View source: R/1_model_parameters.R
model_parameters.default  R Documentation 
Extract and compute indices and measures to describe parameters of (general) linear models (GLMs).
## Default S3 method: 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, verbose = TRUE, vcov = NULL, vcov_args = NULL, ... ) ## S3 method for class 'glm' 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 class 'censReg' 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, verbose = TRUE, vcov = NULL, vcov_args = NULL, ... ) ## S3 method for class 'ridgelm' model_parameters(model, verbose = TRUE, ...) ## S3 method for class 'polr' 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 class 'negbin' 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 class 'svyglm' model_parameters( model, ci = 0.95, ci_method = "wald", bootstrap = FALSE, iterations = 1000, standardize = NULL, exponentiate = FALSE, p_adjust = NULL, verbose = TRUE, ... )
model 
Model object. 
ci 
Confidence Interval (CI) level. Default to 
ci_method 
Method for computing degrees of freedom for
confidence intervals (CI) and the related pvalues. Allowed are following
options (which vary depending on the model class): 
bootstrap 
Should estimates be based on bootstrapped model? If

iterations 
The number of bootstrap replicates. This only apply in the case of bootstrapped frequentist models. 
standardize 
The method used for standardizing the parameters. Can be

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 
p_adjust 
Character vector, if not 
summary 
Logical, if 
keep 
Character containing a regular expression pattern that
describes the parameters that should be included (for 
drop 
See 
verbose 
Toggle warnings and messages. 
vcov 
Variancecovariance 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.

vcov_args 
List of arguments to be passed to the function identified by
the 
... 
Arguments passed to or from other methods. For instance, when

df_method 
Deprecated. Please use 
A data frame of indices related to the model's parameters.
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 pvalues. 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 pvalues.
"wald"
:
Applies to nonBayesian models. For linear models, CIs computed using the Wald method (SE and a tdistribution with residual df); pvalues computed using the Wald method with a tdistribution with residual df. For other models, CIs computed using the Wald method (SE and a normal distribution); pvalues computed using the Wald method with a normal distribution.
"normal"
Applies to nonBayesian models. Compute Wald CIs and pvalues, but always use a normal distribution.
"residual"
Applies to nonBayesian models. Compute Wald CIs and pvalues, but always use a tdistribution 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 singlelevel) models, determining appropriate df for Waldbased inference in mixed models is more difficult. See the R GLMM FAQ 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 pvalues instead.
"satterthwaite"
Applies to linear mixed models. CIs computed using the Wald method (SE and a tdistribution with Satterthwaite df); pvalues computed using the Wald method with a tdistribution with Satterthwaite df.
"kenward"
Applies to linear mixed models. CIs computed using the Wald method (KenwardRoger SE and a tdistribution with KenwardRoger df); pvalues computed using the Wald method with KenwardRoger SE and tdistribution with KenwardRoger df.
"ml1"
Applies to linear mixed models. CIs computed using the Wald
method (SE and a tdistribution with ml1 approximated df); pvalues
computed using the Wald method with a tdistribution with ml1 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 tdistribution with betweenwithin df);
pvalues computed using the Wald method with a tdistribution with betweenwithin df.
See ci_betwithin()
.
Likelihoodbased methods:
Likelihoodbased inference is based on comparing the likelihood for the maximumlikelihood 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 maximumlikelihood and alternative models are compared to a χsquared distribution to compute CIs and pvalues.
"profile"
Applies to nonBayesian 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;
pvalues computed using the Wald method with a normaldistribution (note:
this might change in a future update!)
"uniroot"
Applies to nonBayesian 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; pvalues
computed using the Wald method with a normaldistribution (note: this
might change in a future update!)
Methods for bootstrapped or Bayesian models:
Bootstrapbased 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 pvalues 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 nonBayesian models, only applies if bootstrap = TRUE
. CIs computed
as equal tailed intervals using the quantiles of the bootstrap or
posterior samples; pvalues are based on the probability of direction.
See bayestestR::eti()
.
"hdi"
Applies to all models (including Bayesian models). For nonBayesian
models, only applies if bootstrap = TRUE
. CIs computed as highest density intervals
for the bootstrap or posterior samples; pvalues are based on the probability of direction.
See bayestestR::hdi()
.
"bci"
(or "bcai"
)
Applies to all models (including Bayesian models).
For nonBayesian models, only applies if bootstrap = TRUE
. CIs computed
as bias corrected and accelerated intervals for the bootstrap or
posterior samples; pvalues 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;
pvalues are based on the probability of direction. See bayestestR::si()
.
"boot"
Applies to nonBayesian models of class merMod
. CIs computed
using parametric bootstrapping (simulating data from the fitted model);
pvalues computed using the Wald method with a normaldistribution)
(note: this might change in a future update!).
For all iterationbased methods other than "boot"
("hdi"
, "quantile"
, "ci"
, "eti"
, "si"
, "bci"
, "bcai"
),
pvalues are based on the probability of direction (bayestestR::p_direction()
),
which is converted into a pvalue using bayestestR::pd_to_p()
.
insight::standardize_names()
to
rename columns into a consistent, standardized naming scheme.
library(parameters) model < lm(mpg ~ wt + cyl, data = mtcars) model_parameters(model) # bootstrapped parameters if (require("boot", quietly = TRUE)) { model_parameters(model, bootstrap = TRUE) } # standardized parameters model_parameters(model, standardize = "refit") # robust, heteroskedasticityconsistent standard errors if (require("sandwich") && require("clubSandwich")) { model_parameters(model, vcov = "HC3") model_parameters(model, vcov = "vcovCL", vcov_args = list(cluster = mtcars$cyl) ) } # different pvalue style in output model_parameters(model, p_digits = 5) model_parameters(model, digits = 3, ci_digits = 4, p_digits = "scientific") # 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)
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