tree.predictions: Predictions from tree model

In RTIInternational/mobForest: Model Based Random Forest Analysis

Description

This method computes predicted outcome for each observation in the data frame using the tree model supplied as an input argument.

Usage

tree.predictions(j, df, tree)

Arguments

j	the observation
df	A data frame containing the variables in the model.
tree	An object of class mob inheriting from BinaryTree

Value

A vector of predicted outcome

Examples

library(mlbench)
set.seed(1111)
# Random Forest analysis of model based recursive partitioning load data
data("BostonHousing", package = "mlbench")
data <- BostonHousing[1:90, c("rad", "tax", "crim", "medv", "lstat")]
fmBH <- mob.rf.tree(main_model = "medv ~ lstat", 
                     partition_vars = c("rad", "tax", "crim"), mtry = 2,
                     control = mob_control(), data = data, 
                     model = linearModel)
tree.predictions(j = 1, df = data, tree = fmBH@tree)

RTIInternational/mobForest documentation built on Aug. 3, 2019, 8:28 a.m.