eat (version 0.1.4)

RFEAT: 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)

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.

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.

Examples

# \donttest{
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)
# }