixed"), random effects ("random") or both ("all") be returned? Only applies to mixed models. May be abbreviated.

component

Which type of parameters to return, such as parameters for the conditional model, the zero-inflated part of the model, the dispersion term, the instrumental variables or marginal effects be returned? Applies to models with zero-inflated and/or dispersion formula, or to models with instrumental variables (so called fixed-effects regressions), or models with marginal effects (from mfx). See details in section Model Components .May be abbreviated. Note that the conditional component also refers to the count or mean component - names may differ, depending on the modeling package. There are three convenient shortcuts (not applicable to all model classes):

  • component = "all" returns all possible parameters.

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

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

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 can have 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

  • nonlinear, for non-linear models (like models of class nlmerMod or nls), the staring estimates for the nonlinear parameters

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

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.

  • "nonlinear": for non-linear models (like models of class nlmerMod or nls), returns staring estimates for the nonlinear parameters.

  • "correlation": for models with correlation-component, like gls, the variables used to describe the correlation structure are returned.

  • "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.

Special models

Some model classes also allow rather uncommon options. These are:

  • mhurdle: "infrequent_purchase", "ip", and "auxiliary"

  • BGGM: "correlation" and "intercept"

  • BFBayesFactor, glmx: "extra"

  • averaging:"conditional" and "full"

  • mjoint: "survival"

  • mfx: "precision", "marginal"

  • betareg, DirichletRegModel: "precision"

  • mvord: "thresholds" and "correlation"

  • clm2: "scale"

  • selection: "selection", "outcome", and "auxiliary"

For models of class brmsfit (package brms), even more options are possible for the component argument, which are not all documented in detail here.

Parameters, Variables, Predictors and Terms

There are four functions that return information about the variables in a model: find_predictors(), find_variables(), find_terms() and find_parameters(). There are some differences between those functions, which are explained using following model. Note that some, but not all of those functions return information about the dependent and independent variables. In this example, we only show the differences for the independent variables.

model <- lm(mpg ~ factor(gear), data = mtcars)

  • find_terms(model) returns the model terms, i.e. how the variables were used in the model, e.g. applying transformations like factor(), poly() etc. find_terms() may return a variable name multiple times in case of multiple transformations. The return value would be "factor(gear)".

  • find_parameters(model) returns the names of the model parameters (coefficients). The return value would be "(Intercept)", "factor(gear)4" and "factor(gear)5".

  • find_variables() returns the original variable names. find_variables() returns each variable name only once. The return value would be "gear".

  • find_predictors() is comparable to find_variables() and also returns the original variable names, but excluded the dependent (response) variables. The return value would be "gear".

Examples

data(mtcars)
m <- lm(mpg ~ wt + cyl + vs, data = mtcars)
find_predictors(m)
#> $conditional
#> [1] "wt"  "cyl" "vs" 
#>