Compute the proportion of the whole posterior distribution that doesn't lie within a region of practical equivalence (ROPE). It is equivalent to running rope(..., ci = 1).

p_rope(x, ...)

# S3 method for default
p_rope(x, ...)

# S3 method for numeric
p_rope(x, range = "default", ...)

# S3 method for data.frame
p_rope(x, range = "default", ...)

# S3 method for emmGrid
p_rope(x, range = "default", ...)

# S3 method for BFBayesFactor
p_rope(x, range = "default", ...)

# S3 method for MCMCglmm
p_rope(x, range = "default", ...)

# S3 method for stanreg
p_rope(
  x,
  range = "default",
  effects = c("fixed", "random", "all"),
  component = c("location", "all", "conditional", "smooth_terms", "sigma",
    "distributional", "auxiliary"),
  parameters = NULL,
  ...
)

# S3 method for brmsfit
p_rope(
  x,
  range = "default",
  effects = c("fixed", "random", "all"),
  component = c("conditional", "zi", "zero_inflated", "all"),
  parameters = NULL,
  ...
)

Arguments

x

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

...

Currently not used.

range

ROPE's lower and higher bounds. Should be "default" or depending on the number of outcome variables a vector or a list. In models with one response, `range` should be a vector of length two (e.g., c(-0.1, 0.1)). In multivariate models, `range` should be a list with a numeric vectors for each response variable. Vector names should correspond to the name of the response variables. If "default" and input is a vector, the range is set to c(-0.1, 0.1). If "default" and input is a Bayesian model, rope_range() is used.

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 zero-inflated part of the model be returned? May be abbreviated. Only applies to brms-models.

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.

Examples

library(bayestestR) p_rope(x = rnorm(1000, 0, 0.01), range = c(-0.1, 0.1))
#> Proportion of samples inside the ROPE [-0.10, 0.10]: > .999
p_rope(x = mtcars, range = c(-0.1, 0.1))
#> Proportion of samples inside the ROPE [-0.10, 0.10] #> #> Parameter | p (ROPE) #> -------------------- #> mpg | < .001 #> cyl | < .001 #> disp | < .001 #> hp | < .001 #> drat | < .001 #> wt | < .001 #> qsec | < .001 #> vs | 0.562 #> am | 0.594 #> gear | < .001 #> carb | < .001