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Probability of being in the ROPE

Source: R/p_rope.R
p_rope.Rd

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).

Usage

p_rope(x, ...)

# S3 method for class 'numeric'
p_rope(x, range = "default", verbose = TRUE, ...)

# S3 method for class 'data.frame'
p_rope(x, range = "default", rvar_col = NULL, verbose = TRUE, ...)

# S3 method for class 'stanreg'
p_rope(
  x,
  range = "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_rope(
  x,
  range = "default",
  effects = c("fixed", "random", "all"),
  component = c("conditional", "zi", "zero_inflated", "all"),
  parameters = NULL,
  verbose = TRUE,
  ...
)

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. For models with one response, range can be:

  • a vector of length two (e.g., c(-0.1, 0.1)),

  • a list of numeric vector of the same length as numbers of parameters (see 'Examples').

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

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.

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 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