Parameters from zero-inflated models.
# S3 method for zeroinfl
model_parameters(
model,
ci = 0.95,
bootstrap = FALSE,
iterations = 1000,
component = c("all", "conditional", "zi", "zero_inflated"),
standardize = NULL,
exponentiate = FALSE,
robust = FALSE,
p_adjust = NULL,
...
)
A model with zero-inflation component.
Confidence Interval (CI) level. Default to 0.95 (95%).
Should estimates be based on bootstrapped model? If TRUE
, then arguments of Bayesian regressions apply (see also bootstrap_parameters()
).
The number of bootstrap replicates. This only apply in the case of bootstrapped frequentist models.
Model component for which parameters should be shown. May be one of "conditional"
, "precision"
(betareg), "scale"
(ordinal), "extra"
(glmx), "marginal"
(mfx) or "all"
.
The method used for standardizing the parameters. Can be "refit"
, "posthoc"
, "smart"
, "basic"
or NULL
(default) for no standardization. See 'Details' in standardize_parameters
. Note that robust estimation (i.e. robust=TRUE
) of standardized parameters only works when standardize="refit"
.
Logical, indicating whether or not to exponentiate the the coefficients (and related confidence intervals). This is typical for, say, logistic regressions, or more generally speaking: for models with log or logit link. Note: standard errors are also transformed (by multiplying the standard errors with the exponentiated coefficients), to mimic behaviour of other software packages, such as Stata.
Logical, if TRUE
, robust standard errors are calculated (if possible), and confidence intervals and p-values are based on these robust standard errors. Additional arguments like vcov_estimation
or vcov_type
are passed down to other methods, see standard_error_robust()
for details.
Character vector, if not NULL
, indicates the method to adjust p-values. See p.adjust
for details.
Arguments passed to or from other methods. For instance, when bootstrap = TRUE
, arguments like ci_method
are passed down to describe_posterior
.
A data frame of indices related to the model's parameters.
standardize_names()
to rename
columns into a consistent, standardized naming scheme.
# NOT RUN {
library(parameters)
if (require("pscl")) {
data("bioChemists")
model <- zeroinfl(art ~ fem + mar + kid5 + ment | kid5 + phd, data = bioChemists)
model_parameters(model)
}
# }
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