get.mf.object.lm

A bootstrap model object created by <a rd-options="=bootstrap" href="/link/bootstrap()?package=mobForest&version=1.3.1&to=%3Dbootstrap" data-mini-rdoc="=bootstrap::bootstrap()">bootstrap()</a>

object

main_model

partition_vars

A data frame containing the variables in the model.

data

A data frame representing test data for validating
random forest model. This data is not used in in tree building process.

new_test_data

Number of trees to be constructed in forest (default = 300)

ntree

A description of error distribution and link function to be used
in the model. This parameter needs to be specified if generalized linear
model is considered. The parameter "binomial()" is to be specified
when logistic regression is considered and "poisson()" when Poisson
regression is considered as the node model. The values allowed for this
parameter are binomial() and poisson().

family

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

Functions to implements random forest method for model based
recursive partitioning. The mob() function, developed by Zeileis et al. (2008),
within 'party' package, is modified to construct model-based decision trees based
on random forests methodology. The main input function mobforest.analysis() takes
all input parameters to construct trees, compute out-of-bag errors, predictions,
and overall accuracy of forest. The algorithm performs parallel computation
using cluster functions within 'parallel' package.

Kasey Jones

mobForest

Model Based Random Forest Analysis

get.mf.object.lm function

A bootstrap model object created by <a rd-options='=bootstrap' href='bootstrap()'>bootstrap()</a>

get.mf.object.lm: Fit a linear model to a mobForest model

Description

Usage

Arguments

Value

See Also