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 themselves 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, throws an error.

tidy_optim(x, ...)

Arguments

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.

Note

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)

tidy(), stats::optim()

Other list tidiers: glance_optim(), list_tidiers, tidy_irlba(), tidy_svd(), tidy_xyz()

Value

A tibble::tibble() with columns:

parameter

The parameter being modeled.

std.error

The standard error of the regression term.

value

The value/estimate of the component. Results from data reshaping.

std.error is only provided as a column if the Hessian is calculated.

Examples


f <- function(x) (x[1] - 2)^2 + (x[2] - 3)^2 + (x[3] - 8)^2
o <- optim(c(1, 1, 1), f)