model_parameters.zcpglm  R Documentation 
Parameters from ZeroInflated Models
Description
Parameters from zeroinflated models (from packages like pscl,
cplm or countreg).
Usage
## S3 method for class 'zcpglm'
model_parameters(
model,
ci = 0.95,
bootstrap = FALSE,
iterations = 1000,
component = c("all", "conditional", "zi", "zero_inflated"),
standardize = NULL,
exponentiate = FALSE,
p_adjust = NULL,
keep = NULL,
drop = NULL,
summary = getOption("parameters_summary", FALSE),
verbose = TRUE,
...
)
## S3 method for class 'mhurdle'
model_parameters(
model,
ci = 0.95,
component = c("all", "conditional", "zi", "zero_inflated", "infrequent_purchase", "ip",
"auxiliary"),
exponentiate = FALSE,
p_adjust = NULL,
keep = NULL,
drop = NULL,
verbose = TRUE,
...
)
Arguments
model 
A model with zeroinflation component.

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 zeroinflation part of the model, or the dispersion model be returned?
Applies to models with zeroinflation and/or dispersion component. component
may be one of "conditional" , "zi" , "zeroinflated" , "dispersion" or
"all" (default). May be abbreviated.

standardize 
The method used for standardizing the parameters. Can be
NULL (default; no standardization), "refit" (for refitting 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 logtransformed response values. Note: Deltamethod 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
nonGaussian families.

p_adjust 
Character vector, if not NULL , indicates the method to
adjust pvalues. 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 nonmatching 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: "(onetwothree)" .
If keep is a named list of regular expression patterns, the names of the
listelement 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 .

summary 
Logical, if TRUE , prints summary information about the
model (model formula, number of observations, residual standard deviation
and more).

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() .

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
library(parameters)
if (require("pscl")) {
data("bioChemists")
model < zeroinfl(art ~ fem + mar + kid5 + ment  kid5 + phd, data = bioChemists)
model_parameters(model)
}