Tidy summarizes information about the components of a model. A model component might be a single term in a regression, a single hypothesis, a cluster, or a class. Exactly what tidy considers to be a model component varies across models but is usually self-evident. If a model has several distinct types of components, you will need to specify which components to return.
# S3 method for garch
glance(x, test = c("box-ljung-test", "jarque-bera-test"), ...)A tibble::tibble() with exactly one row and columns:
Akaike's Information Criterion for the model.
Bayesian Information Criterion for the model.
The log-likelihood of the model. [stats::logLik()] may be a useful reference.
Which method was used.
Number of observations used.
P-value corresponding to the test statistic.
Test statistic.
Parameter field in the htest, typically degrees of freedom.
A garch object returned by tseries::garch().
Character specification of which hypothesis test to use. The
garch function reports 2 hypothesis tests: Jarque-Bera to residuals
and Box-Ljung to squared residuals.
Additional arguments. Not used. Needed to match generic
signature only. Cautionary note: Misspelled arguments will be
absorbed in ..., where they will be ignored. If the misspelled
argument has a default value, the default value will be used.
For example, if you pass conf.lvel = 0.9, all computation will
proceed using conf.level = 0.95. Two exceptions here are:
tidy() methods will warn when supplied an exponentiate argument if
it will be ignored.
augment() methods will warn when supplied a newdata argument if it
will be ignored.
glance(), tseries::garch(), []
Other garch tidiers:
tidy.garch()