model_parameters.cpglmm: Parameters from Mixed Models

Description

Parameters from (linear) mixed models.

Usage

# S3 method for cpglmm
model_parameters(
  model,
  ci = 0.95,
  bootstrap = FALSE,
  iterations = 1000,
  standardize = NULL,
  effects = "all",
  group_level = FALSE,
  exponentiate = FALSE,
  ci_method = NULL,
  p_adjust = NULL,
  verbose = TRUE,
  df_method = ci_method,
  ...
)
# S3 method for glmmTMB
model_parameters(
  model,
  ci = 0.95,
  bootstrap = FALSE,
  iterations = 1000,
  effects = "all",
  component = "all",
  group_level = FALSE,
  standardize = NULL,
  exponentiate = FALSE,
  ci_method = NULL,
  p_adjust = NULL,
  wb_component = TRUE,
  summary = FALSE,
  keep = NULL,
  drop = NULL,
  parameters = keep,
  verbose = TRUE,
  df_method = ci_method,
  ...
)
# S3 method for merMod
model_parameters(
  model,
  ci = 0.95,
  bootstrap = FALSE,
  ci_method = NULL,
  iterations = 1000,
  standardize = NULL,
  effects = "all",
  group_level = FALSE,
  exponentiate = FALSE,
  robust = FALSE,
  p_adjust = NULL,
  wb_component = TRUE,
  summary = FALSE,
  keep = NULL,
  drop = NULL,
  parameters = keep,
  verbose = TRUE,
  df_method = ci_method,
  ...
)
# S3 method for mixor
model_parameters(
  model,
  ci = 0.95,
  effects = "all",
  bootstrap = FALSE,
  iterations = 1000,
  standardize = NULL,
  exponentiate = FALSE,
  verbose = TRUE,
  ...
)
# S3 method for clmm
model_parameters(
  model,
  ci = 0.95,
  bootstrap = FALSE,
  iterations = 1000,
  standardize = NULL,
  effects = "all",
  group_level = FALSE,
  exponentiate = FALSE,
  ci_method = NULL,
  p_adjust = NULL,
  verbose = TRUE,
  df_method = ci_method,
  ...
)

Arguments

model

A mixed model.

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 draws to simulate/bootstrap.

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 effectsize::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 effectsize::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. robust=TRUE) of standardized parameters only works when standardize="refit".

effects

Should parameters for fixed effects ("fixed"), random effects ("random"), or both ("all") be returned? Only applies to mixed models. May be abbreviated.

group_level

Logical, for multilevel models (i.e. models with random effects) and when effects = "all" or effects = "random", include the parameters for each group level from random effects. If group_level = FALSE (the default), only information on SD and COR are shown.

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.

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.

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.

df_method

Deprecated. Please use ci_method.

...

Arguments passed to or from other methods.

component

Should all parameters, parameters for the conditional model, or for the zero-inflated part of the model be returned? Applies to models with zero-inflated component. component may be one of "conditional", "zi", "zero-inflated", "dispersion" or "all" (default). May be abbreviated.

wb_component

Logical, if TRUE and models contains within- and between-effects (see datawizard::demean()), the Component column will indicate which variables belong to the within-effects, between-effects, and cross-level interactions. By default, the Component column indicates, which parameters belong to the conditional or zero-inflated component of the model.

summary

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 parameters 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

parameters

Deprecated, alias for keep.

robust

Logical, if TRUE, robust standard errors are calculated (if possible), and confidence intervals and p-values are based on these robust standard errors. Additional arguments like vcov_estimation or vcov_type are passed down to other methods, see standard_error_robust() for details and this vignette for working examples.

Value

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

Confidence intervals for random effect variances

When ci_method = "profile" and effects is either "random" or "all", profiled confidence intervals are computed for the random effects. For all other options of ci_method, confidence intervals for random effects will be missing.

Examples

Run this code

# NOT RUN {
library(parameters)
if (require("lme4")) {
  data(mtcars)
  model <- lmer(mpg ~ wt + (1 | gear), data = mtcars)
  model_parameters(model)
}
# }
# NOT RUN {
if (require("glmmTMB")) {
  data(Salamanders)
  model <- glmmTMB(
    count ~ spp + mined + (1 | site),
    ziformula = ~mined,
    family = poisson(),
    data = Salamanders
  )
  model_parameters(model, effects = "all")
}

if (require("lme4")) {
  model <- lmer(mpg ~ wt + (1 | gear), data = mtcars)
  model_parameters(model, bootstrap = TRUE, iterations = 50)
}
# }