Parameters from special models

Parameters from special regression models not listed under one of the previous categories yet.

Usage

# S3 method for class 'glimML'
model_parameters(
  model,
  ci = 0.95,
  bootstrap = FALSE,
  iterations = 1000,
  component = "conditional",
  standardize = NULL,
  exponentiate = FALSE,
  p_adjust = NULL,
  include_info = getOption("parameters_info", 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 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" (e.g. betareg), "scale" (e.g. ordinal), "extra" (e.g. glmx), "marginal" (e.g. mfx), "conditional" or "full" (for MuMIn::model.avg()) or "all". See section Model components for an overview of possible options for component.

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(). 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.
By default, the response (dependent) variable is also standardized, if applicable. Set include_response = FALSE to avoid standardization of the response variable. See details in datawizard::standardize.default().
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".

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. For models with a log-transformed response variable, when exponentiate = TRUE, a one-unit increase in the predictor is associated with multiplying the outcome by that predictor's coefficient. 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

String value, if not NULL, indicates the method to adjust p-values. See stats::p.adjust() for details. Further possible adjustment methods are "tukey", "scheffe", "sidak", "sup-t", and "none" to explicitly disable adjustment for emmGrid objects (from emmeans). "sup-t" computes simultaneous confidence bands, also called sup-t confidence band (Montiel Olea & Plagborg-Møller, 2019).

include_info

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 (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.

verbose

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().

Further non-documented arguments are:

digits, p_digits, ci_digits and footer_digits to set the number of digits for the output. groups can be used to group coefficients. These arguments will be passed to the print-method, or can directly be used in print(), see documentation in print.parameters_model().
If s_value = TRUE, the p-value will be replaced by the S-value in the output (cf. Rafi and Greenland 2020).
pd adds an additional column with the probability of direction (see bayestestR::p_direction() for details). Furthermore, see 'Examples' for this function.
For developers, whose interest mainly is to get a "tidy" data frame of model summaries, it is recommended to set pretty_names = FALSE to speed up computation of the summary table.

Value

A data frame of indices related to the model's parameters.

Model components

Possible values for the component argument depend on the model class. Following are valid options:

"all": returns all model components, applies to all models, but will only have an effect for models with more than just the conditional model component.
"conditional": only returns the conditional component, i.e. "fixed effects" terms from the model. Will only have an effect for models with more than just the conditional model component.
"smooth_terms": returns smooth terms, only applies to GAMs (or similar models that may contain smooth terms).
"zero_inflated" (or "zi"): returns the zero-inflation component.
"dispersion": returns the dispersion model component. This is common for models with zero-inflation or that can model the dispersion parameter.
"instruments": for instrumental-variable or some fixed effects regression, returns the instruments.
"nonlinear": for non-linear models (like models of class nlmerMod or nls), returns staring estimates for the nonlinear parameters.
"correlation": for models with correlation-component, like gls, the variables used to describe the correlation structure are returned.

Special models

Some model classes also allow rather uncommon options. These are:

mhurdle: "infrequent_purchase", "ip", and "auxiliary"
BGGM: "correlation" and "intercept"
BFBayesFactor, glmx: "extra"
averaging:"conditional" and "full"
mjoint: "survival"
mfx: "precision", "marginal"
betareg, DirichletRegModel: "precision"
mvord: "thresholds" and "correlation"
clm2: "scale"
selection: "selection", "outcome", and "auxiliary"
lavaan: One or more of "regression", "correlation", "loading", "variance", "defined", or "mean". Can also be "all" to include all components.

For models of class brmsfit (package brms), even more options are possible for the component argument, which are not all documented in detail here.

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.

Usage

Arguments

Value

Model components

See also

Examples