
Find names of model parameters from mixed models
Source:R/find_parameters_mixed.R
find_parameters.glmmTMB.Rd
Returns the names of model parameters, like they typically
appear in the summary()
output.
Usage
# 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 hglm
find_parameters(
x,
effects = c("all", "fixed", "random"),
component = c("all", "conditional", "dispersion"),
flatten = FALSE,
...
)
# S3 method for merMod
find_parameters(x, effects = c("all", "fixed", "random"), flatten = FALSE, ...)
Arguments
- x
A fitted model.
- effects
Should parameters for fixed effects, random effects or both be returned? Only applies to mixed models. May be abbreviated.
- component
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. Ifcomponent = "location"
, location parameters such asconditional
orzero_inflated
are returned (everything that are fixed or random effects - depending on theeffects
argument - but no auxiliary parameters). Forcomponent = "distributional"
(or"auxiliary"
), components likesigma
ordispersion
(and other auxiliary parameters) are returned.- flatten
Logical, if
TRUE
, the values are returned as character vector, not as list. Duplicated values are removed.- ...
Currently not used.
Value
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.
Examples
data(mtcars)
m <- lm(mpg ~ wt + cyl + vs, data = mtcars)
find_parameters(m)
#> $conditional
#> [1] "(Intercept)" "wt" "cyl" "vs"
#>