parameters (version 0.16.0)

p_value: p-values

Description

This function attempts to return, or compute, p-values of a model's parameters. See the documentation for your object's class:

• Bayesian models (rstanarm, brms, MCMCglmm, ...)

• Zero-inflated models (hurdle, zeroinfl, zerocount, ...)

• Marginal effects models (mfx)

• Models with special components (DirichletRegModel, clm2, cgam, ...)

Usage

p_value(model, ...)
# S3 method for default
p_value(
  model,
  dof = NULL,
  method = NULL,
  robust = FALSE,
  component = "all",
  verbose = TRUE,
  ...
)
# S3 method for emmGrid
p_value(model, ci = 0.95, adjust = "none", ...)

Arguments

model

A statistical model.

...

Arguments passed down to standard_error_robust() when confidence intervals or p-values based on robust standard errors should be computed. Only available for models where method = "robust" is supported.

dof

Number of degrees of freedom to be used when calculating confidence intervals. If NULL (default), the degrees of freedom are retrieved by calling degrees_of_freedom() with approximation method defined in method. If not NULL, use this argument to override the default degrees of freedom used to compute confidence intervals.

method

If "robust", and if model is supported by the sandwich or clubSandwich packages, computes p-values based on robust covariance matrix estimation.

robust

Logical, if TRUE, computes confidence intervals (or p-values) based on robust standard errors. See standard_error_robust().

component

Model component for which parameters should be shown. See the documentation for your object's class in model_parameters() for further details.

verbose

Toggle warnings and messages.

ci

Confidence Interval (CI) level. Default to 0.95 (95%).

adjust

Character value naming the method used to adjust p-values or confidence intervals. See ?emmeans::summary.emmGrid for details.

Value

A data frame with at least two columns: the parameter names and the p-values. Depending on the model, may also include columns for model components etc.

Examples

# NOT RUN {
data(iris)
model <- lm(Petal.Length ~ Sepal.Length + Species, data = iris)
p_value(model)
# }