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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 'brmsfit'
p_rope(
  x,
  range = "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.

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 another list (one for each response variable) of numeric vectors . 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. See 'Examples'.

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

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.

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)

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