MARSBoost: 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
)

Value

A MARSBoost object.

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