set_fit {modelenv}

Register Fit method for Model

Description

This function is used to register a fit method for a model, engine, and mode combination.

Usage

set_fit(model, mode, eng, value)

get_fit(model)

Arguments

model

A single character string for the model type (e.g. "k_means", etc).

mode

A single character string for the model mode (e.g. "partition").

eng

A single character string for the model engine.

value

A list of values, described in the Details.

Details

The list passed to value needs the following values:

• interface is a single character value that could be ⁠“formula”⁠, ⁠“data.frame”⁠, or ⁠“matrix”⁠. This defines the type of interface used by the underlying fit function (stats::lm, in this case). This helps the translation of the data to be in an appropriate format for the that function.

• protect is an optional list of function arguments that should not be changeable by the user. In this case, we probably don’t want users to pass data values to these arguments (until the fit() function is called).

• func is the package and name of the function that will be called. If you are using a locally defined function, only fun is required.

• defaults is an optional list of arguments to the fit function that the user can change, but whose defaults can be set here. This isn’t needed in this case, but is described later in this document.

Value

A tibble

Examples

set_new_model("shallow_learning_model")
set_model_mode("shallow_learning_model", "partition")
set_model_engine("shallow_learning_model", "partition", "stats")

set_fit(
  model = "shallow_learning_model",
  mode = "partition",
  eng = "stats",
  value = list(
    interface = "formula",
    protect = c("formula", "data"),
    func = c(pkg = "stats", fun = "lm"),
    defaults = list()
  )
)

get_fit("shallow_learning_model")
get_fit("shallow_learning_model")$value