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Practical Significance (ps)

Source: R/p_significance.R
p_significance.Rd

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 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 'brmsfit'
p_significance(
  x,
  threshold = "default",
  effects = "fixed",
  component = "conditional",
  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, which can have following possible values:

  • "default", in which case the range is set to 0.1 if input is a vector, and based on rope_range() if a (Bayesian) model is provided.

  • a single numeric value (e.g., 0.1), which is used as range around zero (i.e. the threshold range is set to -0.1 and 0.1, i.e. reflects a symmetric interval)

  • a numeric vector of length two (e.g., c(-0.2, 0.1)), useful for asymmetric intervals

  • a list of numeric vectors, where each vector corresponds to a parameter

  • a list of named numeric vectors, where names correspond to parameter names. In this case, all parameters that have no matching name in threshold will be set to "default".

use_iterations

Logical, if TRUE and x is a get_predicted object, (returned by insight::get_predicted()), the function is applied to the iterations instead of the predictions. This only applies to models that return iterations for predicted values (e.g., brmsfit models).

verbose

Toggle off warnings.

rvar_col

A single character - the name of an rvar column in the data frame to be processed. See example in p_direction().

effects

Should variables for fixed effects ("fixed"), random effects ("random") or both ("all") be returned? Only applies to mixed models. May be abbreviated.

For models of from packages brms or rstanarm there are additional options:

  • "fixed" returns fixed effects.

  • "random_variance" return random effects parameters (variance and correlation components, e.g. those parameters that start with sd_ or cor_).

  • "grouplevel" returns random effects group level estimates, i.e. those parameters that start with r_.

  • "random" returns both "random_variance" and "grouplevel".

  • "all" returns fixed effects and random effects variances.

  • "full" returns all parameters.

component

Which type of parameters to return, such as parameters for the conditional model, the zero-inflated part of the model, the dispersion term, etc. See details in section Model Components. May be abbreviated. Note that the conditional component also refers to the count or mean component - names may differ, depending on the modeling package. There are three convenient shortcuts (not applicable to all model classes):

  • component = "all" returns all possible parameters.

  • If component = "location", location parameters such as conditional, zero_inflated, smooth_terms, or instruments are returned (everything that are fixed or random effects - depending on the effects argument - but no auxiliary parameters).

  • For component = "distributional" (or "auxiliary"), components like sigma, dispersion, beta or precision (and other auxiliary parameters) are returned.

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.

Value

Values between 0 and 1 corresponding to the probability of practical significance (ps).

Details

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.

Note

There is also a plot()-method implemented in the see-package.

Model components

Possible values for the component argument depend on the model class. Following are valid options:

  • "all": returns all model components, applies to all models, but will only have an effect for models with more than just the conditional model component.

  • "conditional": only returns the conditional component, i.e. "fixed effects" terms from the model. Will only have an effect for models with more than just the conditional model component.

  • "smooth_terms": returns smooth terms, only applies to GAMs (or similar models that may contain smooth terms).

  • "zero_inflated" (or "zi"): returns the zero-inflation component.

  • "location": returns location parameters such as conditional, zero_inflated, or smooth_terms (everything that are fixed or random effects - depending on the effects argument - but no auxiliary parameters).

  • "distributional" (or "auxiliary"): components like sigma, dispersion, beta or precision (and other auxiliary parameters) are returned.

For models of class brmsfit (package brms), even more options are possible for the component argument, which are not all documented in detail here. See also ?insight::find_parameters.

Examples

library(bayestestR)

# Simulate a posterior distribution of mean 1 and SD 1
# ----------------------------------------------------
posterior <- rnorm(1000, mean = 1, sd = 1)
p_significance(posterior)
#> Practical Significance (threshold: 0.10)
#> 
#> Parameter |   ps
#> ----------------
#> Posterior | 0.80

# Simulate a dataframe of posterior distributions
# -----------------------------------------------
df <- data.frame(replicate(4, rnorm(100)))
p_significance(df)
#> Practical Significance (threshold: 0.10)
#> 
#> Parameter |   ps
#> ----------------
#> X1        | 0.51
#> X2        | 0.55
#> X3        | 0.47
#> X4        | 0.47
# \donttest{
# rstanarm models
# -----------------------------------------------
model <- rstanarm::stan_glm(mpg ~ wt + cyl,
  data = mtcars,
  chains = 2, refresh = 0
)
p_significance(model)
#> Practical Significance (threshold: 0.60) 
#> 
#> Parameter   |   ps
#> ------------------
#> (Intercept) | 1.00
#> wt          | 1.00
#> cyl         | 0.98
# multiple thresholds - asymmetric, symmetric, default
p_significance(model, threshold = list(c(-10, 5), 0.2, "default"))
#> Practical Significance 
#> 
#> Parameter   |   ps |           ROPE
#> -----------------------------------
#> (Intercept) | 1.00 | [-10.00, 5.00]
#> wt          | 1.00 | [ -0.20, 0.20]
#> cyl         | 0.98 | [ -0.60, 0.60]
# named thresholds
p_significance(model, threshold = list(wt = 0.2, `(Intercept)` = c(-10, 5)))
#> Practical Significance 
#> 
#> Parameter   |   ps |           ROPE
#> -----------------------------------
#> (Intercept) | 1.00 | [-10.00, 5.00]
#> wt          | 1.00 | [ -0.20, 0.20]
#> cyl         | 0.98 | [ -0.60, 0.60]
# }