View source: R/model_dashboard.R
model_dashboard | R Documentation |
easystats
A dashboard containing the following details for the entered regression model:
tabular summary of parameter estimates
dot-and-whisker plot for parameter estimates
tabular summary of indices for the quality of model fit
collection of models for checking model assumptions
text report
model information table
model_dashboard(
model,
check_model_args = NULL,
parameters_args = NULL,
performance_args = NULL,
output_file = "easydashboard.html",
output_dir = getwd(),
rmd_dir = system.file("templates/easydashboard.Rmd", package = "easystats"),
quiet = FALSE,
browse_html = interactive()
)
model |
A regression model object. |
check_model_args |
A list of named arguments that are passed down to
|
parameters_args |
A list of named arguments that are passed down to
|
performance_args |
A list of named arguments that are passed down to
|
output_file |
A string specifying the file name in |
output_dir |
A string specifying the path to the output directory for
report in |
rmd_dir |
A string specifying the path to the directory containing the
RMarkdown template file. By default, package uses the template shipped with
the package installation ( |
quiet |
An option to suppress printing during rendering from knitr,
pandoc command line and others. To only suppress printing of the last
"Output created: " message, you can set |
browse_html |
A logical deciding if the rendered HTML should be opened
in the browser. Defaults to |
An HTML dashboard.
For models with many observations, or for more complex models in general,
generating the model assumptions plot might become very slow. One reason
is that the underlying graphic engine becomes slow for plotting many data
points. In such cases, setting the argument check_model_args = list(show_dots = FALSE)
might help. Furthermore, look at other arguments of ?performance::check_model
,
which can be set using check_model_args
, to increase performance (in
particular the check
-argument can help, to skip some unnecessary checks).
# define a regression model
mod <- lm(wt ~ mpg, mtcars)
# with default options
model_dashboard(mod)
# customizing 'parameters' output: standardize coefficients
model_dashboard(mod, parameters_args = list(standardize = "refit"))
# customizing 'performance' output: only show selected performance metrics
model_dashboard(mod, performance_args = list(metrics = c("AIC", "RMSE")))
# customizing output of model assumptions plot: don't show dots (faster plot)
model_dashboard(mod, check_model_args = list(show_dots = FALSE))
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