parameters (version 0.10.1)

model_parameters.logitor: Parameters from (General) Linear Models

Description

Extract and compute indices and measures to describe parameters of (general) linear models (GLMs).

Usage

# S3 method for logitor
model_parameters(
  model,
  ci = 0.95,
  bootstrap = FALSE,
  iterations = 1000,
  standardize = NULL,
  exponentiate = TRUE,
  robust = FALSE,
  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,
  robust = 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,
  robust = FALSE,
  p_adjust = NULL,
  verbose = TRUE,
  ...
)
# S3 method for default
model_parameters(
  model,
  ci = 0.95,
  bootstrap = FALSE,
  iterations = 1000,
  standardize = NULL,
  exponentiate = FALSE,
  robust = FALSE,
  p_adjust = NULL,
  verbose = TRUE,
  ...
)
# S3 method for glm
model_parameters(
  model,
  ci = 0.95,
  df_method = "profile",
  bootstrap = FALSE,
  iterations = 1000,
  standardize = NULL,
  exponentiate = FALSE,
  robust = FALSE,
  p_adjust = 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.

standardize

The method used for standardizing the parameters. Can be "refit", "posthoc", "smart", "basic", "pseudo" or NULL (default) for no standardization. See 'Details' in standardize_parameters. Note that robust estimation (i.e. robust=TRUE) 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, say, logistic regressions, or more generally speaking: for models with log or logit link. Note: standard errors are also transformed (by multiplying the standard errors with the exponentiated coefficients), to mimic behaviour of other software packages, such as Stata.

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.

p_adjust

Character vector, if not NULL, indicates the method to adjust p-values. See p.adjust for details.

verbose

Toggle warnings and messages.

...

Arguments passed to or from other methods. For instance, when bootstrap = TRUE, arguments like ci_method are passed down to describe_posterior.

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".

df_method

Method for computing degrees of freedom for confidence intervals (CI). Only applies to models of class glm or polr. May be "profile" or "wald".

Value

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

See Also

standardize_names() to rename columns into a consistent, standardized naming scheme.

Examples

# NOT RUN {
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")

# different p-value 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)
# }