model_parameters.glimML {parameters} | R Documentation |
Parameters from special models
Description
Parameters from special regression models not listed under one of the previous categories yet.
Usage
## S3 method for class 'glimML'
model_parameters(
model,
ci = 0.95,
bootstrap = FALSE,
iterations = 1000,
component = "conditional",
standardize = NULL,
exponentiate = FALSE,
p_adjust = NULL,
include_info = getOption("parameters_info", FALSE),
keep = NULL,
drop = NULL,
verbose = TRUE,
...
)
Arguments
model |
Model object. |
ci |
Confidence Interval (CI) level. Default to |
bootstrap |
Should estimates be based on bootstrapped model? If |
iterations |
The number of bootstrap replicates. This only apply in the case of bootstrapped frequentist models. |
component |
Model component for which parameters should be shown. May be
one of |
standardize |
The method used for standardizing the parameters. Can be
|
exponentiate |
Logical, indicating whether or not to exponentiate the
coefficients (and related confidence intervals). This is typical for
logistic regression, or more generally speaking, for models with log or
logit links. It is also recommended to use |
p_adjust |
String value, if not |
include_info |
Logical, if |
keep |
Character containing a regular expression pattern that
describes the parameters that should be included (for |
drop |
See |
verbose |
Toggle warnings and messages. |
... |
Arguments passed to or from other methods. For instance, when
Further non-documented arguments are:
|
Value
A data frame of indices related to the model's parameters.
Model components
Possible values for the component
argument depend on the model class.
Following are valid options:
-
"all"
: returns all model components, applies to all models, but will only have an effect for models with more than just the conditional model component. -
"conditional"
: only returns the conditional component, i.e. "fixed effects" terms from the model. Will only have an effect for models with more than just the conditional model component. -
"smooth_terms"
: returns smooth terms, only applies to GAMs (or similar models that may contain smooth terms). -
"zero_inflated"
(or"zi"
): returns the zero-inflation component. -
"dispersion"
: returns the dispersion model component. This is common for models with zero-inflation or that can model the dispersion parameter. -
"instruments"
: for instrumental-variable or some fixed effects regression, returns the instruments. -
"nonlinear"
: for non-linear models (like models of classnlmerMod
ornls
), returns staring estimates for the nonlinear parameters. -
"correlation"
: for models with correlation-component, likegls
, the variables used to describe the correlation structure are returned.
Special models
Some model classes also allow rather uncommon options. These are:
-
mhurdle:
"infrequent_purchase"
,"ip"
, and"auxiliary"
-
BGGM:
"correlation"
and"intercept"
-
BFBayesFactor, glmx:
"extra"
-
averaging:
"conditional"
and"full"
-
mjoint:
"survival"
-
mfx:
"precision"
,"marginal"
-
betareg, DirichletRegModel:
"precision"
-
mvord:
"thresholds"
and"correlation"
-
clm2:
"scale"
-
selection:
"selection"
,"outcome"
, and"auxiliary"
-
lavaan: One or more of
"regression"
,"correlation"
,"loading"
,"variance"
,"defined"
, or"mean"
. Can also be"all"
to include all components.
For models of class brmsfit
(package brms), even more options are
possible for the component
argument, which are not all documented in detail
here.
See Also
insight::standardize_names()
to rename columns into a consistent,
standardized naming scheme.
Examples
library(parameters)
if (require("brglm2", quietly = TRUE)) {
data("stemcell")
model <- bracl(
research ~ as.numeric(religion) + gender,
weights = frequency,
data = stemcell,
type = "ML"
)
model_parameters(model)
}