The outcome class is wrapper around a vector of (mutable)
outcomes for ML tasks (supervised learning, causal inference).
When an additive tree ensemble is sampled, the outcome used to
sample a specific model term is the "partial residual" consisting
of the outcome minus the predictions of every other model term
(trees, group random effects, etc...).
Public fields
data_ptrExternal pointer to a C++ Outcome class
Methods
Method new()
Create a new Outcome object.
Arguments
outcomeVector of outcome values
Returns
A new Outcome object.
Method get_data()
Extract raw data in R from the underlying C++ object
Returns
R vector containing (copy of) the values in Outcome object
Method add_vector()
Update the current state of the outcome (i.e. partial residual) data by adding the values of update_vector
Usage
Outcome$add_vector(update_vector)
Arguments
update_vectorVector to be added to outcome
Method subtract_vector()
Update the current state of the outcome (i.e. partial residual) data by subtracting the values of update_vector
Usage
Outcome$subtract_vector(update_vector)
Arguments
update_vectorVector to be subtracted from outcome
Method update_data()
Update the current state of the outcome (i.e. partial residual) data by replacing each element with the elements of new_vector
Usage
Outcome$update_data(new_vector)
Arguments
new_vectorVector from which to overwrite the current data