Nothing
# Arguments passed to or from other methods. For instance, when default methods, glm (almost default)
#################### .default ----------------------
#' Model Parameters
#'
#' Compute and extract model parameters. The available options and arguments depend
#' on the modeling **package** and model `class`. Follow one of these links to read
#' the model-specific documentation:
#' - [Default method][model_parameters.default()]: `lm`, `glm`, **stats**, **censReg**,
#' **MASS**, **survey**, ...
#' - [Additive models][model_parameters.cgam()]: **bamlss**, **gamlss**, **mgcv**,
#' **scam**, **VGAM**, `Gam`, `gamm`, ...
#' - [ANOVA][model_parameters.aov()]: **afex**, `aov`, `anova`, ...
#' - [Bayesian][model_parameters.stanreg()]: **BayesFactor**, **blavaan**, **brms**,
#' **MCMCglmm**, **posterior**, **rstanarm**, `bayesQR`, `bcplm`, `BGGM`, `blmrm`,
#' `blrm`, `mcmc.list`, `MCMCglmm`, ...
#' - [Clustering][model_parameters.kmeans()]: **hclust**, **kmeans**, **mclust**, **pam**, ...
#' - [Correlations, t-tests, etc.][model_parameters.htest()]: **lmtest**, `htest`,
#' `pairwise.htest`, ...
#' - [Meta-Analysis][model_parameters.rma()]: **metaBMA**, **metafor**, **metaplus**, ...
#' - [Mixed models][model_parameters.merMod()]: **cplm**, **glmmTMB**, **lme4**,
#' **lmerTest**, **nlme**, **ordinal**, **robustlmm**, **spaMM**, `mixed`, `MixMod`, ...
#' - [Multinomial, ordinal and cumulative link][model_parameters.mlm()]: **brglm2**,
#' **DirichletReg**, **nnet**, **ordinal**, `mlm`, ...
#' - [Multiple imputation][model_parameters.mira()]: **mice**
#' - [PCA, FA, CFA, SEM][model_parameters.principal()]: **FactoMineR**, **lavaan**,
#' **psych**, `sem`, ...
#' - [Zero-inflated and hurdle][model_parameters.zcpglm()]: **cplm**, **mhurdle**,
#' **pscl**, ...
#' - [Other models][model_parameters.averaging()]: **aod**, **bbmle**, **betareg**,
#' **emmeans**, **epiR**, **ggeffects**, **glmx**, **ivfixed**, **ivprobit**,
#' **JRM**, **lmodel2**, **logitsf**, **marginaleffects**, **margins**, **maxLik**,
#' **mediation**, **mfx**, **multcomp**, **mvord**, **plm**, **PMCMRplus**,
#' **quantreg**, **selection**, **systemfit**, **tidymodels**, **varEST**,
#' **WRS2**, `bfsl`, `deltaMethod`, `fitdistr`, `mjoint`, `mle`, `model.avg`, ...
#'
#' @param model Statistical Model.
#' @param ... Arguments passed to or from other methods. Non-documented
#' arguments are `digits`, `p_digits`, `ci_digits` and `footer_digits` to set
#' the number of digits for the output. 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). `groups` can be used to group
#' coefficients. It will be passed to the print-method, or can directly be used
#' in `print()`, see documentation in [print.parameters_model()]. Furthermore,
#' see 'Examples' in [model_parameters.default()]. 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.
#'
#' @seealso [insight::standardize_names()] to
#' rename columns into a consistent, standardized naming scheme.
#'
#' @note The [`print()`][print.parameters_model] method has several
#' arguments to tweak the output. There is also a
#' [`plot()`-method](https://easystats.github.io/see/articles/parameters.html)
#' implemented in the
#' [**see**-package](https://easystats.github.io/see/), and a dedicated
#' method for use inside rmarkdown files,
#' [`print_md()`][print_md.parameters_model]. \cr \cr **For developers**, if
#' speed performance is an issue, you can use the (undocumented) `pretty_names`
#' argument, e.g. `model_parameters(..., pretty_names = FALSE)`. This will
#' skip the formatting of the coefficient names and make `model_parameters()`
#' faster.
#'
#' @section Standardization of model coefficients:
#' Standardization is based on [standardize_parameters()]. In case
#' of `standardize = "refit"`, the data used to fit the model will be
#' standardized and the model is completely refitted. In such cases, standard
#' errors and confidence intervals refer to the standardized coefficient. The
#' default, `standardize = "refit"`, never standardizes categorical predictors
#' (i.e. factors), which may be a different behaviour compared to other R
#' packages or other software packages (like SPSS). To mimic behaviour of SPSS
#' or packages such as **lm.beta**, use `standardize = "basic"`.
#'
#' @section
#'
#' Standardization Methods:
#'
#' - **refit**: This method is based on a complete model re-fit with a
#' standardized version of the data. Hence, this method is equal to
#' standardizing the variables before fitting the model. It is the "purest" and
#' the most accurate (Neter et al., 1989), but it is also the most
#' computationally costly and long (especially for heavy models such as Bayesian
#' models). This method is particularly recommended for complex models that
#' include interactions or transformations (e.g., polynomial or spline terms).
#' The `robust` (default to `FALSE`) argument enables a robust standardization
#' of data, i.e., based on the `median` and `MAD` instead of the `mean` and
#' `SD`. **See [standardize()] for more details.**
#' **Note** that `standardize_parameters(method = "refit")` may not return
#' the same results as fitting a model on data that has been standardized with
#' `standardize()`; `standardize_parameters()` used the data used by the model
#' fitting function, which might not be same data if there are missing values.
#' see the `remove_na` argument in `standardize()`.
#'
#' - **posthoc**: Post-hoc standardization of the parameters, aiming at
#' emulating the results obtained by "refit" without refitting the model. The
#' coefficients are divided by the standard deviation (or MAD if `robust`) of
#' the outcome (which becomes their expression 'unit'). Then, the coefficients
#' related to numeric variables are additionally multiplied by the standard
#' deviation (or MAD if `robust`) of the related terms, so that they correspond
#' to changes of 1 SD of the predictor (e.g., "A change in 1 SD of `x` is
#' related to a change of 0.24 of the SD of `y`). This does not apply to binary
#' variables or factors, so the coefficients are still related to changes in
#' levels. This method is not accurate and tend to give aberrant results when
#' interactions are specified.
#'
#' - **basic**: This method is similar to `method = "posthoc"`, but treats all
#' variables as continuous: it also scales the coefficient by the standard
#' deviation of model's matrix' parameter of factors levels (transformed to
#' integers) or binary predictors. Although being inappropriate for these cases,
#' this method is the one implemented by default in other software packages,
#' such as [lm.beta::lm.beta()].
#'
#' - **smart** (Standardization of Model's parameters with Adjustment,
#' Reconnaissance and Transformation - *experimental*): Similar to `method =
#' "posthoc"` in that it does not involve model refitting. The difference is
#' that the SD (or MAD if `robust`) of the response is computed on the relevant
#' section of the data. For instance, if a factor with 3 levels A (the
#' intercept), B and C is entered as a predictor, the effect corresponding to B
#' vs. A will be scaled by the variance of the response at the intercept only.
#' As a results, the coefficients for effects of factors are similar to a Glass'
#' delta.
#'
#' - **pseudo** (*for 2-level (G)LMMs only*): In this (post-hoc) method, the
#' response and the predictor are standardized based on the level of prediction
#' (levels are detected with [performance::check_heterogeneity_bias()]): Predictors
#' are standardized based on their SD at level of prediction (see also
#' [datawizard::demean()]); The outcome (in linear LMMs) is standardized based
#' on a fitted random-intercept-model, where `sqrt(random-intercept-variance)`
#' is used for level 2 predictors, and `sqrt(residual-variance)` is used for
#' level 1 predictors (Hoffman 2015, page 342). A warning is given when a
#' within-group variable is found to have access between-group variance.
#'
#' See also [package vignette](https://easystats.github.io/parameters/articles/standardize_parameters_effsize.html).
#'
#' @section Labeling the Degrees of Freedom:
#' Throughout the **parameters** package, we decided to label the residual
#' degrees of freedom *df_error*. The reason for this is that these degrees
#' of freedom not always refer to the residuals. For certain models, they refer
#' to the estimate error - in a linear model these are the same, but in - for
#' instance - any mixed effects model, this isn't strictly true. Hence, we
#' think that `df_error` is the most generic label for these degrees of
#' freedom.
#'
#' @section Confidence intervals and approximation of degrees of freedom:
#' There are different ways of approximating the degrees of freedom depending
#' on different assumptions about the nature of the model and its sampling
#' distribution. The `ci_method` argument modulates the method for computing degrees
#' of freedom (df) that are used to calculate confidence intervals (CI) and the
#' related p-values. Following options are allowed, depending on the model
#' class:
#'
#' **Classical methods:**
#'
#' Classical inference is generally based on the **Wald method**.
#' The Wald approach to inference computes a test statistic by dividing the
#' parameter estimate by its standard error (Coefficient / SE),
#' then comparing this statistic against a t- or normal distribution.
#' This approach can be used to compute CIs and p-values.
#'
#' `"wald"`:
#' - Applies to *non-Bayesian models*. For *linear models*, CIs
#' computed using the Wald method (SE and a *t-distribution with residual df*);
#' p-values computed using the Wald method with a *t-distribution with residual df*.
#' For other models, CIs computed using the Wald method (SE and a *normal distribution*);
#' p-values computed using the Wald method with a *normal distribution*.
#'
#' `"normal"`
#' - Applies to *non-Bayesian models*. Compute Wald CIs and p-values,
#' but always use a normal distribution.
#'
#' `"residual"`
#' - Applies to *non-Bayesian models*. Compute Wald CIs and p-values,
#' but always use a *t-distribution with residual df* when possible. If the
#' residual df for a model cannot be determined, a normal distribution is
#' used instead.
#'
#' **Methods for mixed models:**
#'
#' Compared to fixed effects (or single-level) models, determining appropriate
#' df for Wald-based inference in mixed models is more difficult.
#' See [the R GLMM FAQ](https://bbolker.github.io/mixedmodels-misc/glmmFAQ.html#what-are-the-p-values-listed-by-summaryglmerfit-etc.-are-they-reliable)
#' for a discussion.
#'
#' Several approximate methods for computing df are available, but you should
#' also consider instead using profile likelihood (`"profile"`) or bootstrap ("`boot"`)
#' CIs and p-values instead.
#'
#' `"satterthwaite"`
#' - Applies to *linear mixed models*. CIs computed using the
#' Wald method (SE and a *t-distribution with Satterthwaite df*); p-values
#' computed using the Wald method with a *t-distribution with Satterthwaite df*.
#'
#' `"kenward"`
#' - Applies to *linear mixed models*. CIs computed using the Wald
#' method (*Kenward-Roger SE* and a *t-distribution with Kenward-Roger df*);
#' p-values computed using the Wald method with *Kenward-Roger SE and t-distribution with Kenward-Roger df*.
#'
#' `"ml1"`
#' - Applies to *linear mixed models*. CIs computed using the Wald
#' method (SE and a *t-distribution with m-l-1 approximated df*); p-values
#' computed using the Wald method with a *t-distribution with m-l-1 approximated df*.
#' See [`ci_ml1()`].
#'
#' `"betwithin"`
#' - Applies to *linear mixed models* and *generalized linear mixed models*.
#' CIs computed using the Wald method (SE and a *t-distribution with between-within df*);
#' p-values computed using the Wald method with a *t-distribution with between-within df*.
#' See [`ci_betwithin()`].
#'
#' **Likelihood-based methods:**
#'
#' Likelihood-based inference is based on comparing the likelihood for the
#' maximum-likelihood estimate to the the likelihood for models with one or more
#' parameter values changed (e.g., set to zero or a range of alternative values).
#' Likelihood ratios for the maximum-likelihood and alternative models are compared
#' to a \eqn{\chi}-squared distribution to compute CIs and p-values.
#'
#' `"profile"`
#' - Applies to *non-Bayesian models* of class `glm`, `polr`, `merMod` or `glmmTMB`.
#' CIs computed by *profiling the likelihood curve for a parameter*, using
#' linear interpolation to find where likelihood ratio equals a critical value;
#' p-values computed using the Wald method with a *normal-distribution* (note:
#' this might change in a future update!)
#'
#' `"uniroot"`
#' - Applies to *non-Bayesian models* of class `glmmTMB`. CIs
#' computed by *profiling the likelihood curve for a parameter*, using root
#' finding to find where likelihood ratio equals a critical value; p-values
#' computed using the Wald method with a *normal-distribution* (note: this
#' might change in a future update!)
#'
#' **Methods for bootstrapped or Bayesian models:**
#'
#' Bootstrap-based inference is based on **resampling** and refitting the model
#' to the resampled datasets. The distribution of parameter estimates across
#' resampled datasets is used to approximate the parameter's sampling
#' distribution. Depending on the type of model, several different methods for
#' bootstrapping and constructing CIs and p-values from the bootstrap
#' distribution are available.
#'
#' For Bayesian models, inference is based on drawing samples from the model
#' posterior distribution.
#'
#' `"quantile"` (or `"eti"`)
#' - Applies to *all models (including Bayesian models)*.
#' For non-Bayesian models, only applies if `bootstrap = TRUE`. CIs computed
#' as *equal tailed intervals* using the quantiles of the bootstrap or
#' posterior samples; p-values are based on the *probability of direction*.
#' See [`bayestestR::eti()`].
#'
#' `"hdi"`
#' - Applies to *all models (including Bayesian models)*. For non-Bayesian
#' models, only applies if `bootstrap = TRUE`. CIs computed as *highest density intervals*
#' for the bootstrap or posterior samples; p-values are based on the *probability of direction*.
#' See [`bayestestR::hdi()`].
#'
#' `"bci"` (or `"bcai"`)
#' - Applies to *all models (including Bayesian models)*.
#' For non-Bayesian models, only applies if `bootstrap = TRUE`. CIs computed
#' as *bias corrected and accelerated intervals* for the bootstrap or
#' posterior samples; p-values are based on the *probability of direction*.
#' See [`bayestestR::bci()`].
#'
#' `"si"`
#' - Applies to *Bayesian models* with proper priors. CIs computed as
#' *support intervals* comparing the posterior samples against the prior samples;
#' p-values are based on the *probability of direction*. See [`bayestestR::si()`].
#'
#' `"boot"`
#' - Applies to *non-Bayesian models* of class `merMod`. CIs computed
#' using *parametric bootstrapping* (simulating data from the fitted model);
#' p-values computed using the Wald method with a *normal-distribution)*
#' (note: this might change in a future update!).
#'
#' For all iteration-based methods other than `"boot"`
#' (`"hdi"`, `"quantile"`, `"ci"`, `"eti"`, `"si"`, `"bci"`, `"bcai"`),
#' p-values are based on the probability of direction ([`bayestestR::p_direction()`]),
#' which is converted into a p-value using [`bayestestR::pd_to_p()`].
#'
#' @inheritSection format_parameters Interpretation of Interaction Terms
#' @inheritSection print.parameters_model Global Options to Customize Messages and Tables when Printing
#'
#' @references
#'
#' - Hoffman, L. (2015). Longitudinal analysis: Modeling within-person
#' fluctuation and change. Routledge.
#'
#' - Neter, J., Wasserman, W., & Kutner, M. H. (1989). Applied linear
#' regression models.
#'
#' - Rafi Z, Greenland S. Semantic and cognitive tools to aid statistical
#' science: replace confidence and significance by compatibility and surprise.
#' BMC Medical Research Methodology (2020) 20:244.
#' @return A data frame of indices related to the model's parameters.
#' @export
model_parameters <- function(model, ...) {
UseMethod("model_parameters")
}
# DF naming convention --------------------
# DF column naming
# F has df, df_error
# t has df_error
# z has df_error = Inf
# Chisq has df
# https://github.com/easystats/parameters/issues/455
# Options -------------------------------------
# Add new options to the docs in "print.parameters_model"
# getOption("parameters_summary"): show model summary
# getOption("parameters_mixed_summary"): show model summary for mixed models
# getOption("parameters_cimethod"): show message about CI approximation
# getOption("parameters_exponentiate"): show warning about exp for log/logit links
# getOption("parameters_labels"): use value/variable labels instead pretty names
# getOption("parameters_interaction"): separator char for interactions
# getOption("parameters_select"): default for the `select` argument
#' @rdname model_parameters
#' @export
parameters <- model_parameters
#' Parameters from (General) Linear Models
#'
#' Extract and compute indices and measures to describe parameters of (general)
#' linear models (GLMs).
#'
#' @param model Model object.
#' @param ci Confidence Interval (CI) level. Default to `0.95` (`95%`).
#' @param bootstrap Should estimates be based on bootstrapped model? If
#' `TRUE`, then arguments of [Bayesian
#' regressions][model_parameters.stanreg] apply (see also
#' [`bootstrap_parameters()`]).
#' @param iterations The number of bootstrap replicates. This only apply in the
#' case of bootstrapped frequentist models.
#' @param 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.
#' - 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"`.
#' @param 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. For `compare_parameters()`,
#' `exponentiate = "nongaussian"` will only exponentiate coefficients from
#' non-Gaussian families.
#' @param 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**).
#' @param 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.
#' @param summary Logical, if `TRUE`, prints summary information about the
#' model (model formula, number of observations, residual standard deviation
#' and more).
#' @param 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
#' `$Parameter` column of the parameters table to get the exact parameter
#' names.
#' @param ... Arguments passed to or from other methods. For instance, when
#' `bootstrap = TRUE`, arguments like `type` or `parallel` are
#' passed down to `bootstrap_model()`.
#' @param drop See `keep`.
#' @param verbose Toggle warnings and messages.
#' @inheritParams standard_error
#'
#' @seealso [`insight::standardize_names()`] to
#' rename columns into a consistent, standardized naming scheme.
#'
#' @inheritSection model_parameters Confidence intervals and approximation of degrees of freedom
#'
#' @examplesIf require("boot", quietly = TRUE) && require("sandwich") && require("clubSandwich") && require("brglm2")
#' library(parameters)
#' model <- lm(mpg ~ wt + cyl, data = mtcars)
#'
#' model_parameters(model)
#'
#' # bootstrapped parameters
#' model_parameters(model, bootstrap = TRUE)
#'
#' # standardized parameters
#' model_parameters(model, standardize = "refit")
#'
#' # robust, heteroskedasticity-consistent standard errors
#' model_parameters(model, vcov = "HC3")
#'
#' model_parameters(model,
#' vcov = "vcovCL",
#' vcov_args = list(cluster = mtcars$cyl)
#' )
#'
#' # different p-value style in output
#' model_parameters(model, p_digits = 5)
#' model_parameters(model, digits = 3, ci_digits = 4, p_digits = "scientific")
#' \donttest{
#' # logistic regression model
#' model <- glm(vs ~ wt + cyl, data = mtcars, family = "binomial")
#' model_parameters(model)
#'
#' # show odds ratio / exponentiated coefficients
#' model_parameters(model, exponentiate = TRUE)
#'
#' # bias-corrected logistic regression with penalized maximum likelihood
#' model <- glm(
#' vs ~ wt + cyl,
#' data = mtcars,
#' family = "binomial",
#' method = "brglmFit"
#' )
#' model_parameters(model)
#' }
#' @return A data frame of indices related to the model's parameters.
#' @export
model_parameters.default <- function(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,
...) {
# validation check for inputs
.is_model_valid(model)
# validation check, warn if unsupported argument is used.
# unsupported arguments will be removed from the argument list.
dots <- .check_dots(
dots = list(...),
not_allowed = c("include_sigma", "wb_component"),
class(model)[1],
verbose = FALSE
)
# extract model parameters table, as data frame
out <- tryCatch(
{
.model_parameters_generic(
model = model,
ci = ci,
ci_method = ci_method,
bootstrap = bootstrap,
iterations = iterations,
merge_by = "Parameter",
standardize = standardize,
exponentiate = exponentiate,
p_adjust = p_adjust,
summary = summary,
keep_parameters = keep,
drop_parameters = drop,
vcov = vcov,
vcov_args = vcov_args,
verbose = verbose,
...
)
},
error = function(e) {
fail <- NA
attr(fail, "error") <- gsub(" ", " ", gsub("\\n", "", e$message), fixed = TRUE)
fail
}
)
# tell user if something went wrong...
if (length(out) == 1 && isTRUE(is.na(out))) {
insight::format_error(
paste0(
"Sorry, `model_parameters()` failed with the following error (possible class `",
class(model)[1],
"` not supported):\n"
),
attr(out, "error")
)
} else if (is.null(out)) {
insight::format_error(
paste0(
"Sorry, `model_parameters()` does not currently work for objects of class `",
class(model)[1],
"`."
)
)
}
attr(out, "object_name") <- insight::safe_deparse_symbol(substitute(model))
out
}
# helper function for the composition of the parameters table,
# including a bunch of attributes required for further processing
# (like printing etc.)
.model_parameters_generic <- function(model,
ci = 0.95,
bootstrap = FALSE,
iterations = 1000,
merge_by = "Parameter",
standardize = NULL,
exponentiate = FALSE,
effects = "fixed",
component = "conditional",
ci_method = NULL,
p_adjust = NULL,
summary = FALSE,
keep_parameters = NULL,
drop_parameters = NULL,
verbose = TRUE,
vcov = NULL,
vcov_args = NULL,
...) {
dots <- list(...)
# ==== 1. first step, extracting (bootstrapped) model parameters -------
# Processing, bootstrapped parameters
if (bootstrap) {
# set default method for bootstrapped CI
if (is.null(ci_method) || missing(ci_method)) {
ci_method <- "quantile"
}
args <- list(
model,
iterations = iterations,
ci = ci,
ci_method = ci_method
)
args <- c(args, dots)
params <- do.call("bootstrap_parameters", args)
# Processing, non-bootstrapped parameters
} else {
# set default method for CI
if (is.null(ci_method) || missing(ci_method)) {
ci_method <- "wald"
}
args <- list(
model,
ci = ci,
component = component,
merge_by = merge_by,
standardize = standardize,
effects = effects,
ci_method = ci_method,
p_adjust = p_adjust,
keep_parameters = keep_parameters,
drop_parameters = drop_parameters,
verbose = verbose,
vcov = vcov,
vcov_args = vcov_args
)
args <- c(args, dots)
params <- do.call(".extract_parameters_generic", args)
}
# ==== 2. second step, exponentiate -------
# exponentiate coefficients and SE/CI, if requested
params <- .exponentiate_parameters(params, model, exponentiate)
# ==== 3. third step, add information as attributes -------
# add further information as attributes
params <- .add_model_parameters_attributes(
params,
model,
ci,
exponentiate,
bootstrap,
iterations,
ci_method = ci_method,
p_adjust = p_adjust,
summary = summary,
verbose = verbose,
...
)
class(params) <- c("parameters_model", "see_parameters_model", class(params))
params
}
#################### .glm ----------------------
#' @rdname model_parameters.default
#' @export
model_parameters.glm <- function(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,
...) {
dots <- list(...)
# set default
if (is.null(ci_method)) {
if (isTRUE(bootstrap)) {
ci_method <- "quantile"
} else if (!is.null(vcov) || !is.null(vcov_args)) {
ci_method <- "wald"
} else {
ci_method <- "profile"
}
}
# profiled CIs may take a long time to compute, so we warn the user about it
if (insight::n_obs(model) > 1e4 && identical(ci_method, "profile")) {
insight::format_alert(
"Profiled confidence intervals may take longer time to compute.",
"Use `ci_method=\"wald\"` for faster computation of CIs."
)
}
# tell user that profiled CIs don't respect vcov-args
if (identical(ci_method, "profile") && (!is.null(vcov) || !is.null(vcov_args)) && isTRUE(verbose)) {
insight::format_alert(
"When `ci_method=\"profile\"`, `vcov` only modifies standard errors, test-statistic and p-values, but not confidence intervals.",
"Use `ci_method=\"wald\"` to return confidence intervals based on robust standard errors."
)
}
args <- list(
model = model,
ci = ci,
ci_method = ci_method,
bootstrap = bootstrap,
iterations = iterations,
merge_by = "Parameter",
standardize = standardize,
exponentiate = exponentiate,
p_adjust = p_adjust,
summary = summary,
keep_parameters = keep,
drop_parameters = drop,
vcov = vcov,
vcov_args = vcov_args,
verbose = verbose
)
args <- c(args, dots)
out <- do.call(".model_parameters_generic", args)
attr(out, "object_name") <- insight::safe_deparse_symbol(substitute(model))
out
}
#' @export
model_parameters.zoo <- model_parameters.default
Any scripts or data that you put into this service are public.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.