View source: R/print_parameters.R
print_parameters | R Documentation |
This function takes a data frame, typically a data frame with information on
summaries of model parameters like bayestestR::describe_posterior()
,
bayestestR::hdi()
or parameters::model_parameters()
, as input and splits
this information into several parts, depending on the model. See details
below.
print_parameters(
x,
...,
by = c("Effects", "Component", "Group", "Response"),
format = "text",
parameter_column = "Parameter",
keep_parameter_column = TRUE,
remove_empty_column = FALSE,
titles = NULL,
subtitles = NULL
)
x |
A fitted model, or a data frame returned by |
... |
One or more objects (data frames), which contain information about the model parameters and related statistics (like confidence intervals, HDI, ROPE, ...). |
by |
|
format |
Name of output-format, as string. If |
parameter_column |
String, name of the column that contains the
parameter names. Usually, for data frames returned by functions the
easystats-packages, this will be |
keep_parameter_column |
Logical, if |
remove_empty_column |
Logical, if |
titles , subtitles |
By default, the names of the model components (like
fixed or random effects, count or zero-inflated model part) are added as
attributes |
This function prepares data frames that contain information about model parameters for clear printing.
First, x
is required, which should either be a model object or a
prepared data frame as returned by clean_parameters()
. If
x
is a model, clean_parameters()
is called on that model
object to get information with which model components the parameters
are associated.
Then, ...
take one or more data frames that also contain information
about parameters from the same model, but also have additional information
provided by other methods. For instance, a data frame in ...
might
be the result of, for instance, bayestestR::describe_posterior()
,
or parameters::model_parameters()
, where we have a) a
Parameter
column and b) columns with other parameter values (like
CI, HDI, test statistic, etc.).
Now we have a data frame with model parameters and information about the
association to the different model components, a data frame with model
parameters, and some summary statistics. print_parameters()
then merges
these data frames, so the parameters or statistics of interest are also
associated with the different model components. The data frame is split into
a list, so for a clear printing. Users can loop over this list and print each
component for a better overview. Further, parameter names are "cleaned", if
necessary, also for a cleaner print. See also 'Examples'.
A data frame or a list of data frames (if by
is not NULL
). If a
list is returned, the element names reflect the model components where the
extracted information in the data frames belong to, e.g.
random.zero_inflated.Intercept: persons
. This is the data frame that
contains the parameters for the random effects from group-level "persons"
from the zero-inflated model component.
library(bayestestR)
model <- download_model("brms_zi_2")
x <- hdi(model, effects = "all", component = "all")
# hdi() returns a data frame; here we use only the
# information on parameter names and HDI values
tmp <- as.data.frame(x)[, 1:4]
tmp
# Based on the "by" argument, we get a list of data frames that
# is split into several parts that reflect the model components.
print_parameters(model, tmp)
# This is the standard print()-method for "bayestestR::hdi"-objects.
# For printing methods, it is easy to print complex summary statistics
# in a clean way to the console by splitting the information into
# different model components.
x
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