insight (version 0.11.0)

print_parameters: Prepare summary statistics of model parameters for printing

Description

This function takes a data frame, typically a data frame with information on summaries of model parameters like hdi or equivalence_test, as input and splits this information into several parts, depending on the model. See details below.

Usage

print_parameters(
  x,
  ...,
  split_by = c("Effects", "Component", "Group", "Response"),
  format = "text",
  keep_parameter_column = TRUE
)

Arguments

x

A fitted model, or a data frame returned by clean_parameters.

...

One or more objects (data frames), which contain information about the model parameters and related statistics (like confidence intervals, HDI, ROPE, ...).

split_by

split_by should be a character vector with one or more of the following elements: "Effects", "Component", "Response" and "Group". These are the column names returned by clean_parameters, which is used to extract the information from which the group or component model parameters belong. If NULL, the merged data frame is returned. Else, the data frame is split into a list, split by the values from those columns defined in split_by.

format

Name of output-format, as string. If NULL (or "text"), assumed use for output is basic printing. If "markdown", markdown-format is assumed. This only affects the style of title- and table-caption attributes, which are used in export_table.

keep_parameter_column

Logical, if TRUE, the data frames in the returned list have both a "Cleaned_Parameter" and "Parameter" column. If FALSE, the (unformatted) "Parameter" is removed, and the column with cleaned parameter names ("Cleaned_Parameter") is renamed into "Parameter".

Value

A data frame or a list of data frames (if split_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.

Details

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 hdi, where we have a) a Parameters column and b) columns with the HDI values.

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 statistic of interest (in our example: the HDI) is 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'.

Examples

# NOT RUN {
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 informaton on
# parameter names and HDI values
tmp <- as.data.frame(x)[, 1:4]
tmp

# Based on the "split_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
# }
# NOT RUN {
# }