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"),
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,
...
)
Vector representing a posterior distribution. Can also be a stanreg
or brmsfit
model.
Currently not used.
ROPE's lower and higher bounds. Should be a vector of length two (e.g., c(-0.1, 0.1)
) or "default"
. If "default"
, the range is set to c(-0.1, 0.1)
if input is a vector, and based on rope_range()
if a Bayesian model is provided.
Should results for fixed effects, random effects or both be returned? Only applies to mixed models. May be abbreviated.
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.
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.
# NOT RUN {
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))
# }
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