parameters (version 0.3.0)

ci_wald: Wald-test approximation for CIs and p-values

Description

The Wald-test approximation treats t-values as Wald z. Since the t distribution converges to the z distribution as degrees of freedom increase, this is like assuming infinite degrees of freedom. While this is unambiguously anti-conservative, this approximation appears as reasonable for reasonable sample sizes (Barr et al., 2013). That is, if we take the p-value to measure the probability of a false positive, this approximation produces a higher false positive rate than the nominal 5% at p = 0.05.

Usage

ci_wald(model, ci = 0.95, dof = NULL, component = c("all",
  "conditional", "zi", "zero_inflated"), robust = FALSE, ...)
p_value_wald(model, ...)
# S3 method for merMod
p_value_wald(model, dof = Inf, ...)

Arguments

model

A statistical model.

ci

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

dof

Degrees of Freedom. If not specified, for ci_wald(), defaults to model's residual degrees of freedom (i.e. n-k, where n is the number of observations and k is the number of parameters). For p_value_wald(), defaults to Inf.

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.

robust

Logical, if TRUE, robust standard errors are computed by calling standard_error_robust(). standard_error_robust(), in turn, calls one of the vcov*()-functions from the sandwich-package for robust covariance matrix estimators.

...

Arguments passed down to standard_error_robust() when confidence intervals or p-values based on robust standard errors should be computed.

Value

The p-values.

References

Barr, D. J. (2013). Random effects structure for testing interactions in linear mixed-effects models. Frontiers in psychology, 4, 328.

Examples

# NOT RUN {
library(lme4)
model <- lmer(Petal.Length ~ Sepal.Length + (1 | Species), data = iris)
p_value_wald(model)
ci_wald(model, ci = c(0.90, 0.95))
# }
# NOT RUN {
# }