model_parameters.DirichletRegModel {parameters} | R Documentation |
Parameters from multinomial or cumulative link models
Description
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() . Further non-documented arguments are digits ,
p_digits , ci_digits and footer_digits to set the number of digits for
the output. If s_value = TRUE , the p-value will be replaced by the
S-value in the output (cf. Rafi and Greenland 2020). pd adds an
additional column with the probability of direction (see
bayestestR::p_direction() for details). groups can be used to group
coefficients. It will be passed to the print-method, or can directly be
used in print() , see documentation in print.parameters_model() .
Furthermore, see 'Examples' for this function. For developers, whose
interest mainly is to get a "tidy" data frame of model summaries, it is
recommended to set pretty_names = FALSE to speed up computation of the
summary table.
|
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.
Value
A data frame of indices related to the model's parameters.
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)
[Package
parameters version 0.22.2
Index]