MARSBoost {boostingDEA}

LS-Boosting with adapted Multivariate Adaptive Frontier Splines (MARS)

Description

This function estimates a production frontier satisfying some classical production theory axioms, such as monotonicity and concavity, which is based upon the adaptation of the machine learning technique known as LS-boosting using adapted Multivariate Adaptive Regression Splines (MARS) as base learners.

This function saves information about the LS-Boosted Multivariate Adaptive Frontier Splines model.

Usage

MARSBoost(data, x, y, num.iterations, num.terms, learning.rate)

MARSBoost_object(
  data,
  x,
  y,
  num.iterations,
  learning.rate,
  num.terms,
  MARS.models,
  f0,
  prediction,
  prediction.smooth
)

Arguments

data

data.frame or matrix containing the variables in the model.

x

Column input indexes in data.

y

Column output indexes in data.

num.iterations

Maximum number of iterations the algorithm will perform

num.terms

Maximum number of reflected pairs created by the forward algorithm of MARS.

learning.rate

Learning rate that control overfitting of the algorithm. Value must be in (0,1]

MARS.models

List of the adapted forward MARS models created in each iterations

f0

Initial predictions of the model (they correspond to maximum value of each output variable)

prediction

Final predictions of the original data without applying the smoothing procedure

prediction.smooth

Final predictions of the original data after applying the smoothing procedure

Value

A MARSBoost object.

A MARSBoost object.