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 numeric
p_rope(x, range = "default", verbose = TRUE, ...)
# S3 method for data.frame
p_rope(x, range = "default", rvar_col = NULL, 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 = TRUE,
...
)
# S3 method for brmsfit
p_rope(
x,
range = "default",
effects = c("fixed", "random", "all"),
component = c("conditional", "zi", "zero_inflated", "all"),
parameters = NULL,
verbose = TRUE,
...
)
Vector representing a posterior distribution. Can also be a
stanreg
or brmsfit
model.
Currently not used.
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.
Toggle off warnings.
A single character - the name of an rvar
column in the data
frame to be processed. See example in p_direction()
.
Should results for fixed effects, random effects or both be returned? Only applies to mixed models. May be abbreviated.
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.
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.
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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