MARSBoost {boostingDEA}  R Documentation 
LSBoosting 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 LSboosting using adapted Multivariate Adaptive Regression Splines (MARS) as base learners.
This function saves information about the LSBoosted 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 

x 
Column input indexes in 
y 
Column output indexes in 
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.