p_rope {bayestestR} R Documentation

## Probability of being in the ROPE

### Description

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", ...)

## 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,
...
)

## S3 method for class '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))
p_rope(x = mtcars, range = c(-0.1, 0.1))


[Package bayestestR version 0.13.0 Index]