EAT_WAM: Weighted Additive Model for an Efficiency Analysis Trees...

In eat: Efficiency Analysis Trees

Weighted Additive Model for an Efficiency Analysis Trees model

Description

Weighted Additive Model for an Efficiency Analysis Trees model.

Usage

EAT_WAM(j, scores, x_k, y_k, atreeTk, ytreeTk, nX, nY, N_leaves, weights)

Arguments

j	Number of DMUs.
scores	matrix. Empty matrix for scores.
x_k	data.frame. Set of input variables.
y_k	data.frame Set of output variables.
atreeTk	matrix Set of "a" Pareto-coordinates.
ytreeTk	matrix Set of predictions.
nX	Number of inputs.
nY	Number of outputs.
N_leaves	Number of leaf nodes.
weights	Character. "MIP" for Measure of Inefficiency Proportion or "RAM" for Range Adjusted Measure of Inefficiency.

Value

A numerical vector with efficiency scores.

eat documentation built on Jan. 10, 2023, 5:13 p.m.