Find names of all variables
Returns a list with the names of all variables, including response value and random effects.
find_variables(
x,
effects = c("all", "fixed", "random"),
component = c("all", "conditional", "zi", "zero_inflated", "dispersion",
"instruments", "smooth_terms"),
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)
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)
dispersion, the name of the dispersion terms
instruments, the names of instrumental variables
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)
}Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.