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.
find_predictors(x, ...)# S3 method for default
find_predictors(
x,
effects = c("fixed", "random", "all"),
component = c("all", "conditional", "zi", "zero_inflated", "dispersion",
"instruments", "correlation", "smooth_terms"),
flatten = FALSE,
verbose = TRUE,
...
)
A fitted model.
Currently not used.
Should variables for fixed effects, random effects or both be returned? Only applies to mixed models. May be abbreviated.
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.
Logical, if TRUE, the values are returned
as character vector, not as list. Duplicated values are removed.
Toggle warnings.
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
# NOT RUN {
data(mtcars)
m <- lm(mpg ~ wt + cyl + vs, data = mtcars)
find_predictors(m)
# }
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