Returns the formula(s) for the different parts of a model
(like fixed or random effects, zero-inflated component, ...).
formula_ok()
checks if a model formula has valid syntax
regarding writing TRUE
instead of T
inside poly()
and that no data names are used (i.e. no data$variable
, but rather
variable
).
find_formula(x, ...)formula_ok(x, verbose = TRUE, ...)
# S3 method for default
find_formula(x, verbose = TRUE, ...)
# S3 method for nestedLogit
find_formula(x, dichotomies = FALSE, verbose = TRUE, ...)
A list of formulas that describe the model. For simple models,
only one list-element, conditional
, is returned. For more complex
models, the returned list may have following elements:
conditional
, the "fixed effects" part from the model (in the
context of fixed-effects or instrumental variable regression, also
called regressors) . One exception are DirichletRegModel
models
from DirichletReg, which has two or three components,
depending on model
.
random
, the "random effects" part from the model (or the
id
for gee-models and similar)
zero_inflated
, the "fixed effects" part from the
zero-inflation component of the model
zero_inflated_random
, the "random effects" part from the
zero-inflation component of the model
dispersion
, the dispersion formula
instruments
, for fixed-effects or instrumental variable
regressions like ivreg::ivreg()
, lfe::felm()
or plm::plm()
,
the instrumental variables
cluster
, for fixed-effects regressions like
lfe::felm()
, the cluster specification
correlation
, for models with correlation-component like
nlme::gls()
, the formula that describes the correlation structure
scale
, for distributional models such as mgcv::gaulss()
family fitted
with mgcv::gam()
, the formula that describes the scale parameter
slopes
, for fixed-effects individual-slope models like
feisr::feis()
, the formula for the slope parameters
precision
, for DirichletRegModel
models from
DirichletReg, when parametrization (i.e. model
) is
"alternative"
.
A fitted model.
Currently not used.
Toggle warnings.
Logical, if model is a nestedLogit
objects, returns
the formulas for the dichotomies.
data(mtcars)
m <- lm(mpg ~ wt + cyl + vs, data = mtcars)
find_formula(m)
m <- lme4::lmer(Sepal.Length ~ Sepal.Width + (1 | Species), data = iris)
f <- find_formula(m)
f
format(f)
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