Returns the names of model parameters, like they typically
appear in the summary()
output.
# S3 method for glmmTMB
find_parameters(
x,
effects = c("all", "fixed", "random"),
component = c("all", "conditional", "zi", "zero_inflated", "dispersion"),
flatten = FALSE,
...
)# S3 method for nlmerMod
find_parameters(
x,
effects = c("all", "fixed", "random"),
component = c("all", "conditional", "nonlinear"),
flatten = FALSE,
...
)
# S3 method for merMod
find_parameters(x, effects = c("all", "fixed", "random"), flatten = FALSE, ...)
A list of parameter names. The returned list may have following elements:
conditional
, the "fixed effects" part from the model.
random
, the "random effects" part from the model.
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 parameters (auxiliary parameter)
nonlinear
, the parameters from the nonlinear formula.
A fitted model.
Should parameters for fixed effects, random effects or both be returned? Only applies to mixed models. May be abbreviated.
Which type of parameters to return, such as parameters for
the conditional model, the zero-inflated part of the model or the
dispersion term? Applies to models with zero-inflated and/or dispersion
formula. 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
or zero_inflated
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
or dispersion
(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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