# Parameters from special models

Source:`R/methods_aod.R`

, `R/methods_averaging.R`

, `R/methods_betareg.R`

, and 8 more
`model_parameters.averaging.Rd`

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 = c("conditional", "random", "dispersion", "all"),
standardize = NULL,
exponentiate = FALSE,
p_adjust = NULL,
summary = getOption("parameters_summary", FALSE),
keep = NULL,
drop = NULL,
verbose = TRUE,
...
)
# S3 method for class 'averaging'
model_parameters(
model,
ci = 0.95,
component = c("conditional", "full"),
exponentiate = FALSE,
p_adjust = NULL,
summary = getOption("parameters_summary", FALSE),
keep = NULL,
drop = NULL,
verbose = TRUE,
...
)
# S3 method for class 'betareg'
model_parameters(
model,
ci = 0.95,
bootstrap = FALSE,
iterations = 1000,
component = c("conditional", "precision", "all"),
standardize = NULL,
exponentiate = FALSE,
p_adjust = NULL,
summary = getOption("parameters_summary", FALSE),
keep = NULL,
drop = NULL,
verbose = TRUE,
...
)
# S3 method for class 'emm_list'
model_parameters(
model,
ci = 0.95,
exponentiate = FALSE,
p_adjust = NULL,
verbose = TRUE,
...
)
# S3 method for class 'glmx'
model_parameters(
model,
ci = 0.95,
bootstrap = FALSE,
iterations = 1000,
component = c("all", "conditional", "extra"),
standardize = NULL,
exponentiate = FALSE,
p_adjust = NULL,
keep = NULL,
drop = NULL,
verbose = TRUE,
...
)
# S3 method for class 'marginaleffects'
model_parameters(model, ci = 0.95, exponentiate = FALSE, ...)
# S3 method for class 'metaplus'
model_parameters(
model,
ci = 0.95,
bootstrap = FALSE,
iterations = 1000,
standardize = NULL,
exponentiate = FALSE,
include_studies = TRUE,
keep = NULL,
drop = NULL,
verbose = TRUE,
...
)
# S3 method for class 'meta_random'
model_parameters(
model,
ci = 0.95,
ci_method = "eti",
exponentiate = FALSE,
include_studies = TRUE,
verbose = TRUE,
...
)
# S3 method for class 'meta_bma'
model_parameters(
model,
ci = 0.95,
ci_method = "eti",
exponentiate = FALSE,
include_studies = TRUE,
verbose = TRUE,
...
)
# S3 method for class 'betaor'
model_parameters(
model,
ci = 0.95,
bootstrap = FALSE,
iterations = 1000,
component = c("conditional", "precision", "all"),
standardize = NULL,
exponentiate = FALSE,
p_adjust = NULL,
verbose = TRUE,
...
)
# S3 method for class 'betamfx'
model_parameters(
model,
ci = 0.95,
bootstrap = FALSE,
iterations = 1000,
component = c("all", "conditional", "precision", "marginal"),
standardize = NULL,
exponentiate = FALSE,
p_adjust = NULL,
keep = NULL,
drop = NULL,
verbose = TRUE,
...
)
# S3 method for class 'mjoint'
model_parameters(
model,
ci = 0.95,
effects = "fixed",
component = c("all", "conditional", "survival"),
exponentiate = FALSE,
p_adjust = NULL,
keep = NULL,
drop = NULL,
verbose = TRUE,
...
)
# S3 method for class 'mvord'
model_parameters(
model,
ci = 0.95,
component = c("all", "conditional", "thresholds", "correlation"),
standardize = NULL,
exponentiate = FALSE,
p_adjust = NULL,
summary = getOption("parameters_summary", FALSE),
keep = NULL,
drop = NULL,
verbose = TRUE,
...
)
# S3 method for class 'selection'
model_parameters(
model,
ci = 0.95,
component = c("all", "selection", "outcome", "auxiliary"),
bootstrap = FALSE,
iterations = 1000,
standardize = NULL,
exponentiate = FALSE,
p_adjust = NULL,
summary = getOption("parameters_summary", FALSE),
keep = NULL,
drop = NULL,
verbose = TRUE,
...
)
```

## Arguments

- model
Model object.

- ci
Confidence Interval (CI) level. Default to

`0.95`

(`95%`

).- bootstrap
Should estimates be based on bootstrapped model? If

`TRUE`

, then arguments of Bayesian regressions apply (see also`bootstrap_parameters()`

).- 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

`"conditional"`

,`"precision"`

(**betareg**),`"scale"`

(**ordinal**),`"extra"`

(**glmx**),`"marginal"`

(**mfx**),`"conditional"`

or`"full"`

(for`MuMIn::model.avg()`

) or`"all"`

.- standardize
The method used for standardizing the parameters. Can be

`NULL`

(default; no standardization),`"refit"`

(for re-fitting the model on standardized data) or one of`"basic"`

,`"posthoc"`

,`"smart"`

,`"pseudo"`

. See 'Details' in`standardize_parameters()`

.**Importantly**:The

`"refit"`

method does*not*standardize categorical predictors (i.e. factors), which may be a different behaviour compared to other R packages (such as**lm.beta**) or other software packages (like SPSS). to mimic such behaviours, either use`standardize="basic"`

or standardize the data with`datawizard::standardize(force=TRUE)`

*before*fitting the model.For mixed models, when using methods other than

`"refit"`

, only the fixed effects will be standardized.Robust estimation (i.e.,

`vcov`

set to a value other than`NULL`

) of standardized parameters only works when`standardize="refit"`

.

- 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

`exponentiate = TRUE`

for models with log-transformed response values.**Note:**Delta-method standard errors are also computed (by multiplying the standard errors by the transformed coefficients). This is to mimic behaviour of other software packages, such as Stata, but these standard errors poorly estimate uncertainty for the transformed coefficient. The transformed confidence interval more clearly captures this uncertainty. For`compare_parameters()`

,`exponentiate = "nongaussian"`

will only exponentiate coefficients from non-Gaussian families.- p_adjust
Character vector, if not

`NULL`

, indicates the method to adjust p-values. See`stats::p.adjust()`

for details. Further possible adjustment methods are`"tukey"`

,`"scheffe"`

,`"sidak"`

and`"none"`

to explicitly disable adjustment for`emmGrid`

objects (from**emmeans**).- summary
Logical, if

`TRUE`

, prints summary information about the model (model formula, number of observations, residual standard deviation and more).- keep
Character containing a regular expression pattern that describes the parameters that should be included (for

`keep`

) or excluded (for`drop`

) in the returned data frame.`keep`

may also be a named list of regular expressions. All non-matching parameters will be removed from the output. If`keep`

is a character vector, every parameter name in the*"Parameter"*column that matches the regular expression in`keep`

will be selected from the returned data frame (and vice versa, all parameter names matching`drop`

will be excluded). Furthermore, if`keep`

has more than one element, these will be merged with an`OR`

operator into a regular expression pattern like this:`"(one|two|three)"`

. If`keep`

is a named list of regular expression patterns, the names of the list-element should equal the column name where selection should be applied. This is useful for model objects where`model_parameters()`

returns multiple columns with parameter components, like in`model_parameters.lavaan()`

. Note that the regular expression pattern should match the parameter names as they are stored in the returned data frame, which can be different from how they are printed. Inspect the`$Parameter`

column of the parameters table to get the exact parameter names.- drop
See

`keep`

.- verbose
Toggle warnings and messages.

- ...
Arguments passed to or from other methods. For instance, when

`bootstrap = TRUE`

, arguments like`type`

or`parallel`

are passed down to`bootstrap_model()`

.- include_studies
Logical, if

`TRUE`

(default), includes parameters for all studies. Else, only parameters for overall-effects are shown.- ci_method
Method for computing degrees of freedom for confidence intervals (CI) and the related p-values. Allowed are following options (which vary depending on the model class):

`"residual"`

,`"normal"`

,`"likelihood"`

,`"satterthwaite"`

,`"kenward"`

,`"wald"`

,`"profile"`

,`"boot"`

,`"uniroot"`

,`"ml1"`

,`"betwithin"`

,`"hdi"`

,`"quantile"`

,`"ci"`

,`"eti"`

,`"si"`

,`"bci"`

, or`"bcai"`

. See section*Confidence intervals and approximation of degrees of freedom*in`model_parameters()`

for further details. When`ci_method=NULL`

, in most cases`"wald"`

is used then.- effects
Should results for fixed effects, random effects or both be returned? Only applies to mixed models. May be abbreviated.

## 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)
}
#>
#> Attaching package: ‘brglm2’
#> The following object is masked from ‘package:boot’:
#>
#> aids
#> # Response level: definitely
#>
#> Parameter | Log-Odds | SE | 95% CI | z | p
#> -------------------------------------------------------------------
#> (Intercept) | -1.25 | 0.26 | [-1.76, -0.73] | -4.76 | < .001
#> religion | 0.44 | 0.10 | [ 0.23, 0.64] | 4.20 | < .001
#> gender [female] | -0.14 | 0.17 | [-0.47, 0.19] | -0.82 | 0.414
#>
#> # Response level: probably
#>
#> Parameter | Log-Odds | SE | 95% CI | z | p
#> ----------------------------------------------------------------
#> (Intercept) | 0.47 | 0.29 | [-0.10, 1.04] | 1.62 | 0.105
#> religion | 0.26 | 0.13 | [ 0.01, 0.51] | 2.01 | 0.044
#> gender [female] | 0.19 | 0.21 | [-0.22, 0.60] | 0.90 | 0.370
#>
#> # Response level: probably not
#>
#> Parameter | Log-Odds | SE | 95% CI | z | p
#> -----------------------------------------------------------------
#> (Intercept) | 0.43 | 0.39 | [-0.33, 1.18] | 1.11 | 0.268
#> religion | 0.01 | 0.17 | [-0.33, 0.35] | 0.07 | 0.945
#> gender [female] | -0.16 | 0.28 | [-0.71, 0.39] | -0.57 | 0.566
#>
#> Uncertainty intervals (equal-tailed) and p-values (two-tailed) computed
#> using a Wald z-distribution approximation.
#>
#> The model has a log- or logit-link. Consider using `exponentiate =
#> TRUE` to interpret coefficients as ratios.
```