RFEAT: Random Forest + Efficiency Analysis Trees

In eat: Efficiency Analysis Trees

Random Forest + Efficiency Analysis Trees

Description

This function builds m individual Efficiency Analysis Trees in a forest structure.

Usage

RFEAT(data, x, y, numStop = 5, m = 50, s_mtry = "BRM", na.rm = TRUE)

Arguments

data	data.frame or matrix containing the variables in the model.
x	Column input indexes in data.
y	Column output indexes in data.
numStop	Minimum number of observations in a node for a split to be attempted.
m	Number of trees to be built.
s_mtry	Number of variables randomly sampled as candidates at each split. The available options are: "BRM": in / 3 "DEA1": (t.obs / 2) - out "DEA2": (t.obs / 3) - out "DEA3": t.obs - 2 * out "DEA4": min(t.obs / out, (t.obs / 3) - out) Any integer
na.rm	logical. If TRUE, NA rows are omitted.

Value

A RFEAT object containing:

• data

  • df: data frame containing the variables in the model.

  • x: input indexes in data.

  • y: output indexes in data.

  • input_names: input variable names.

  • output_names: output variable names.

  • row_names: rownames in data.

• control

  • numStop: numStop hyperparameter value.

  • m: m hyperparameter value.

  • s_mtry: s_mtry hyperparameter value.

  • na.rm: na.rm hyperparameter value.

• forest: list containing the individual EAT models.

• error: Out-of-Bag error at the forest.

• OOB: list containing Out-of-Bag set for each tree.

Examples

simulated <- X2Y2.sim(N = 50, border = 0.1)

RFmodel <- RFEAT(data = simulated, x = c(1,2), y = c(3, 4), numStop = 5,
                  m = 50, s_mtry = "BRM", na.rm = TRUE)

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