Parameters from Bayesian models.
# S3 method for data.frame
model_parameters(
model,
centrality = "median",
dispersion = FALSE,
ci = 0.95,
ci_method = "hdi",
test = c("pd", "rope"),
rope_range = "default",
rope_ci = 0.95,
keep = NULL,
drop = NULL,
parameters = keep,
verbose = TRUE,
...
)# S3 method for brmsfit
model_parameters(
model,
centrality = "median",
dispersion = FALSE,
ci = 0.95,
ci_method = "hdi",
test = c("pd", "rope"),
rope_range = "default",
rope_ci = 0.95,
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 = 0.95,
ci_method = "hdi",
test = c("pd", "rope"),
rope_range = "default",
rope_ci = 0.95,
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,
...
)
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 0.95 (95%). See
bayestestR::ci() for further details.
Method for computing degrees of freedom for
confidence intervals (CI) and the related p-values. Allowed are following
options (which vary depending on the model class): "residual",
"normal", "likelihood", "satterthwaite", "kenward", "wald",
"profile", "boot", "uniroot", "ml1", "betwithin", "hdi",
"quantile", "ci", "eti", "si", "bci", or "bcai". See section
Confidence intervals and approximation of degrees of freedom in
model_parameters() for further details. When ci_method=NULL, in most
cases "wald" is used then.
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 (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.
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.
Which type of parameters to return, such as parameters for the
conditional model, the zero-inflated part of the model, the dispersion
term, or other auxiliary parameters be returned? Applies to models with
zero-inflated and/or dispersion formula, or if parameters such as sigma
should be included. May be abbreviated. Note that the conditional
component is also called count or mean component, depending on the
model. There are three convenient shortcuts: component = "all" returns
all possible parameters. If component = "location", location parameters
such as conditional, zero_inflated, or smooth_terms, are returned
(everything that are fixed or random effects - depending on the effects
argument - but no auxiliary parameters). For component = "distributional"
(or "auxiliary"), components like sigma, dispersion, or beta
(and other auxiliary parameters) are returned.
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
NULL (default; no standardization), "refit" (for re-fitting the model
on standardized data) or one of "basic", "posthoc", "smart",
"pseudo". See 'Details' in effectsize::standardize_parameters().
Important:
The "refit" method does not standardized 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 returned.
Robust estimation (i.e., vcov set to a value other than NULL) 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.
There are different ways of approximating the degrees of freedom depending
on different assumptions about the nature of the model and its sampling
distribution. The ci_method argument modulates the method for computing degrees
of freedom (df) that are used to calculate confidence intervals (CI) and the
related p-values. Following options are allowed, depending on the model
class:
Classical methods:
Classical inference is generally based on the Wald method. The Wald approach to inference computes a test statistic by dividing the parameter estimate by its standard error (Coefficient / SE), then comparing this statistic against a t- or normal distribution. This approach can be used to compute CIs and p-values.
"wald":
Applies to non-Bayesian models. For linear models, CIs computed using the Wald method (SE and a t-distribution with residual df); p-values computed using the Wald method with a t-distribution with residual df. For other models, CIs computed using the Wald method (SE and a normal distribution); p-values computed using the Wald method with a normal distribution.
"normal"
Applies to non-Bayesian models. Compute Wald CIs and p-values, but always use a normal distribution.
"residual"
Applies to non-Bayesian models. Compute Wald CIs and p-values, but always use a t-distribution with residual df when possible. If the residual df for a model cannot be determined, a normal distribution is used instead.
Methods for mixed models:
Compared to fixed effects (or single-level) models, determining appropriate df for Wald-based inference in mixed models is more difficult. See the R GLMM FAQ for a discussion.
Several approximate methods for computing df are available, but you should
also consider instead using profile likelihood ("profile") or bootstrap ("boot")
CIs and p-values instead.
"satterthwaite"
Applies to linear mixed models. CIs computed using the Wald method (SE and a t-distribution with Satterthwaite df); p-values computed using the Wald method with a t-distribution with Satterthwaite df.
"kenward"
Applies to linear mixed models. CIs computed using the Wald method (Kenward-Roger SE and a t-distribution with Kenward-Roger df); p-values computed using the Wald method with Kenward-Roger SE and t-distribution with Kenward-Roger df.
"ml1"
Applies to linear mixed models. CIs computed using the Wald
method (SE and a t-distribution with m-l-1 approximated df); p-values
computed using the Wald method with a t-distribution with m-l-1 approximated df.
See ci_ml1().
"betwithin"
Applies to linear mixed models and generalized linear mixed models.
CIs computed using the Wald method (SE and a t-distribution with between-within df);
p-values computed using the Wald method with a t-distribution with between-within df.
See ci_betwithin().
Likelihood-based methods:
Likelihood-based inference is based on comparing the likelihood for the maximum-likelihood estimate to the the likelihood for models with one or more parameter values changed (e.g., set to zero or a range of alternative values). Likelihood ratios for the maximum-likelihood and alternative models are compared to a \(\chi\)-squared distribution to compute CIs and p-values.
"profile"
Applies to non-Bayesian models of class glm, polr or glmmTMB.
CIs computed by profiling the likelihood curve for a parameter, using
linear interpolation to find where likelihood ratio equals a critical value;
p-values computed using the Wald method with a normal-distribution (note:
this might change in a future update!)
"uniroot"
Applies to non-Bayesian models of class glmmTMB. CIs
computed by profiling the likelihood curve for a parameter, using root
finding to find where likelihood ratio equals a critical value; p-values
computed using the Wald method with a normal-distribution (note: this
might change in a future update!)
Methods for bootstrapped or Bayesian models:
Bootstrap-based inference is based on resampling and refitting the model to the resampled datasets. The distribution of parameter estimates across resampled datasets is used to approximate the parameter's sampling distribution. Depending on the type of model, several different methods for bootstrapping and constructing CIs and p-values from the bootstrap distribution are available.
For Bayesian models, inference is based on drawing samples from the model posterior distribution.
"quantile" (or "eti")
Applies to all models (including Bayesian models).
For non-Bayesian models, only applies if bootstrap = TRUE. CIs computed
as equal tailed intervals using the quantiles of the bootstrap or
posterior samples; p-values are based on the probability of direction.
See bayestestR::eti().
"hdi"
Applies to all models (including Bayesian models). For non-Bayesian
models, only applies if bootstrap = TRUE. CIs computed as highest density intervals
for the bootstrap or posterior samples; p-values are based on the probability of direction.
See bayestestR::hdi().
"bci" (or "bcai")
Applies to all models (including Bayesian models).
For non-Bayesian models, only applies if bootstrap = TRUE. CIs computed
as bias corrected and accelerated intervals for the bootstrap or
posterior samples; p-values are based on the probability of direction.
See bayestestR::bci().
"si"
Applies to Bayesian models with proper priors. CIs computed as
support intervals comparing the posterior samples against the prior samples;
p-values are based on the probability of direction. See bayestestR::si().
"boot"
Applies to non-Bayesian models of class merMod. CIs computed
using parametric bootstrapping (simulating data from the fitted model);
p-values computed using the Wald method with a normal-distribution)
(note: this might change in a future update!).
For all iteration-based methods other than "boot"
("hdi", "quantile", "ci", "eti", "si", "bci", "bcai"),
p-values are based on the probability of direction (bayestestR::p_direction()),
which is converted into a p-value using bayestestR::pd_to_p().
insight::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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