A mixed model.

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 draws to simulate/bootstrap.

The method used for standardizing the parameters. Can be "refit", "posthoc", "smart", "basic", "pseudo" 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".

Should parameters for fixed effects ("fixed"), random effects ("random"), or both ("all") be returned? Only applies to mixed models. May be abbreviated.

Logical, for multilevel models (i.e. models with random effects) and when effects = "all" or effects = "random", include the parameters for each group level from random effects. If group_level = FALSE (the default), only information on SD and COR are shown.

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. For compare_parameters(), exponentiate = "nongaussian" will only exponentiate coefficients for all models except those from Gaussian family.

Logical, if TRUE, a summary of the random effects is included. See random_parameters for details.

Method for computing degrees of freedom for p values, standard errors and confidence intervals (CI). May be "wald" (default, see degrees_of_freedom), "ml1" (see dof_ml1), "betwithin" (see dof_betwithin), "satterthwaite" (see dof_satterthwaite) or "kenward" (see dof_kenward). The options df_method = "boot", df_method = "profile" and df_method = "uniroot" only affect confidence intervals; in this case, bootstrapped resp. profiled confidence intervals are computed. "uniroot" only applies to models of class glmmTMB. Note that when df_method is not "wald", robust standard errors etc. cannot be computed.

Character vector, if not NULL, indicates the method to adjust p-values. See p.adjust for details. Further possible adjustment methods are "tukey", "scheffe", "sidak" and "none" to explicitly disable adjustment for emmGrid objects (from emmeans).

Toggle warnings and messages.

Arguments passed to or from other methods.

Should all parameters, parameters for the conditional model, or for the zero-inflated part of the model be returned? Applies to models with zero-inflated component. component may be one of "conditional", "zi", "zero-inflated", "dispersion" or "all" (default). May be abbreviated.

Logical, if TRUE and models contains within- and between-effects (see demean), the Component column will indicate which variables belong to the within-effects, between-effects, and cross-level interactions. By default, the Component column indicates, which parameters belong to the conditional or zero-inflated component of the model.

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

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 and this vignette for working examples.