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 numeric 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, ... ) # S3 method for 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. If |

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 |

verbose | Toggle off warnings. |

component | Should results for all parameters, parameters for the conditional model or the zero-inflated part of the model be returned? May be abbreviated. Only applies to brms-models. |

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

`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.

There is also a `plot()`

-method implemented in the see-package.

library(bayestestR) # Simulate a posterior distribution of mean 1 and SD 1 # ---------------------------------------------------- posterior <- rnorm(1000, mean = 1, sd = 1) p_significance(posterior)#> ps [0.10] = 80.10%# Simulate a dataframe of posterior distributions # ----------------------------------------------- df <- data.frame(replicate(4, rnorm(100))) p_significance(df)#> # Probability of Significance (ps [0.10]) #> #> Parameter | ps #> ------------------ #> X1 | 51.00% #> X2 | 55.00% #> X3 | 47.00% #> X4 | 47.00% #>if (FALSE) { # rstanarm models # ----------------------------------------------- if (require("rstanarm")) { model <- rstanarm::stan_glm(mpg ~ wt + cyl, data = mtcars, chains = 2, refresh = 0 ) p_significance(model) } }