bayestestR (version 0.4.0)

p_significance: Practical Significance (ps)

Description

Compute the probability of Practical Significance (ps), which can be conceptualized as a unidirectional equivalence test. It returns the probability that effect is above a given threshold corresponding to a negligible effect in the median's direction. Mathematically, it is defined as the proportion of the posterior distribution of the median sign above the threshold.

Usage

p_significance(x, ...)
# S3 method for numeric
p_significance(x, threshold = "default", ...)
# S3 method for data.frame
p_significance(x, threshold = "default", ...)
# S3 method for MCMCglmm
p_significance(x, threshold = "default", ...)
# S3 method for emmGrid
p_significance(x, threshold = "default", ...)
# S3 method for stanreg
p_significance(x, threshold = "default",
  effects = c("fixed", "random", "all"), parameters = NULL,
  verbose = TRUE, ...)

Arguments

x

Vector representing a posterior distribution. Can also be a stanreg or brmsfit model.

...

Currently not used.

threshold

The threshold value that separates significant from negligible effect. If "default", the range is set to 0.1 if input is a vector, and based on rope_range() if a Bayesian model is provided.

effects

Should results for fixed effects, random effects or both be returned? Only applies to mixed models. May be abbreviated.

parameters

Regular expression pattern that describes the parameters that should be returned. Meta-parameters (like lp__ or prior_) are filtered by default, so only parameters that typically appear in the summary() are returned. Use parameters to select specific parameters for the output.

verbose

Toggle off warnings.

Value

Values between 0.5 and 1 corresponding to the probability of practical significance (ps).

Examples

# NOT RUN {
library(bayestestR)

# Simulate a posterior distribution of mean 1 and SD 1
# ----------------------------------------------------
posterior <- rnorm(1000, mean = 1, sd = 1)
p_significance(posterior)

# Simulate a dataframe of posterior distributions
# -----------------------------------------------
df <- data.frame(replicate(4, rnorm(100)))
p_significance(df)

# rstanarm models
# -----------------------------------------------
library(rstanarm)
model <- rstanarm::stan_glm(mpg ~ wt + cyl,
  data = mtcars,
  chains = 2, refresh = 0
)
p_significance(model)
# }