Broom tidies a number of lists that are effectively S3
objects without a class attribute. For example, stats::optim(),
svd() and akima::interp() produce consistent output, but
because they do not have a class attribute, they cannot be handled by S3
dispatch.
These functions look at the elements of a list and determine if there is
an appropriate tidying method to apply to the list. Those tidiers are
implemented as functions of the form tidy_<function> or
glance_<function> and are not exported (but they are documented!).
If no appropriate tidying method is found, they throw an error.
tidy_optim(x, ...)A list returned from stats::optim().
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. Additionally, if you pass
newdata = my_tibble to an augment() method that does not
accept a newdata argument, it will use the default value for
the data argument.
This function assumes that the provided objective function is a negative log-likelihood function. Results will be invalid if an incorrect function is supplied.
tidy(o) glance(o)
Other list tidiers:
glance_optim(),
list_tidiers,
tidy_irlba(),
tidy_svd(),
tidy_xyz()
A tibble::tibble() with columns:
The parameter being modeled.
The standard error of the regression term.
The value/estimate of the component. Results from data reshaping.
std.error is only provided as a column if the Hessian is calculated.