Extract and compute indices and measures to describe parameters of (general) linear models (GLMs).
# S3 method for default
model_parameters(
model,
ci = 0.95,
bootstrap = FALSE,
iterations = 1000,
standardize = NULL,
exponentiate = FALSE,
robust = FALSE,
p_adjust = NULL,
summary = FALSE,
parameters = NULL,
verbose = TRUE,
...
)# S3 method for glm
model_parameters(
model,
ci = 0.95,
df_method = "profile",
bootstrap = FALSE,
iterations = 1000,
standardize = NULL,
exponentiate = FALSE,
robust = FALSE,
p_adjust = NULL,
summary = FALSE,
verbose = TRUE,
...
)
# S3 method for logitor
model_parameters(
model,
ci = 0.95,
bootstrap = FALSE,
iterations = 1000,
standardize = NULL,
exponentiate = TRUE,
robust = FALSE,
p_adjust = NULL,
verbose = TRUE,
...
)
# S3 method for poissonmfx
model_parameters(
model,
ci = 0.95,
bootstrap = FALSE,
iterations = 1000,
component = c("all", "conditional", "marginal"),
standardize = NULL,
exponentiate = FALSE,
robust = FALSE,
p_adjust = NULL,
verbose = TRUE,
...
)
# S3 method for betamfx
model_parameters(
model,
ci = 0.95,
bootstrap = FALSE,
iterations = 1000,
component = c("all", "conditional", "precision", "marginal"),
standardize = NULL,
exponentiate = FALSE,
robust = FALSE,
p_adjust = NULL,
verbose = TRUE,
...
)
Model object.
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 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".
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.
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.
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).
Logical, if TRUE, prints summary information about the
model (model formula, number of observations, residual standard deviation
and more).
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 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.
Method for computing degrees of freedom for confidence
intervals (CI). Only applies to models of class glm or polr.
May be "profile" or "wald".
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".
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)
model <- lm(mpg ~ wt + cyl, data = mtcars)
model_parameters(model)
# bootstrapped parameters
model_parameters(model, bootstrap = TRUE)
# standardized parameters
model_parameters(model, standardize = "refit")
# different p-value style in output
model_parameters(model, p_digits = 5)
model_parameters(model, digits = 3, ci_digits = 4, p_digits = "scientific")
# logistic regression model
model <- glm(vs ~ wt + cyl, data = mtcars, family = "binomial")
model_parameters(model)
# show odds ratio / exponentiated coefficients
model_parameters(model, exponentiate = TRUE)
# }
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