parameters (version 0.10.1)

p_value.lmerMod: p-values for Mixed Models

Description

This function attempts to return, or compute, p-values of mixed models.

Usage

# S3 method for lmerMod
p_value(model, method = "wald", ...)
# S3 method for merMod
p_value(model, method = "wald", ...)
# S3 method for glmmTMB
p_value(
  model,
  component = c("all", "conditional", "zi", "zero_inflated", "dispersion"),
  verbose = TRUE,
  ...
)
# S3 method for MixMod
p_value(
  model,
  component = c("all", "conditional", "zi", "zero_inflated"),
  verbose = TRUE,
  ...
)
# S3 method for mixor
p_value(model, effects = c("all", "fixed", "random"), ...)

Arguments

model

A statistical model.

method

For mixed models, can be "wald" (default), "ml1", "betwithin", "satterthwaite" or "kenward". For models that are supported by the sandwich or clubSandwich packages, may also be method = "robust" to compute p-values based ob robust standard errors.

...

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.

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.

verbose

Toggle warnings and messages.

effects

Should standard errors for fixed effects or random effects be returned? Only applies to mixed models. May be abbreviated. When standard errors for random effects are requested, for each grouping factor a list of standard errors (per group level) for random intercepts and slopes is returned.

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.

Details

By default, p-values are based on Wald-test approximations (see p_value_wald). For certain situations, the "m-l-1" rule might be a better approximation. That is, for method = "ml1", p_value_ml1 is called. For lmerMod objects, if method = "kenward", p-values are based on Kenward-Roger approximations, i.e. p_value_kenward is called, and method = "satterthwaite" calls p_value_satterthwaite.

Examples

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