Parameters from special regression models not listed under one of the previous categories yet.

# S3 method for averaging
model_parameters(
model,
ci = 0.95,
component = c("conditional", "full"),
exponentiate = FALSE,
verbose = TRUE,
...
)

# S3 method for betareg
model_parameters(
model,
ci = 0.95,
bootstrap = FALSE,
iterations = 1000,
component = c("conditional", "precision", "all"),
standardize = NULL,
exponentiate = FALSE,
verbose = TRUE,
...
)

# S3 method for glmx
model_parameters(
model,
ci = 0.95,
bootstrap = FALSE,
iterations = 1000,
component = c("all", "conditional", "extra"),
standardize = NULL,
exponentiate = FALSE,
verbose = TRUE,
...
)

## Arguments

model Model object. Confidence Interval (CI) level. Default to 0.95 (95%). Model component for which parameters should be shown. May be one of "conditional", "precision" (betareg), "scale" (ordinal), "extra" (glmx), "marginal" (mfx), "conditional" or "full" (for MuMIn::model.avg()) or "all". Logical, indicating whether or not to exponentiate the the coefficients (and related confidence intervals). This is typical for logistic regression, or more generally speaking, for models with log or logit links. 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 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. For instance, when bootstrap = TRUE, arguments like type or parallel are passed down to bootstrap_model(), and arguments like ci_method are passed down to describe_posterior. 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. 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. Important: Categorical predictors (i.e. factors) are never standardized by default, which may be a different behaviour compared to other R packages or other software packages (like SPSS). If standardizing categorical predictors is desired, either use standardize="basic" to mimic behaviour of SPSS or packages such as lm.beta, or standardize the data with effectsize::standardize(force=TRUE) before fitting the model. Robust estimation (i.e. robust=TRUE) of standardized parameters only works when standardize="refit".

## Value

A data frame of indices related to the model's parameters.

standardize_names() to rename columns into a consistent, standardized naming scheme.

## Examples

library(parameters)
if (require("brglm2", quietly = TRUE)) {
data("stemcell")
model <- bracl(
research ~ as.numeric(religion) + gender,
weights = frequency,
data = stemcell,
type = "ML"
)
model_parameters(model)
}
#> # Response level: definitely
#>
#> Parameter       | Log-Odds |   SE |         95% CI |     z |      p
#> -------------------------------------------------------------------
#> (Intercept)     |    -1.25 | 0.26 | [-1.76, -0.73] | -4.76 | < .001
#> religion        |     0.44 | 0.10 | [ 0.23,  0.64] |  4.20 | < .001
#> gender [female] |    -0.14 | 0.17 | [-0.47,  0.19] | -0.82 | 0.414
#>
#> # Response level: probably
#>
#> Parameter       | Log-Odds |   SE |        95% CI |    z |     p
#> ----------------------------------------------------------------
#> (Intercept)     |     0.47 | 0.29 | [-0.10, 1.04] | 1.62 | 0.105
#> religion        |     0.26 | 0.13 | [ 0.01, 0.51] | 2.01 | 0.044
#> gender [female] |     0.19 | 0.21 | [-0.22, 0.60] | 0.90 | 0.370
#>
#> # Response level: probably not
#>
#> Parameter       | Log-Odds |   SE |        95% CI |     z |     p
#> -----------------------------------------------------------------
#> (Intercept)     |     0.43 | 0.39 | [-0.33, 1.18] |  1.11 | 0.268
#> religion        |     0.01 | 0.17 | [-0.33, 0.35] |  0.07 | 0.945
#> gender [female] |    -0.16 | 0.28 | [-0.71, 0.39] | -0.57 | 0.566