Outcome / partial residual used to sample an additive model.

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_ptr

External pointer to a C++ Outcome class

Methods

Public methods

• Outcome$new()

• Outcome$get_data()

• Outcome$add_vector()

• Outcome$subtract_vector()

• Outcome$update_data()

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Method new()

Create a new Outcome object.

Usage

Outcome$new(outcome)

Arguments

outcome

Vector of outcome values

Returns

A new Outcome object.

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Method get_data()

Extract raw data in R from the underlying C++ object

Usage

Outcome$get_data()

Returns

R vector containing (copy of) the values in Outcome object

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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_vector

Vector to be added to outcome

Returns

NULL

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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_vector

Vector to be subtracted from outcome

Returns

NULL

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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_vector

Vector from which to overwrite the current data

Returns

NULL