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Parameters from zero-inflated models (from packages like pscl, cplm or countreg).


# S3 method for zcpglm
  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 mhurdle
  ci = 0.95,
  component = c("all", "conditional", "zi", "zero_inflated", "infrequent_purchase", "ip",
  exponentiate = FALSE,
  p_adjust = NULL,
  keep = NULL,
  drop = NULL,
  verbose = TRUE,



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.


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.


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".


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.


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).


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.


See keep.


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


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().


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.


if (require("pscl")) {
  model <- zeroinfl(art ~ fem + mar + kid5 + ment | kid5 + phd, data = bioChemists)
#> Loading required package: pscl
#> Classes and Methods for R developed in the
#> Political Science Computational Laboratory
#> Department of Political Science
#> Stanford University
#> Simon Jackman
#> hurdle and zeroinfl functions by Achim Zeileis
#> # Fixed Effects 
#> Parameter     | Log-Mean |       SE |         95% CI |     z |      p
#> ---------------------------------------------------------------------
#> (Intercept)   |     0.56 |     0.07 | [ 0.43,  0.69] |  8.26 | < .001
#> fem [Women]   |    -0.23 |     0.06 | [-0.34, -0.11] | -3.91 | < .001
#> mar [Married] |     0.14 |     0.07 | [ 0.01,  0.27] |  2.07 | 0.038 
#> kid5          |    -0.17 |     0.05 | [-0.26, -0.07] | -3.43 | < .001
#> ment          |     0.02 | 2.12e-03 | [ 0.02,  0.03] | 10.05 | < .001
#> # Zero-Inflation 
#> Parameter   | Log-Odds |   SE |         95% CI |     z |     p
#> --------------------------------------------------------------
#> (Intercept) |    -0.93 | 0.43 | [-1.78, -0.08] | -2.14 | 0.032
#> kid5        |     0.05 | 0.22 | [-0.38,  0.47] |  0.21 | 0.831
#> phd         |    -0.25 | 0.14 | [-0.51,  0.02] | -1.84 | 0.065