Helper functions to convert between formula and matrix interface

Functions to take a formula interface and get the resulting objects (y, x, weights, etc) back or the other way around. The functions are intended for developer use. For the most part, this emulates the internals of lm() (and also see the notes at https://developer.r-project.org/model-fitting-functions.html).

.convert_form_to_x_fit() and .convert_x_to_form_fit() are for when the data are created for modeling. .convert_form_to_x_fit() saves both the data objects as well as the objects needed when new data are predicted (e.g. terms, etc.).

.convert_form_to_x_new() and .convert_x_to_form_new() are used when new samples are being predicted and only require the predictors to be available.

Usage

.convert_form_to_x_fit(
  formula,
  data,
  ...,
  na.action = na.omit,
  indicators = "traditional",
  composition = "data.frame",
  remove_intercept = TRUE
)

.convert_x_to_form_fit(x, weights = NULL, remove_intercept = TRUE)

.convert_form_to_x_new(
  object,
  new_data,
  na.action = stats::na.pass,
  composition = "data.frame"
)

.convert_x_to_form_new(object, new_data)

Arguments

formula: An object of class formula (or one that can be coerced to that class): a symbolic description of the model to be fitted.
data: A data frame containing all relevant variables (e.g. predictors, case weights, etc).
...: Additional arguments passed to stats::model.frame().
na.action: A function which indicates what should happen when the data contain NAs.
indicators: A string describing whether and how to create indicator/dummy variables from factor predictors. Possible options are "none", "traditional", and "one_hot".
composition: A string describing whether the resulting x and y should be returned as a "matrix" or a "data.frame".
remove_intercept: A logical indicating whether to remove the intercept column after model.matrix() is finished.
x: A matrix, sparse matrix, or data frame of predictors. Only some models have support for sparse matrix input. See modelenv::get_encoding() for details. x should have column names.
weights: A numeric vector containing the weights.
object: An object of class cluster_fit.
new_data: A rectangular data object, such as a data frame.