multimedia (version 0.2.0)

model-class: Representation of an Outcome or Mediation Model

Description

To work with many model types simultaneously, multimedia uses a model class with the necessary mediation model functionality that wraps any specific implementation. The slots below define the generally required functionality for any specific implementation.

Slots

estimator

A function that takes a formula, input data frame X, and an response data.frame $Y$ and returns a model. For example, for the random forest model, this is created by wrapping parallelize() on the ranger() function for random forest estimation function using the 'ranger' package.

estimates

A list containing the estimated model.

sampler

A function that supports sampling new responses from the estimated model.

model_type

A string specifying the type of model associated with the class. For example, "rf_model()" denotes a random forest model.

predictor

A function that returns fitted predictions given new inputs. For example, this can be the original predict() method for a multivariate response model, or it can be a loop over predicts for each feature in the mediation or outcome model.

Examples

m <- lm_model()
estimator(m)(mpg ~ hp + wt, data = mtcars)

m <- rf_model()
estimator(m)(mpg ~ hp + wt, data = mtcars)