View source: R/p_significance.R
p_significance  R Documentation 
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.
p_significance(x, ...)
## S3 method for class 'numeric'
p_significance(x, threshold = "default", ...)
## S3 method for class 'get_predicted'
p_significance(
x,
threshold = "default",
use_iterations = FALSE,
verbose = TRUE,
...
)
## S3 method for class 'data.frame'
p_significance(x, threshold = "default", rvar_col = NULL, ...)
## S3 method for class 'stanreg'
p_significance(
x,
threshold = "default",
effects = c("fixed", "random", "all"),
component = c("location", "all", "conditional", "smooth_terms", "sigma",
"distributional", "auxiliary"),
parameters = NULL,
verbose = TRUE,
...
)
## S3 method for class 'brmsfit'
p_significance(
x,
threshold = "default",
effects = c("fixed", "random", "all"),
component = c("conditional", "zi", "zero_inflated", "all"),
parameters = NULL,
verbose = TRUE,
...
)
x 
Vector representing a posterior distribution. Can also be a

... 
Currently not used. 
threshold 
The threshold value that separates significant from negligible effect, which can have following possible values:

use_iterations 
Logical, if 
verbose 
Toggle off warnings. 
rvar_col 
A single character  the name of an 
effects 
Should results for fixed effects, random effects or both be returned? Only applies to mixed models. May be abbreviated. 
component 
Should results for all parameters, parameters for the conditional model or the zeroinflated part of the model be returned? May be abbreviated. Only applies to brmsmodels. 
parameters 
Regular expression pattern that describes the parameters
that should be returned. Metaparameters (like 
p_significance()
returns the proportion of a probability
distribution (x
) that is outside a certain range (the negligible
effect, or ROPE, see argument threshold
). If there are values of the
distribution both below and above the ROPE, p_significance()
returns
the higher probability of a value being outside the ROPE. Typically, this
value should be larger than 0.5 to indicate practical significance. However,
if the range of the negligible effect is rather large compared to the
range of the probability distribution x
, p_significance()
will be less than 0.5, which indicates no clear practical significance.
Values between 0 and 1 corresponding to the probability of practical significance (ps).
There is also a plot()
method implemented in the seepackage.
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
# 
model < rstanarm::stan_glm(mpg ~ wt + cyl,
data = mtcars,
chains = 2, refresh = 0
)
p_significance(model)
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