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

## 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.- verbose
Toggle off warnings.

- 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
```