Parameters from special regression models not listed under one of the previous categories yet.
Parameters from Hypothesis Testing.
# S3 method for PMCMR
model_parameters(model, ...)# S3 method for glimML
model_parameters(
model,
ci = 0.95,
bootstrap = FALSE,
iterations = 1000,
component = c("conditional", "random", "dispersion", "all"),
standardize = NULL,
exponentiate = FALSE,
p_adjust = NULL,
summary = getOption("parameters_summary", FALSE),
keep = NULL,
drop = NULL,
verbose = TRUE,
...
)
# S3 method for averaging
model_parameters(
model,
ci = 0.95,
component = c("conditional", "full"),
exponentiate = FALSE,
p_adjust = NULL,
summary = getOption("parameters_summary", FALSE),
keep = NULL,
drop = NULL,
verbose = TRUE,
...
)
# S3 method for mle2
model_parameters(
model,
ci = 0.95,
ci_method = NULL,
bootstrap = FALSE,
iterations = 1000,
standardize = NULL,
exponentiate = FALSE,
p_adjust = NULL,
summary = getOption("parameters_summary", FALSE),
keep = NULL,
drop = NULL,
vcov = NULL,
vcov_args = NULL,
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,
p_adjust = NULL,
summary = getOption("parameters_summary", FALSE),
keep = NULL,
drop = NULL,
verbose = TRUE,
...
)
# S3 method for bfsl
model_parameters(
model,
ci = 0.95,
ci_method = "residual",
p_adjust = NULL,
summary = getOption("parameters_summary", FALSE),
keep = NULL,
drop = NULL,
verbose = TRUE,
...
)
# S3 method for deltaMethod
model_parameters(model, p_adjust = NULL, verbose = TRUE, ...)
# S3 method for emmGrid
model_parameters(
model,
ci = 0.95,
centrality = "median",
dispersion = FALSE,
ci_method = "eti",
test = "pd",
rope_range = "default",
rope_ci = 0.95,
exponentiate = FALSE,
p_adjust = NULL,
keep = NULL,
drop = NULL,
verbose = TRUE,
...
)
# S3 method for emm_list
model_parameters(
model,
ci = 0.95,
exponentiate = FALSE,
p_adjust = NULL,
verbose = TRUE,
...
)
# S3 method for epi.2by2
model_parameters(model, verbose = TRUE, ...)
# S3 method for fitdistr
model_parameters(model, exponentiate = FALSE, verbose = TRUE, ...)
# S3 method for ggeffects
model_parameters(model, keep = NULL, drop = NULL, verbose = TRUE, ...)
# S3 method for SemiParBIV
model_parameters(
model,
ci = 0.95,
bootstrap = FALSE,
iterations = 1000,
standardize = NULL,
exponentiate = FALSE,
p_adjust = NULL,
keep = NULL,
drop = NULL,
verbose = TRUE,
...
)
# S3 method for glmm
model_parameters(
model,
ci = 0.95,
effects = c("all", "fixed", "random"),
bootstrap = FALSE,
iterations = 1000,
standardize = NULL,
exponentiate = FALSE,
keep = NULL,
drop = NULL,
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,
p_adjust = NULL,
keep = NULL,
drop = NULL,
verbose = TRUE,
...
)
# S3 method for ivFixed
model_parameters(
model,
ci = 0.95,
ci_method = "wald",
keep = NULL,
drop = NULL,
verbose = TRUE,
...
)
# S3 method for ivprobit
model_parameters(
model,
ci = 0.95,
ci_method = "wald",
keep = NULL,
drop = NULL,
verbose = TRUE,
...
)
# S3 method for lmodel2
model_parameters(
model,
ci = 0.95,
exponentiate = FALSE,
p_adjust = NULL,
keep = NULL,
drop = NULL,
verbose = TRUE,
...
)
# S3 method for logistf
model_parameters(
model,
ci = 0.95,
ci_method = NULL,
bootstrap = FALSE,
iterations = 1000,
standardize = NULL,
exponentiate = FALSE,
p_adjust = NULL,
summary = getOption("parameters_summary", FALSE),
keep = NULL,
drop = NULL,
vcov = NULL,
vcov_args = NULL,
verbose = TRUE,
...
)
# S3 method for lqmm
model_parameters(
model,
ci = 0.95,
bootstrap = FALSE,
iterations = 1000,
p_adjust = NULL,
verbose = TRUE,
...
)
# S3 method for marginaleffects
model_parameters(model, ci = 0.95, exponentiate = FALSE, ...)
# S3 method for comparisons
model_parameters(model, ci = 0.95, exponentiate = FALSE, ...)
# S3 method for marginalmeans
model_parameters(model, ci = 0.95, exponentiate = FALSE, ...)
# S3 method for hypotheses
model_parameters(model, ci = 0.95, exponentiate = FALSE, ...)
# S3 method for slopes
model_parameters(model, ci = 0.95, exponentiate = FALSE, ...)
# S3 method for predictions
model_parameters(model, ci = 0.95, exponentiate = TRUE, ...)
# S3 method for margins
model_parameters(
model,
ci = 0.95,
exponentiate = FALSE,
p_adjust = NULL,
verbose = TRUE,
...
)
# S3 method for maxLik
model_parameters(
model,
ci = 0.95,
ci_method = NULL,
bootstrap = FALSE,
iterations = 1000,
standardize = NULL,
exponentiate = FALSE,
p_adjust = NULL,
summary = getOption("parameters_summary", FALSE),
keep = NULL,
drop = NULL,
verbose = TRUE,
vcov = NULL,
vcov_args = NULL,
...
)
# S3 method for maxim
model_parameters(
model,
ci = 0.95,
ci_method = NULL,
bootstrap = FALSE,
iterations = 1000,
standardize = NULL,
exponentiate = FALSE,
p_adjust = NULL,
summary = getOption("parameters_summary", FALSE),
keep = NULL,
drop = NULL,
verbose = TRUE,
vcov = NULL,
vcov_args = NULL,
...
)
# S3 method for mediate
model_parameters(model, ci = 0.95, exponentiate = FALSE, verbose = TRUE, ...)
# S3 method for metaplus
model_parameters(
model,
ci = 0.95,
bootstrap = FALSE,
iterations = 1000,
standardize = NULL,
exponentiate = FALSE,
include_studies = TRUE,
keep = NULL,
drop = NULL,
verbose = TRUE,
...
)
# S3 method for meta_random
model_parameters(
model,
ci = 0.95,
ci_method = "eti",
exponentiate = FALSE,
include_studies = TRUE,
verbose = TRUE,
...
)
# S3 method for meta_fixed
model_parameters(
model,
ci = 0.95,
ci_method = "eti",
exponentiate = FALSE,
include_studies = TRUE,
verbose = TRUE,
...
)
# S3 method for meta_bma
model_parameters(
model,
ci = 0.95,
ci_method = "eti",
exponentiate = FALSE,
include_studies = TRUE,
verbose = TRUE,
...
)
# S3 method for logitor
model_parameters(
model,
ci = 0.95,
bootstrap = FALSE,
iterations = 1000,
standardize = NULL,
exponentiate = TRUE,
p_adjust = NULL,
verbose = TRUE,
...
)
# S3 method for poissonirr
model_parameters(
model,
ci = 0.95,
bootstrap = FALSE,
iterations = 1000,
standardize = NULL,
exponentiate = TRUE,
p_adjust = NULL,
verbose = TRUE,
...
)
# S3 method for negbinirr
model_parameters(
model,
ci = 0.95,
bootstrap = FALSE,
iterations = 1000,
standardize = NULL,
exponentiate = TRUE,
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,
p_adjust = NULL,
keep = NULL,
drop = NULL,
verbose = TRUE,
...
)
# S3 method for logitmfx
model_parameters(
model,
ci = 0.95,
bootstrap = FALSE,
iterations = 1000,
component = c("all", "conditional", "marginal"),
standardize = NULL,
exponentiate = FALSE,
p_adjust = NULL,
keep = NULL,
drop = NULL,
verbose = TRUE,
...
)
# S3 method for probitmfx
model_parameters(
model,
ci = 0.95,
bootstrap = FALSE,
iterations = 1000,
component = c("all", "conditional", "marginal"),
standardize = NULL,
exponentiate = FALSE,
p_adjust = NULL,
keep = NULL,
drop = NULL,
verbose = TRUE,
...
)
# S3 method for negbinmfx
model_parameters(
model,
ci = 0.95,
bootstrap = FALSE,
iterations = 1000,
component = c("all", "conditional", "marginal"),
standardize = NULL,
exponentiate = FALSE,
p_adjust = NULL,
keep = NULL,
drop = NULL,
verbose = TRUE,
...
)
# S3 method for betaor
model_parameters(
model,
ci = 0.95,
bootstrap = FALSE,
iterations = 1000,
component = c("conditional", "precision", "all"),
standardize = NULL,
exponentiate = 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,
p_adjust = NULL,
keep = NULL,
drop = NULL,
verbose = TRUE,
...
)
# S3 method for mjoint
model_parameters(
model,
ci = 0.95,
effects = "fixed",
component = c("all", "conditional", "survival"),
exponentiate = FALSE,
p_adjust = NULL,
keep = NULL,
drop = NULL,
verbose = TRUE,
...
)
# S3 method for model_fit
model_parameters(
model,
ci = 0.95,
effects = "fixed",
component = "conditional",
ci_method = "profile",
bootstrap = FALSE,
iterations = 1000,
standardize = NULL,
exponentiate = FALSE,
p_adjust = NULL,
verbose = TRUE,
...
)
# S3 method for glht
model_parameters(
model,
ci = 0.95,
exponentiate = FALSE,
keep = NULL,
drop = NULL,
verbose = TRUE,
...
)
# S3 method for mvord
model_parameters(
model,
ci = 0.95,
component = c("all", "conditional", "thresholds", "correlation"),
standardize = NULL,
exponentiate = FALSE,
p_adjust = NULL,
summary = getOption("parameters_summary", FALSE),
keep = NULL,
drop = NULL,
verbose = TRUE,
...
)
# S3 method for pgmm
model_parameters(
model,
ci = 0.95,
component = c("conditional", "all"),
exponentiate = FALSE,
p_adjust = NULL,
keep = NULL,
drop = NULL,
verbose = TRUE,
...
)
# S3 method for rqss
model_parameters(
model,
ci = 0.95,
ci_method = "residual",
bootstrap = FALSE,
iterations = 1000,
standardize = NULL,
exponentiate = FALSE,
p_adjust = NULL,
keep = NULL,
drop = NULL,
verbose = TRUE,
...
)
# S3 method for rqs
model_parameters(
model,
ci = 0.95,
bootstrap = FALSE,
iterations = 1000,
standardize = NULL,
exponentiate = FALSE,
p_adjust = NULL,
keep = NULL,
drop = NULL,
verbose = TRUE,
...
)
# S3 method for selection
model_parameters(
model,
ci = 0.95,
component = c("all", "selection", "outcome", "auxiliary"),
bootstrap = FALSE,
iterations = 1000,
standardize = NULL,
exponentiate = FALSE,
p_adjust = NULL,
summary = getOption("parameters_summary", FALSE),
keep = NULL,
drop = NULL,
verbose = TRUE,
...
)
# S3 method for mle
model_parameters(
model,
ci = 0.95,
ci_method = NULL,
bootstrap = FALSE,
iterations = 1000,
standardize = NULL,
exponentiate = FALSE,
p_adjust = NULL,
summary = getOption("parameters_summary", FALSE),
keep = NULL,
drop = NULL,
vcov = NULL,
vcov_args = NULL,
verbose = TRUE,
...
)
# S3 method for systemfit
model_parameters(
model,
ci = 0.95,
ci_method = NULL,
bootstrap = FALSE,
iterations = 1000,
standardize = NULL,
exponentiate = FALSE,
p_adjust = NULL,
summary = FALSE,
keep = NULL,
drop = NULL,
verbose = TRUE,
...
)
# S3 method for varest
model_parameters(
model,
ci = 0.95,
bootstrap = FALSE,
iterations = 1000,
standardize = NULL,
exponentiate = FALSE,
p_adjust = NULL,
verbose = TRUE,
...
)
# S3 method for t1way
model_parameters(model, keep = NULL, verbose = TRUE, ...)
# S3 method for med1way
model_parameters(model, verbose = TRUE, ...)
# S3 method for dep.effect
model_parameters(model, keep = NULL, verbose = TRUE, ...)
# S3 method for yuen
model_parameters(model, verbose = TRUE, ...)
A data frame of indices related to the model's parameters.
A data frame of indices related to the model's parameters.
A data frame of indices related to the model's parameters.
Object from WRS2 package.
Arguments passed to or from other methods.
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.
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".
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 standardize_parameters().
Importantly:
The "refit" method does not standardize 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 standardized.
Robust estimation (i.e., vcov set to a value other than NULL) of
standardized parameters only works when standardize="refit".
Logical, indicating whether or not to exponentiate the
coefficients (and related confidence intervals). This is typical for
logistic regression, or more generally speaking, for models with log or
logit links. It is also recommended to use exponentiate = TRUE for models
with log-transformed response values. 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 stats::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 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.
Toggle warnings and messages.
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.
Variance-covariance matrix used to compute uncertainty estimates (e.g., for robust standard errors). This argument accepts a covariance matrix, a function which returns a covariance matrix, or a string which identifies the function to be used to compute the covariance matrix.
A covariance matrix
A function which returns a covariance matrix (e.g., stats::vcov())
A string which indicates the kind of uncertainty estimates to return.
Heteroskedasticity-consistent: "vcovHC", "HC", "HC0", "HC1",
"HC2", "HC3", "HC4", "HC4m", "HC5". See ?sandwich::vcovHC.
Cluster-robust: "vcovCR", "CR0", "CR1", "CR1p", "CR1S", "CR2",
"CR3". See ?clubSandwich::vcovCR.
Bootstrap: "vcovBS", "xy", "residual", "wild", "mammen", "webb".
See ?sandwich::vcovBS.
Other sandwich package functions: "vcovHAC", "vcovPC", "vcovCL", "vcovPL".
List of arguments to be passed to the function identified by
the vcov argument. This function is typically supplied by the sandwich
or clubSandwich packages. Please refer to their documentation (e.g.,
?sandwich::vcovHAC) to see the list of available arguments.
The point-estimates (centrality indices) to compute. Character
(vector) or list with one or more of these options: "median", "mean", "MAP"
(see map_estimate()), "trimmed" (which is just mean(x, trim = threshold)),
"mode" or "all".
Logical, if TRUE, computes indices of dispersion related
to the estimate(s) (SD and MAD for mean and median, respectively).
Dispersion is not available for "MAP" or "mode" centrality indices.
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.
Should results for fixed effects, random effects or both be returned? Only applies to mixed models. May be abbreviated.
Logical, if TRUE (default), includes parameters
for all studies. Else, only parameters for overall-effects are shown.
insight::standardize_names() to rename
columns into a consistent, standardized naming scheme.
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)
}
# \donttest{
if (require("multcomp", quietly = TRUE)) {
# multiple linear model, swiss data
lmod <- lm(Fertility ~ ., data = swiss)
mod <- glht(
model = lmod,
linfct = c(
"Agriculture = 0",
"Examination = 0",
"Education = 0",
"Catholic = 0",
"Infant.Mortality = 0"
)
)
model_parameters(mod)
}
if (require("PMCMRplus", quietly = TRUE)) {
model <- suppressWarnings(
kwAllPairsConoverTest(count ~ spray, data = InsectSprays)
)
model_parameters(model)
}
# }
if (require("WRS2") && packageVersion("WRS2") >= "1.1.3") {
model <- t1way(libido ~ dose, data = viagra)
model_parameters(model)
}
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