Find names of all variables — find

Returns a list with the names of all variables, including response value and random effects.

Usage

find_variables(
  x,
  effects = "all",
  component = "all",
  flatten = FALSE,
  verbose = TRUE
)

Arguments

x

A fitted model.

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 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)
cluster, the names of cluster or grouping variables
dispersion, the name of the dispersion terms
instruments, the names of instrumental variables
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)

Note

The difference to find_terms() is that find_variables() returns each variable name only once, while find_terms() may return a variable multiple times in case of transformations or when arithmetic expressions were used in the formula.

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(cbpp, package = "lme4")
data(sleepstudy, package = "lme4")
# some data preparation...
cbpp$trials <- cbpp$size - cbpp$incidence
sleepstudy$mygrp <- sample(1:5, size = 180, replace = TRUE)
sleepstudy$mysubgrp <- NA
for (i in 1:5) {
  filter_group <- sleepstudy$mygrp == i
  sleepstudy$mysubgrp[filter_group] <-
    sample(1:30, size = sum(filter_group), replace = TRUE)
}

m1 <- lme4::glmer(
  cbind(incidence, size - incidence) ~ period + (1 | herd),
  data = cbpp,
  family = binomial
)
find_variables(m1)
#> $response
#> [1] "incidence" "size"     
#> 
#> $conditional
#> [1] "period"
#> 
#> $random
#> [1] "herd"
#> 

m2 <- lme4::lmer(
  Reaction ~ Days + (1 | mygrp / mysubgrp) + (1 | Subject),
  data = sleepstudy
)
#> boundary (singular) fit: see help('isSingular')
find_variables(m2)
#> $response
#> [1] "Reaction"
#> 
#> $conditional
#> [1] "Days"
#> 
#> $random
#> [1] "mysubgrp" "mygrp"    "Subject" 
#> 
find_variables(m2, flatten = TRUE)
#> [1] "Reaction" "Days"     "mysubgrp" "mygrp"    "Subject"