# Parameters from multinomial or cumulative link models

Source:`R/methods_DirichletReg.R`

, `R/methods_bife.R`

, `R/methods_brglm2.R`

, and 2 more
`model_parameters.mlm.Rd`

Parameters from multinomial or cumulative link models

## Usage

```
# S3 method for class 'DirichletRegModel'
model_parameters(
model,
ci = 0.95,
bootstrap = FALSE,
iterations = 1000,
component = c("all", "conditional", "precision"),
standardize = NULL,
exponentiate = FALSE,
p_adjust = NULL,
keep = NULL,
drop = NULL,
verbose = TRUE,
...
)
# S3 method for class 'bifeAPEs'
model_parameters(model, ...)
# S3 method for class 'bracl'
model_parameters(
model,
ci = 0.95,
bootstrap = FALSE,
iterations = 1000,
standardize = NULL,
exponentiate = FALSE,
p_adjust = NULL,
summary = getOption("parameters_summary", FALSE),
keep = NULL,
drop = NULL,
verbose = TRUE,
...
)
# S3 method for class 'mlm'
model_parameters(
model,
ci = 0.95,
vcov = NULL,
vcov_args = NULL,
bootstrap = FALSE,
iterations = 1000,
standardize = NULL,
exponentiate = FALSE,
p_adjust = NULL,
keep = NULL,
drop = NULL,
verbose = TRUE,
...
)
# S3 method for class 'clm2'
model_parameters(
model,
ci = 0.95,
bootstrap = FALSE,
iterations = 1000,
component = c("all", "conditional", "scale"),
standardize = NULL,
exponentiate = FALSE,
p_adjust = NULL,
summary = getOption("parameters_summary", FALSE),
keep = NULL,
drop = NULL,
verbose = TRUE,
...
)
```

## Arguments

- model
A model with multinomial or categorical response value.

- 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
Should all parameters, parameters for the conditional model, for the zero-inflation part of the model, or the dispersion model be returned? Applies to models with zero-inflation and/or dispersion component.

`component`

may be one of`"conditional"`

,`"zi"`

,`"zero-inflated"`

,`"dispersion"`

or`"all"`

(default). May be abbreviated.- 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**).- 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()`

.- summary
Logical, if

`TRUE`

, prints summary information about the model (model formula, number of observations, residual standard deviation and more).- vcov
Variance-covariance matrix used to compute uncertainty estimates (e.g., for robust standard errors). This argument accepts a covariance matrix, a function which returns a covariance matrix, or a string which identifies the function to be used to compute the covariance matrix.

A covariance matrix

A function which returns a covariance matrix (e.g.,

`stats::vcov()`

)A string which indicates the kind of uncertainty estimates to return.

Heteroskedasticity-consistent:

`"vcovHC"`

,`"HC"`

,`"HC0"`

,`"HC1"`

,`"HC2"`

,`"HC3"`

,`"HC4"`

,`"HC4m"`

,`"HC5"`

. See`?sandwich::vcovHC`

.Cluster-robust:

`"vcovCR"`

,`"CR0"`

,`"CR1"`

,`"CR1p"`

,`"CR1S"`

,`"CR2"`

,`"CR3"`

. See`?clubSandwich::vcovCR`

.Bootstrap:

`"vcovBS"`

,`"xy"`

,`"residual"`

,`"wild"`

,`"mammen"`

,`"webb"`

. See`?sandwich::vcovBS`

.Other

`sandwich`

package functions:`"vcovHAC"`

,`"vcovPC"`

,`"vcovCL"`

,`"vcovPL"`

.

- vcov_args
List of arguments to be passed to the function identified by the

`vcov`

argument. This function is typically supplied by the**sandwich**or**clubSandwich**packages. Please refer to their documentation (e.g.,`?sandwich::vcovHAC`

) to see the list of available arguments.

## Details

Multinomial or cumulative link models, i.e. models where the
response value (dependent variable) is categorical and has more than two
levels, usually return coefficients for each response level. Hence, the
output from `model_parameters()`

will split the coefficient tables
by the different levels of the model's response.

## See also

`insight::standardize_names()`

to rename
columns into a consistent, standardized naming scheme.

## Examples

```
data("stemcell", package = "brglm2")
model <- brglm2::bracl(
research ~ as.numeric(religion) + gender,
weights = frequency,
data = stemcell,
type = "ML"
)
model_parameters(model)
#> # 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.
```