
Parameters from special models
Source:R/methods_aod.R
, R/methods_averaging.R
, R/methods_betareg.R
, and 8 more
model_parameters.averaging.Rd
Parameters from special regression models not listed under one of the previous categories yet.
Usage
# 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 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 emm_list
model_parameters(
model,
ci = 0.95,
exponentiate = FALSE,
p_adjust = 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 marginaleffects
model_parameters(model, ci = 0.95, exponentiate = FALSE, ...)
# 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_bma
model_parameters(
model,
ci = 0.95,
ci_method = "eti",
exponentiate = FALSE,
include_studies = TRUE,
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 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 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,
...
)
Arguments
- model
Model object.
- ci
Confidence Interval (CI) level. Default to
0.95
(95%
).- bootstrap
Should estimates be based on bootstrapped model? If
TRUE
, then arguments of Bayesian regressions apply (see alsobootstrap_parameters()
).- iterations
The number of bootstrap replicates. This only apply in the case of bootstrapped frequentist models.
- component
Model component for which parameters should be shown. May be one of
"conditional"
,"precision"
(betareg),"scale"
(ordinal),"extra"
(glmx),"marginal"
(mfx),"conditional"
or"full"
(forMuMIn::model.avg()
) or"all"
.- standardize
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' instandardize_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 usestandardize="basic"
or standardize the data withdatawizard::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 thanNULL
) of standardized parameters only works whenstandardize="refit"
.
- exponentiate
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. Forcompare_parameters()
,exponentiate = "nongaussian"
will only exponentiate coefficients from non-Gaussian families.- p_adjust
Character vector, if not
NULL
, indicates the method to adjust p-values. Seestats::p.adjust()
for details. Further possible adjustment methods are"tukey"
,"scheffe"
,"sidak"
and"none"
to explicitly disable adjustment foremmGrid
objects (from emmeans).- summary
Logical, if
TRUE
, prints summary information about the model (model formula, number of observations, residual standard deviation and more).- keep
Character containing a regular expression pattern that describes the parameters that should be included (for
keep
) or excluded (fordrop
) 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. Ifkeep
is a character vector, every parameter name in the "Parameter" column that matches the regular expression inkeep
will be selected from the returned data frame (and vice versa, all parameter names matchingdrop
will be excluded). Furthermore, ifkeep
has more than one element, these will be merged with anOR
operator into a regular expression pattern like this:"(one|two|three)"
. Ifkeep
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 wheremodel_parameters()
returns multiple columns with parameter components, like inmodel_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.- drop
See
keep
.- verbose
Toggle warnings and messages.
- ...
Arguments passed to or from other methods. For instance, when
bootstrap = TRUE
, arguments liketype
orparallel
are passed down tobootstrap_model()
.- include_studies
Logical, if
TRUE
(default), includes parameters for all studies. Else, only parameters for overall-effects are shown.- ci_method
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 inmodel_parameters()
for further details. Whenci_method=NULL
, in most cases"wald"
is used then.- effects
Should results for fixed effects, random effects or both be returned? Only applies to mixed models. May be abbreviated.
See also
insight::standardize_names()
to rename
columns into a consistent, standardized naming scheme.
Examples
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)
}
#>
#> Attaching package: ‘brglm2’
#> The following object is masked from ‘package:boot’:
#>
#> aids
#> # Response level: definitely
#>
#> Parameter | Log-Odds | SE | 95% CI | z | p
#> -------------------------------------------------------------------
#> (Intercept) | -1.25 | 0.26 | [-1.76, -0.73] | -4.76 | < .001
#> religion | 0.44 | 0.10 | [ 0.23, 0.64] | 4.20 | < .001
#> gender [female] | -0.14 | 0.17 | [-0.47, 0.19] | -0.82 | 0.414
#>
#> # Response level: probably
#>
#> Parameter | Log-Odds | SE | 95% CI | z | p
#> ----------------------------------------------------------------
#> (Intercept) | 0.47 | 0.29 | [-0.10, 1.04] | 1.62 | 0.105
#> religion | 0.26 | 0.13 | [ 0.01, 0.51] | 2.01 | 0.044
#> gender [female] | 0.19 | 0.21 | [-0.22, 0.60] | 0.90 | 0.370
#>
#> # Response level: probably not
#>
#> Parameter | Log-Odds | SE | 95% CI | z | p
#> -----------------------------------------------------------------
#> (Intercept) | 0.43 | 0.39 | [-0.33, 1.18] | 1.11 | 0.268
#> religion | 0.01 | 0.17 | [-0.33, 0.35] | 0.07 | 0.945
#> gender [female] | -0.16 | 0.28 | [-0.71, 0.39] | -0.57 | 0.566
#>
#> Uncertainty intervals (equal-tailed) and p-values (two-tailed) computed
#> using a Wald z-distribution approximation.
#>
#> The model has a log- or logit-link. Consider using `exponentiate =
#> TRUE` to interpret coefficients as ratios.