
Parameters from special models
Source:R/methods_PMCMRplus.R
, R/methods_aod.R
, R/methods_averaging.R
, and 33 more
model_parameters.averaging.Rd
Parameters from special regression models not listed under one of the previous categories yet.
Parameters from Hypothesis Testing.
Usage
# 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,
verbose = TRUE,
...
)
# S3 method for averaging
model_parameters(
model,
ci = 0.95,
component = c("conditional", "full"),
exponentiate = FALSE,
p_adjust = 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),
df_method = ci_method,
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,
verbose = TRUE,
...
)
# S3 method for bfsl
model_parameters(
model,
ci = 0.95,
ci_method = "residual",
p_adjust = 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 = c("pd", "rope"),
rope_range = "default",
rope_ci = 0.95,
exponentiate = FALSE,
p_adjust = NULL,
parameters = 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, parameters = NULL, verbose = TRUE, ...)
# S3 method for SemiParBIV
model_parameters(
model,
ci = 0.95,
bootstrap = FALSE,
iterations = 1000,
standardize = NULL,
exponentiate = FALSE,
p_adjust = 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,
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,
verbose = TRUE,
...
)
# S3 method for ivFixed
model_parameters(model, ci = 0.95, ci_method = "wald", verbose = TRUE, ...)
# S3 method for ivprobit
model_parameters(model, ci = 0.95, ci_method = "wald", verbose = TRUE, ...)
# S3 method for lmodel2
model_parameters(
model,
ci = 0.95,
exponentiate = FALSE,
p_adjust = 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),
df_method = ci_method,
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, ...)
# S3 method for comparisons
model_parameters(model, ci = 0.95, ...)
# S3 method for marginalmeans
model_parameters(model, ci = 0.95, ...)
# S3 method for deltamethod
model_parameters(model, ci = 0.95, ...)
# 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,
parameters = keep,
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,
parameters = keep,
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,
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,
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,
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,
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,
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,
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,
parameters = keep,
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, 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,
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,
parameters = keep,
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,
parameters = keep,
verbose = TRUE,
...
)
# S3 method for rqs
model_parameters(
model,
ci = 0.95,
bootstrap = FALSE,
iterations = 1000,
standardize = NULL,
exponentiate = FALSE,
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,
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),
df_method = ci_method,
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,
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, ...)
Arguments
- model
Object from
WRS2
package.- ...
Arguments passed to or from other methods.
- 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()
. 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 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 returned.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 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.- 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).- verbose
Toggle warnings and messages.
- 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.- summary
Logical, if
TRUE
, prints summary information about the model (model formula, number of observations, residual standard deviation and more).- df_method
Deprecated. Please use
ci_method
.- vcov
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"
.
- vcov_args
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.- centrality
The point-estimates (centrality indices) to compute. Character (vector) or list with one or more of these options:
"median"
,"mean"
,"MAP"
or"all"
.- dispersion
Logical, if
TRUE
, computes indices of dispersion related to the estimate(s) (SD
andMAD
formean
andmedian
, respectively).- test
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()
orp_direction()
) and its results included in the summary output.- rope_range
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 tox +- 0.1*SD(response)
.- rope_ci
The Credible Interval (CI) probability, corresponding to the proportion of HDI, to use for the percentage in ROPE.
- parameters
Deprecated, alias for
keep
.- effects
Should results for fixed effects, random effects or both be returned? Only applies to mixed models. May be abbreviated.
- 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
.- include_studies
Logical, if
TRUE
(default), includes parameters for all studies. Else, only parameters for overall-effects are shown.
Value
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.
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)
}
#> # 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.
# \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)
}
#>
#> Attaching package: ‘mvtnorm’
#> The following object is masked from ‘package:mclust’:
#>
#> dmvnorm
#>
#> Attaching package: ‘TH.data’
#> The following object is masked from ‘package:MASS’:
#>
#> geyser
#> # Fixed Effects
#>
#> Parameter | Coefficient | SE | 95% CI | t(41) | p
#> ----------------------------------------------------------------------------
#> Agriculture == 0 | -0.17 | 0.07 | [-0.36, 0.01] | -2.45 | 0.080
#> Examination == 0 | -0.26 | 0.25 | [-0.93, 0.41] | -1.02 | 0.785
#> Education == 0 | -0.87 | 0.18 | [-1.35, -0.39] | -4.76 | < .001
#> Catholic == 0 | 0.10 | 0.04 | [ 0.01, 0.20] | 2.95 | 0.023
#> Infant Mortality == 0 | 1.08 | 0.38 | [ 0.07, 2.09] | 2.82 | 0.033
#>
#> p-value adjustment method: single-step
#>
#>
#> Uncertainty intervals (equal-tailed) and p-values (two-tailed) computed
#> using a Wald t-distribution approximation.
if (require("PMCMRplus", quietly = TRUE)) {
model <- kwAllPairsConoverTest(count ~ spray, data = InsectSprays)
model_parameters(model)
}
#> Warning: Ties are present. Quantiles were corrected for ties.
#> Conover's all-pairs test
#>
#> Group1 | Group2 | Statistic | p | alternative | Distribution | p_adjustment
#> --------------------------------------------------------------------------------
#> B | A | 0.89 | 0.988 | two.sided | q | single-step
#> C | A | -13.58 | < .001 | two.sided | q | single-step
#> C | B | -14.47 | < .001 | two.sided | q | single-step
#> D | A | -8.87 | < .001 | two.sided | q | single-step
#> D | B | -9.76 | < .001 | two.sided | q | single-step
#> D | C | 4.71 | 0.017 | two.sided | q | single-step
#> E | A | -10.95 | < .001 | two.sided | q | single-step
#> E | B | -11.84 | < .001 | two.sided | q | single-step
#> E | C | 2.63 | 0.437 | two.sided | q | single-step
#> E | D | -2.09 | 0.681 | two.sided | q | single-step
#> F | A | 1.15 | 0.964 | two.sided | q | single-step
#> F | B | 0.26 | > .999 | two.sided | q | single-step
#> F | C | 14.74 | < .001 | two.sided | q | single-step
#> F | D | 10.02 | < .001 | two.sided | q | single-step
#> F | E | 12.11 | < .001 | two.sided | q | single-step
# }
if (require("WRS2") && packageVersion("WRS2") >= "1.1.3") {
model <- t1way(libido ~ dose, data = viagra)
model_parameters(model)
}
#> Loading required package: WRS2
#> A heteroscedastic one-way ANOVA for trimmed means
#>
#> F | df | df (error) | p | Estimate | 95% CI | Effectsize
#> ---------------------------------------------------------------------------------------------
#> 3.00 | 2 | 4 | 0.160 | 0.79 | [0.41, 1.43] | Explanatory measure of effect size