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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 also bootstrap_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" (for MuMIn::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' in 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".

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. 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).

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 in model_parameters() for further details. When ci_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 and MAD for mean and median, 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() or p_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 to x +- 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 (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.

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