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,
parameters = NULL,
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,
parameters = NULL,
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,
parameters = NULL,
verbose = TRUE,
...
)
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 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 vector of length 1 with a regular expression pattern
that describes the parameters that should be returned from the data frame, or
a named list of regular expressions. All non-matching parameters will be
removed from the output. If parameters is a character vector, every
parameter in the "Parameters" column that matches the regular expression in
parameters will be selected from the returned data frame. Furthermore,
if parameters has more than one element, these will be merged into
a regular expression pattern like this: "(one|two|three)". If
parameters 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.
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.
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("rstanarm")) {
model <- stan_glm(
Sepal.Length ~ Petal.Length * Species,
data = iris, iter = 500, refresh = 0
)
model_parameters(model)
}
# }
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