Parameters from Bayesian models.

# S3 method for data.frame
model_parameters(
model,
centrality = "median",
dispersion = FALSE,
ci = 0.89,
ci_method = "hdi",
test = c("pd", "rope"),
rope_range = "default",
rope_ci = 1,
keep = NULL,
drop = NULL,
parameters = keep,
verbose = TRUE,
...
)

# S3 method for brmsfit
model_parameters(
model,
centrality = "median",
dispersion = FALSE,
ci = 0.89,
ci_method = "hdi",
test = c("pd", "rope"),
rope_range = "default",
rope_ci = 1,
bf_prior = NULL,
diagnostic = c("ESS", "Rhat"),
priors = FALSE,
effects = "fixed",
component = "all",
exponentiate = FALSE,
standardize = NULL,
group_level = FALSE,
keep = NULL,
drop = NULL,
parameters = keep,
verbose = TRUE,
...
)

# S3 method for stanreg
model_parameters(
model,
centrality = "median",
dispersion = FALSE,
ci = 0.89,
ci_method = "hdi",
test = c("pd", "rope"),
rope_range = "default",
rope_ci = 1,
bf_prior = NULL,
diagnostic = c("ESS", "Rhat"),
priors = TRUE,
effects = "fixed",
exponentiate = FALSE,
standardize = NULL,
group_level = FALSE,
keep = NULL,
drop = NULL,
parameters = keep,
verbose = TRUE,
...
)

## Arguments

model Bayesian model (including SEM from blavaan. May also be a data frame with posterior samples. The point-estimates (centrality indices) to compute. Character (vector) or list with one or more of these options: "median", "mean", "MAP" or "all". Logical, if TRUE, computes indices of dispersion related to the estimate(s) (SD and MAD for mean and median, respectively). Credible Interval (CI) level. Default to 0.89 (89%). See ci for further details. The type of index used for Credible Interval. Can be "HDI" (default, see hdi), "ETI" (see eti), "BCI" (see bci) or "SI" (see si). The indices of effect existence to compute. Character (vector) or list with one or more of these options: "p_direction" (or "pd"), "rope", "p_map", "equivalence_test" (or "equitest"), "bayesfactor" (or "bf") or "all" to compute all tests. For each "test", the corresponding bayestestR function is called (e.g. rope or p_direction) and its results included in the summary output. ROPE's lower and higher bounds. Should be a list of two values (e.g., c(-0.1, 0.1)) or "default". If "default", the bounds are set to x +- 0.1*SD(response). The Credible Interval (CI) probability, corresponding to the proportion of HDI, to use for the percentage in ROPE. Character containing a regular expression pattern that describes the parameters that should be included in the returned data frame (for keep), resp. parameters to exclude (drop). 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 parameters 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. Character containing a regular expression pattern that describes the parameters that should be included in the returned data frame (for keep), resp. parameters to exclude (drop). 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 parameters 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. Deprecated, alias for keep. Toggle messages and warnings. Currently not used. Distribution representing a prior for the computation of Bayes factors / SI. Used if the input is a posterior, otherwise (in the case of models) ignored. Diagnostic metrics to compute. Character (vector) or list with one or more of these options: "ESS", "Rhat", "MCSE" or "all". Add the prior used for each parameter. Should results for fixed effects, random effects or both be returned? Only applies to mixed models. May be abbreviated. 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. 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". 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.

## Value

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

## Note

When standardize = "refit", columns diagnostic, bf_prior and priors refer to the original model. If model is a data frame, arguments diagnostic, bf_prior and priors are ignored.

There is also a plot()-method implemented in the see-package.

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

## Examples

if (FALSE) {
library(parameters)
if (require("rstanarm")) {
model <- stan_glm(
Sepal.Length ~ Petal.Length * Species,
data = iris, iter = 500, refresh = 0
)
model_parameters(model)
}
}