find_variables {insight} | R Documentation |
Returns a list with the names of all variables, including response value and random effects.
find_variables( x, effects = "all", component = "all", flatten = FALSE, verbose = TRUE )
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 |
verbose |
Toggle warnings. |
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)
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.
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.
if (require("lme4")) { data(cbpp) data(sleepstudy) # 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 <- glmer( cbind(incidence, size - incidence) ~ period + (1 | herd), data = cbpp, family = binomial ) find_variables(m1) m2 <- lmer( Reaction ~ Days + (1 | mygrp / mysubgrp) + (1 | Subject), data = sleepstudy ) find_variables(m2) find_variables(m2, flatten = TRUE) }