parameters (version 0.3.0)

p_value: p-values

Description

This function attempts to return, or compute, p-values of a model's parameters. The nature of the p-values is different depending on the model:

• Mixed models (lme4): By default, p-values are based on Wald-test approximations (see p_value_wald). For lmerMod objects, if method = "kenward", p-values are based on Kenward-Roger approximations, i.e. p_value_kenward is called.

Usage

p_value(model, ...)
# S3 method for lmerMod
p_value(model, method = "wald", ...)
# S3 method for glmmTMB
p_value(model, component = c("all", "conditional",
  "zi", "zero_inflated"), ...)
# S3 method for MixMod
p_value(model, component = c("all", "conditional", "zi",
  "zero_inflated"), ...)
p_value_robust(model, vcov_estimation = "HC", vcov_type = c("HC3",
  "const", "HC", "HC0", "HC1", "HC2", "HC4", "HC4m", "HC5"),
  vcov_args = NULL, ...)

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.

method

For mixed models, can be "wald" (default) or "kenward".

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" or "all" (default). May be abbreviated.

vcov_estimation

String, indicating the suffix of the vcov*()-function from the sandwich-package, e.g. vcov_estimation = "CL" (which calls vcovCL to compute clustered covariance matrix estimators), or vcov_estimation = "HC" (which calls vcovHC to compute heteroskedasticity-consistent covariance matrix estimators).

vcov_type

Character vector, specifying the estimation type for the robust covariance matrix estimation (see vcovHC for details).

vcov_args

List of named vectors, used as additional arguments that are passed down to the sandwich-function specified in vcov_estimation.

Value

The p-values.

Examples

# NOT RUN {
model <- lme4::lmer(Petal.Length ~ Sepal.Length + (1 | Species), data = iris)
p_value(model)
# }