Returns the names of model parameters, like they typically
appear in the summary()
output.
# S3 method for averaging
find_parameters(x, component = c("conditional", "full"), flatten = FALSE, ...)# S3 method for betareg
find_parameters(
x,
component = c("all", "conditional", "precision", "location", "distributional",
"auxiliary"),
flatten = FALSE,
...
)
# S3 method for DirichletRegModel
find_parameters(
x,
component = c("all", "conditional", "precision", "location", "distributional",
"auxiliary"),
flatten = FALSE,
...
)
# S3 method for mjoint
find_parameters(
x,
component = c("all", "conditional", "survival"),
flatten = FALSE,
...
)
# S3 method for glmx
find_parameters(
x,
component = c("all", "conditional", "extra"),
flatten = FALSE,
...
)
A list of parameter names. The returned list may have following elements:
conditional
, the "fixed effects" part from the model.
full
, parameters from the full model.
A fitted model.
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. May be abbreviated. Note that the
conditional component is also called count or mean
component, depending on the model. There are three convenient shortcuts:
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.
Logical, if TRUE
, the values are returned
as character vector, not as list. Duplicated values are removed.
Currently not used.
data(mtcars)
m <- lm(mpg ~ wt + cyl + vs, data = mtcars)
find_parameters(m)
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