boostingDEA (version 0.1.0)

MARSAdapted: Adapted Multivariate Adaptive Frontier Splines

Description

Create an adapted version of Multivariate Adaptive Regression Splines (MARS) model to estimate a production frontier satisfying some classical production theory axioms, such as monotonicity and concavity.

Usage

MARSAdapted(
  data,
  x,
  y,
  nterms,
  Kp = 1,
  d = 2,
  err_red = 0.01,
  minspan = 0,
  endspan = 0,
  linpreds = FALSE,
  na.rm = TRUE
)

Value

An AdaptedMARS 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.

nterms

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

Kp

Maximum degree of interaction allowed. Default is 1.

d

Generalized Cross Validation (GCV) penalty per knot. Default is 2. If it is set to -1, GCV = RSS / n.

err_red

Minimum reduced error rate for the addition of two new basis functions. Default is 0.01.

minspan

Minimum number of observations between knots. When minspan = 0 (default), it is calculated as in Friedman's MARS paper section 3.8 with alpha = 0.05.

endspan

Minimum number of observations before the first and after the final knot. When endspan = 0 (default), it is calculated as in Friedman's MARS paper section 3.8 with alpha = 0.05.

linpreds

logical. If TRUE, predictors can enter linearly

na.rm

logical. If TRUE, NA rows are omitted.